Merge pull request #51 from velocitatem/feat-strong-learning-implementation-with-data-contamination

Feat strong learning implementation with data contamination

.github/workflows/latex.yml

@@ -19,10 +19,56 @@ jobs:
         with:
           root_file: main.tex
           working_directory: paper/src
-          args: -pdf -interaction=nonstopmode -file-line-error -outdir=../build
+          args: -pdf -f -interaction=nonstopmode -file-line-error -outdir=../build
           pre_compile: bash ../concat_code.sh
       - name: Upload PDF
         uses: actions/upload-artifact@v4
         with:
           name: thesis-pdf
           path: paper/build/main.pdf
+
+      - name: Get current date
+        id: date
+        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
+
+      - name: Upload to Cloudflare R2
+        env:
+          AWS_ACCESS_KEY_ID: ${{ secrets.R2_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets.R2_SECRET_ACCESS_KEY }}
+          AWS_ENDPOINT_URL: ${{ secrets.R2_ENDPOINT }}
+          DATE: ${{ steps.date.outputs.date }}
+          BUCKET_NAME: ${{ secrets.R2_BUCKET_NAME }}
+        run: |
+          pip install boto3
+          python3 << 'EOF'
+          import boto3
+          import os
+
+          s3 = boto3.client('s3',
+              endpoint_url=os.environ['AWS_ENDPOINT_URL'],
+              aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],
+              aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY']
+          )
+
+          date = os.environ['DATE']
+          bucket = os.environ['BUCKET_NAME']
+
+          # upload dated version
+          dated_filename = f"thesis-{date}.pdf"
+          s3.upload_file(
+              'paper/build/main.pdf',
+              bucket,
+              dated_filename,
+              ExtraArgs={'ContentType': 'application/pdf'}
+          )
+          print(f"Uploaded {dated_filename}")
+
+          # upload latest version
+          s3.upload_file(
+              'paper/build/main.pdf',
+              bucket,
+              'thesis-latest.pdf',
+              ExtraArgs={'ContentType': 'application/pdf'}
+          )
+          print(f"Uploaded thesis-latest.pdf")
+          EOF

.gitignore

@@ -5,12 +5,28 @@
 **/.virtual_documents/
 **/session_*.svg
 **/*graph.svg
-paper/src/bib/auto
+**/auto/*.el
+*.old
+**/package-lock.json
+**/*.parquet
+**/_build/
 
-# Airflow logs - exclude DAG run logs
+paper/src/bib/auto
+=======
+**/_build/
+paper/src/auto/*
+paper/src/bib/auto
+docs/goals/*.md
+PHANTOM.wiki/
 experiments/airflow/logs/*
 experiments/airflow/logs/scheduler/
 experiments/airflow/logs/dag_processor_manager/
+experiments/collected_data/
+experiments/agents/collected_data/
+sim/rl/behavior_loader/*.dot
+sim/rl/behavior_loader/*.png
+sim/rl/behavior_loader/*.svg
+sim/rl/behavior_loader/*.pdf
 tests/e2e/node_modules/**
-**/auto/*.el
-*.old
+lab/case/thesis/runs*/
+sim/case/thesis_simplified/runs*/

Makefile

@@ -22,14 +22,15 @@ $(BUILDDIR):
 pdf.build: $(BUILDDIR)
 	@bash paper/concat_code.sh
 	@cd $(SRCDIR) && \
-	$(LATEXMK) -pdf -jobname=$(JOBNAME) \
+	$(LATEXMK) -pdf -jobname=$(JOBNAME) -f \
 		-interaction=nonstopmode -file-line-error \
+		-r ../.latexmkrc \
 		-outdir=../$(BUILDDIR) $(TEX)
 
 .PHONY: pdf.watch
 pdf.watch: $(BUILDDIR)
 	@cd $(SRCDIR) && \
-	$(LATEXMK) -pvc -pdf -jobname=$(JOBNAME) \
+	$(LATEXMK) -pvc -pdf -jobname=$(JOBNAME) -f \
 		-interaction=nonstopmode -file-line-error \
 		-r ../.latexmkrc \
 		-outdir=../$(BUILDDIR) $(TEX)
@@ -74,6 +75,18 @@ stats.lines:
 	@find . \( -path '*/node_modules' -o -path '*/.venv' -o -path '*/venv' \) -prune -o \
 	\( -name "*.ts" -o -name "*.py" \) -type f -print0 | xargs -0 cat | wc -l
 
+.PHONY wordcount
+wordcount:
+	@echo "Counting words in main text (excluding appendix)..."
+	@texcount -nosub -total -sum -1 \
+		$(SRCDIR)/chapters/01-intro.tex \
+		$(SRCDIR)/chapters/02-literature-review.tex \
+		$(SRCDIR)/chapters/03-methodology.tex \
+		$(SRCDIR)/chapters/04-results.tex \
+		$(SRCDIR)/chapters/05-discussion.tex \
+		$(SRCDIR)/chapters/06-conclusion.tex
+
+
 .PHONY: pdf clean watch run.webapp test count-lines all
 pdf: pdf.build
 clean: pdf.clean
@@ -81,4 +94,4 @@ watch: pdf.watch
 run.webapp: web.dev
 test: test.backend
 count-lines: stats.lines
-all: pdf.build
+all: pdf.build

README.md

@@ -3,10 +3,92 @@
 ### PHANTOM
 
 [![Build PDF](https://github.com/velocitatem/PHANTOM/actions/workflows/latex.yml/badge.svg)](https://github.com/velocitatem/PHANTOM/actions/workflows/latex.yml)
+[![Paper](https://img.shields.io/badge/Paper-PDF-red?logo=adobe-acrobat-reader)](https://pub-d5b94a3c29fd40c6b3881946e463fdb7.r2.dev/thesis-latest.pdf)
 [![TPU Research Cloud](https://img.shields.io/badge/TPU%20Research%20Cloud-TRC%20supported-4285F4?logo=googlecloud&logoColor=white)](https://sites.research.google/trc/faq/)
 [![Vercel Deploy](https://deploy-badge.vercel.app/?url=https://phantom-hotel.vercel.app&name=Hotel)](https://phantom-hotel.vercel.app)
 [![Vercel Deploy](https://deploy-badge.vercel.app/?url=https://phantom-airline.vercel.app&name=Airline)](https://phantom-airline.vercel.app)
 
 
 
-
+```mermaid
+mindmap
+  PHANTOM((PHANTOM Project))
+    North Star
+      Study how automated actors change markets 
+      Build an experimentation platform for real-world-like commerce 
+      Two-loop learning system
+        Online observation loop 
+        Offline "defense gym" loop 
+    Core Economic Questions
+      Price Discovery
+        How prices respond to demand signals
+        How signal quality changes with bots/agents
+      Demand & Elasticity
+        Shifts in willingness-to-pay
+        Short-run vs long-run elasticity
+      Market Efficiency & Welfare
+        Consumer surplus vs producer surplus
+        Deadweight loss from frictions/manipulation
+      Price Discrimination & Segmentation
+        Behavioral feature-based segmentation
+        Fairness vs profitability tradeoffs
+      Information Asymmetry
+        Agents amplify search and arbitrage
+        Sellers infer more about buyers; buyers infer more about sellers
+      Strategic Interaction
+        Consumers vs firms vs agents
+        Feedback loops: policy ↔ behavior ↔ price
+      Market Power & Competition
+        Algorithmic pricing as competitive tool
+        Risks: tacit coordination / "algorithmic collusion"
+      Externalities
+        Congestion and attention costs
+        Spillovers: one segment’s behavior affects others’ prices
+    System-Level View
+      Participants
+        Humans
+        Agents (automated buyers/actors) 
+        Firms (pricing decision-makers)
+        Platform (measurement + control layer)
+      Markets Simulated
+        Repeated transactions
+        Limited inventory / capacity constraints (conceptually)
+        Time dynamics (learning over time)
+      Interventions
+        Pricing policies
+        Experiment assignment / randomized exposure
+        Agent behavioral policies (task-driven)
+    Measurement & Causal Inference
+      What is observed
+        Actions (search, click, purchase intent)
+        Context (product attributes, time, exposure)
+        Outcomes (conversion, revenue, churn proxies)
+      Identification strategy
+        A/B tests and randomization
+        Counterfactual baselines
+        Robustness checks (offline replay)
+      Key metrics
+        Revenue / profit proxies
+        Conversion & bounce
+        Price volatility / stability
+        Welfare proxies (e.g., dispersion, access)
+    Risk, Governance, and Ethics
+      Manipulation & Integrity
+        Bot-driven demand distortion
+        Measurement contamination
+      Fairness & Transparency
+        Differential pricing concerns
+        Explainability and auditability
+      Safety Constraints
+        Guardrails on price moves
+        Monitoring for runaway feedback loops
+    Outputs
+      Insights
+        When do agents raise/lower prices via behavior shifts?
+        Which market designs are robust to automation?
+      Defenses
+        Agent-aware pricing policies (robust control)
+        Detection + mitigation strategies (feature-level separability)
+      Platform Value
+        Reusable testbed for market + AI-agent research
+```

docker-compose.yml

@@ -112,11 +112,14 @@ services:
     depends_on:
       - postgres
     environment:
-      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__CORE__EXECUTOR=LocalExecutor
       - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
       - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
       - AIRFLOW__CORE__LOAD_EXAMPLES=false
       - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - AIRFLOW__CORE__PARALLELISM=16
+      - AIRFLOW__CORE__DAG_CONCURRENCY=8
+      - AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG=4
       - _AIRFLOW_DB_MIGRATE=true
       - _AIRFLOW_WWW_USER_CREATE=true
       - _AIRFLOW_WWW_USER_USERNAME=admin
@@ -136,12 +139,17 @@ services:
       - airflow-init
       - redis
     environment:
-      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__CORE__EXECUTOR=LocalExecutor
       - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
       - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
       - AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION=true
       - AIRFLOW__CORE__LOAD_EXAMPLES=false
       - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - AIRFLOW__CORE__PARALLELISM=16
+      - AIRFLOW__CORE__DAG_CONCURRENCY=8
+      - AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG=4
+      - AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL=30
+      - AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL=60
       - AIRFLOW__WEBSERVER__EXPOSE_CONFIG=true
       - AIRFLOW__WEBSERVER__SECRET_KEY=${AIRFLOW_SECRET_KEY}
       - AIRFLOW__API__AUTH_BACKENDS=airflow.api.auth.backend.basic_auth
@@ -174,12 +182,18 @@ services:
       redis:
         condition: service_started
     environment:
-      - AIRFLOW__CORE__EXECUTOR=SequentialExecutor
+      - AIRFLOW__CORE__EXECUTOR=LocalExecutor
       - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
       - AIRFLOW__CORE__FERNET_KEY=${AIRFLOW_FERNET_KEY}
       - AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION=true
       - AIRFLOW__CORE__LOAD_EXAMPLES=false
       - AIRFLOW__CORE__ENABLE_XCOM_PICKLING=true
+      - AIRFLOW__CORE__PARALLELISM=16
+      - AIRFLOW__CORE__DAG_CONCURRENCY=8
+      - AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG=4
+      - AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL=30
+      - AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL=60
+      - AIRFLOW__SCHEDULER__PARSING_PROCESSES=2
       - AIRFLOW__WEBSERVER__SECRET_KEY=${AIRFLOW_SECRET_KEY}
       - AIRFLOW__API__AUTH_BACKENDS=airflow.api.auth.backend.basic_auth
       - KAFKA_HOST=kafka

docs/goals/goals.csv

@@ -0,0 +1,21 @@
+store_mode,task_name,task_description,definition_of_done
+airline,The Indecisive Executive (SEA-LAX),"You are traveling SEA to LAX for business. You prefer Business Class for the comfort, but you need to justify the expense to your company. 1) Find the Business Class option and check its price. 2) Compare it against the Economy option on the same route to see how much money you are saving or spending. 3) Spend some time weighing the pros and cons of the ""Flexible"" fare rule vs the standard one. 4) Ultimately, decide that your comfort is worth it and book the Business Class ticket.","Booking for SEA-LAX Business Class is completed."
+airline,The Cross-Country Splurge (LAX-JFK),"You are flying LAX to JFK and want to treat yourself to First Class, but only if it's the right flight. 1) Find the First Class option. 2) thoroughly check the details (duration, arrival time). 3) Compare it with the Business Class option if available, or just look at other departure times to ensure this is the best schedule. 4) After confirming this is the absolute best option, proceed to book First Class.","Booking for LAX-JFK First Class is completed."
+airline,The Budget Student (DFW-ORD),"You are a broke student flying DFW to ORD. You have a budget of roughly $200. 1) Find the cheapest Economy flight. 2) Before booking, frantically check if there are any other flights or if the ""Premium"" economy is somehow cheaper (it won't be, but you should check). 3) Hesitate for a moment to consider if you should just drive instead. 4) Resign yourself to the flight and book the Economy ticket.","Booking for DFW-ORD Economy Class is completed."
+airline,The Quick Hop Commuter (LAX-SFO),"You need to get from LAX to SFO as fast as possible. Price is secondary to speed. 1) Search for flights and identify the one with the shortest duration (1h 30m). 2) Click into the details to verify the arrival time fits your schedule. 3) briefly explore if there's a Business Class upgrade available for this short flight. 4) Decide to stick with Economy since it's such a short trip and book it.","Booking for LAX-SFO is completed."
+airline,The Status Chaser (SFO-SEA),"You are trying to earn airline points and need a ""Premium"" class ticket specifically. 1) Search SFO to SEA. 2) Filter or look for the Premium Economy option. 3) Compare the price gap between Premium and Standard Economy. 4) Browse the details to see if the ""Premium"" fare includes better baggage allowance. 5) Conclude it's worth the points and book the Premium seat.","Booking for SFO-SEA Premium Economy is completed."
+airline,The Family Reunion (MIA-ATL),"You are booking for a family of 4 (2 adults, 2 children) flying MIA to ATL. 1) Search for 4 passengers. 2) You prefer Premium, but if the total is too high, you might settle for Economy. 3) Add Premium to your cart, look at the total, and hesitate. 4) Go back and check the Economy price for 4 people. 5) Decide to treat your family and go back to book the Premium option.","Booking for MIA-ATL (Premium) is completed."
+airline,The Red Eye Skeptic (LAX-JFK),"You need to fly LAX to JFK but hate late arrivals. 1) Search for the flight and check the arrival time of the First Class option. 2) It arrives early morning (02:15), which worries you. 3) Spend some time looking for other flight options on different days to see if there's a better schedule. 4) Realize this is the only direct option that works and proceed to book it despite the time.","Booking for LAX-JFK is completed."
+airline,The Refundable Requirement (ATL-DFW),"Your meeting in Dallas might get cancelled, so you strictly need a ""Refundable"" ticket. 1) Search ATL to DFW. 2) Find the First Class option and verify it lists ""Refundable"". 3) Check the Economy option to see if it is also refundable (it might not be). 4) Weigh the cost difference. 5) Choose the First Class Refundable option for peace of mind.","Booking for ATL-DFW First Class is completed."
+airline,The Hub Connector (ORD-MIA),"You are flying ORD to MIA to catch a cruise. You cannot be late. 1) Search for the flight. 2) Verify the ""stops"" is 0 (Direct). 3) Click into details to check the duration. 4) Worry that 3h 30m might be too long in Economy. 5) Look for a Business class option. 6) Decide to save money for the cruise and book Economy.","Booking for ORD-MIA Economy is completed."
+airline,The West Coast Hopper (SEA-LAX Business),"You fly this route often and usually pay around $700. 1) Search SEA to LAX. 2) Find the Business Class ticket. 3) Check if the price is near your usual $720 or if it's surged. 4) If it looks expensive, browse other dates to compare. 5) Return to your original desired date and book the Business Class seat.","Booking for SEA-LAX Business is completed."
+hotel,The Honeymoon Suite (Presidential),"It is your honeymoon. You want the best room available, specifically one with a ""jacuzzi"". 1) Search for a room for 2 people. 2) Identify the ""Presidential Suite"". 3) Click details to confirm the amenities include a jacuzzi. 4) Browse the ""Executive Suite"" just to see what you are upgrading from. 5) Go back to the Presidential Suite, confirm it's the one you want, and book it.","Booking for the Presidential Suite is completed."
+hotel,The Digital Nomad (Executive),"You are working remotely and strictly need a ""workspace"". 1) Search for a room. 2) Check the ""Executive Suite"" details for a workspace. 3) Check the ""Deluxe Room"" to see if it also has a workspace and is cheaper. 4) Compare the images (if available) or amenity lists of both. 5) Decide the Executive Suite looks more comfortable for a week of work and book it.","Booking for the Executive Suite is completed."
+hotel,The Safety First (Superior),"You are traveling with valuables and need a ""safe"" in the room. 1) Search for a room. 2) Look at the ""Standard Room"" amenities. Does it have a safe? 3) Look at the ""Superior Room"". Verify it has a safe. 4) Compare the price difference. Is safety worth the extra cost? 5) Decide it is, and book the Superior Room.","Booking for the Superior Room is completed."
+hotel,The Bachelor Party (Max Occupancy),"You are booking for 4 guys. You want everyone in one room if possible. 1) Search for 4 adults. 2) Find the room that fits 4 people (Presidential). 3) It looks expensive. Go back and search for 2 adults to see the price of a ""Standard Room"". 4) Calculate if booking two Standard Rooms is cheaper than one Presidential. 5) Decide it's too much hassle to manage two bookings and book the Presidential Suite.","Booking for the Presidential Suite is completed."
+hotel,The Budget Refundable (Junior),"You want a cheap room but your dates might change, so it MUST be refundable. 1) Search for a room. 2) Sort by price or find the cheapest options. 3) Check the ""Standard"" and ""Superior"" rooms. Notice they are likely Non-Refundable. 4) Find the ""Junior Suite"" which is Refundable. 5) Grumble about the price difference but book the Junior Suite because you need the flexibility.","Booking for the Junior Suite is completed."
+hotel,The View Hunter (Executive),"You want a room with a ""city_view"" or balcony. 1) Search for a room. 2) Check the amenities of the ""Deluxe Room"". 3) Check the amenities of the ""Executive Suite"". 4) Compare the prices. 5) Decide to treat yourself to the Executive Suite for the better view/balcony and book it.","Booking for the Executive Suite is completed."
+hotel,The Just-A-Bed (Standard),"You just need a place to crash. Lowest price wins. 1) Search for a room. 2) Identify the absolute cheapest option (Standard Room). 3) Click details just to make sure it has ""wifi"". 4) Briefly glance at the ""Superior Room"" to see if the upgrade is <$10. 5) If not, go back and book the Standard Room immediately.","Booking for the Standard Room is completed."
+hotel,The Family Vacation (Deluxe),"You are traveling with a child. You need a room that isn't too cramped but not a suite. 1) Search for 2 adults, 1 child. 2) Look at the ""Deluxe Room"". 3) Check the amenities for ""coffee_maker"" (parents need coffee). 4) Compare it with the ""Junior Suite"". 5) Decide the Deluxe Room is sufficient value and book it.","Booking for the Deluxe Room is completed."
+hotel,The Long Stay (Junior),"You are staying for 7 nights. You want something nicer than a standard room but affordable. 1) Search for a room. 2) Look at the ""Junior Suite"". 3) Check the amenities for a ""mini_fridge"" or similar. 4) Compare the total cost for 7 nights against your budget. 5) Hesitate and look at the ""Standard Room"" price. 6) Decide the extra space of the Junior Suite is worth it for a long stay and book it.","Booking for the Junior Suite is completed."
+hotel,The Last Minute Panic (Superior),"It's late and you need a room for tonight. 1) Search for a room for 1 person. 2) You recognize the ""Superior Room"" brand. 3) Click it. 4) Quickly verify check-in times or details. 5) Don't overthink it—book the Superior Room as fast as possible.","Booking for the Superior Room is completed."

docs/index.html

@@ -47,7 +47,7 @@
   <meta name="citation_author" content="Rösel, Daniel">
   <meta name="citation_publication_date" content="2025">
   <meta name="citation_conference_title" content="IE University Bachelor's Thesis">
-  <meta name="citation_pdf_url" content="TODO">
+  <meta name="citation_pdf_url" content="https://pub-d5b94a3c29fd40c6b3881946e463fdb7.r2.dev/thesis-latest.pdf">
 
   <!-- Additional SEO -->
   <meta name="theme-color" content="#2563eb">
@@ -233,14 +233,13 @@
 
                   <div class="is-size-5 publication-authors">
                     <span class="author-block">IE University<br>Bachelor's Thesis 2025</span>
-                    <span class="eql-cntrb"><small><br>Advisor: <a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Alberto Martín Izquierdo</a></small></span>
+                    <span class="eql-cntrb"><small><br>Advisor: Alberto Martín Izquierdo</small></span>
                   </div>
 
                   <div class="column has-text-centered">
                     <div class="publication-links">
-                         <!-- TODO: Update with your arXiv paper ID -->
                       <span class="link-block">
-                        <a href="https://arxiv.org/pdf/<ARXIV PAPER ID>.pdf" target="_blank"
+                        <a href="https://pub-d5b94a3c29fd40c6b3881946e463fdb7.r2.dev/thesis-latest.pdf" target="_blank"
                         class="external-link button is-normal is-rounded is-dark">
                         <span class="icon">
                           <i class="fas fa-file-pdf"></i>
@@ -315,7 +314,10 @@
         <h2 class="title is-3">Abstract</h2>
         <div class="content has-text-justified">
           <p>
-            The primary objective of this thesis is to develop and validate pricing heuristics that protect e-commerce platforms from systematic exploitation by Large Language Model (LLM) agents within dynamic pricing environments. As AI agents increasingly mediate consumer transactions, they enable users to circumvent the Cost of Information (the price premium accumulated through demand signal expression) by conducting reconnaissance in isolated sessions before executing purchases through clean sessions at base prices. This research will make an anticipatory contribution by adapting recommendation system methodologies to distinguish between genuine human browsing behaviour and agent-orchestrated information gathering, thereby enabling pricing systems to maintain margin integrity without degrading the user experience for legitimate customers or getting rid of leads generated by LLMs.
+            This research establishes the following contributions: definition and formalization of non-human transactors in e-commerce platforms, development of a testing-ground for capturing the behavioral essence of these transactors across a large variety of digital systems, construction of a discriminative model to prove separability as a strong learner for downstream mitigation of contamination by non-human entities, translation of such learned separability into existing dynamic pricing machine learning loops, and establishment of a high-level KPI-affecting causal effect and cost-saving framework for the future of internet commerce in the presence of such non-human learners.
+          </p>
+          <p>
+            This work develops behavioral signature models using recommendation system techniques to profile session-level interaction, temporal engagement, and cross-session correlation. The AI Agent market is forecasted to grow from around USD 5-8 billion in 2025 to USD 42-52 billion by 2030, raising the question of how these systems should be designed for future robustness and how to maintain a competitive edge in the analytical components of e-commerce platforms.
           </p>
         </div>
       </div>
@@ -433,8 +435,7 @@
     <div class="container">
       <h2 class="title">Poster</h2>
 
-      <!-- TODO: Replace with your poster PDF -->
-      <iframe  src="static/pdfs/sample.pdf" width="100%" height="550">
+      <iframe  src="https://pub-d5b94a3c29fd40c6b3881946e463fdb7.r2.dev/thesis-latest.pdf" width="100%" height="550">
           </iframe>
 
       </div>

engine/engine.py

@@ -0,0 +1,66 @@
+from sys import platform
+import numpy as np
+from .lib.demand import generate_demand, estimate_demand
+from .lib.behavior import sample_behavior
+from logging import INFO, getLogger
+logger = getLogger(__name__)
+logger.setLevel(INFO)
+
+
+
+class MarketEngine():
+    def __init__(self,
+                 alpha = 0.5,
+                 N = 100,
+                 demand_distribution = (50, 10),
+                 demand_sampling_function = np.random.normal):
+        self.Nagents = int(N*alpha)
+        self.Nhumans = int(N*(1-alpha))
+        self.demand = (demand_sampling_function, demand_distribution)
+
+    def act(self, prices):
+        demand = generate_demand(prices, *self.demand)
+        sample_n = lambda n, human: [sample_behavior(demand, human=human) for _ in range(n)]
+        human_t, agent_t = sample_n(self.Nhumans, True), sample_n(self.Nagents, False)
+        trajectories = human_t + agent_t
+        demand_estimate = estimate_demand(trajectories)
+        return demand_estimate
+
+    def measure(self):
+        pass
+
+class PricingEngine():
+    def __init__(self,
+                 ) -> None:
+        pass
+
+    def act(self, demand):
+        return np.random.uniform(low=25, high=100, size=10)
+
+
+
+class Limbo():
+    def __init__(self,
+                 platform,
+                 market
+                 ) -> None:
+        self.platform_turn = True
+        self.platform = platform
+        self.market = market
+        self.output = None
+
+    def step(self):
+        # we could code golf this a little bit
+        if self.platform_turn:
+            self.output = self.platform.act(self.output)
+        else:
+            self.output = self.market.act(self.output)
+        print(self.output)
+        self.platform_turn = not self.platform_turn
+
+if __name__ == "__main__":
+    platform = PricingEngine()
+    market = MarketEngine()
+    limbo = Limbo(platform, market)
+    for _ in range(10):
+        limbo.step()

engine/lib/__init__.py

@@ -0,0 +1,3 @@
+from .demand import generate_demand, estimate_demand
+from .behavior import sample_behavior
+from .render import DashboardRenderer, style_axis

engine/lib/behavior.py

@@ -0,0 +1,47 @@
+from sim.rl.behavior_loader.models import BehaviorModel, AgentBehaviorModel, aggregate_event_transitions
+import pandas as pd
+import numpy as np
+from .demand import generate_demand
+
+base_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments"
+human_dir, agent_dir = f"{base_dir}/collected_data/", f"{base_dir}/agents/collected_data/"
+
+_cache = {}  # lazy cache for models and base pivots
+
+def _get_base_pivot(human: bool):
+    key = 'human' if human else 'agent'
+    if key not in _cache:
+        model = BehaviorModel(human_dir) if human else AgentBehaviorModel(agent_dir)
+        mdp = model.build_MDP()
+        _cache[key] = pd.DataFrame(aggregate_event_transitions(mdp)).fillna(0.0)
+    return _cache[key]
+
+def adjust_behavior_to_condition(condition, transition_matrix):
+    # expand NxN transition matrix to (N*P)x(N*P) weighted by demand condition
+    cond_norm = condition / np.sum(condition)
+    n_products = len(condition)
+    base_vals = transition_matrix.values
+    base_cols, base_rows = transition_matrix.columns.tolist(), transition_matrix.index.tolist()
+
+    # expand via kronecker-like tiling: each cell becomes a P*P block weighted by outer product of cond_norm
+    expanded = np.kron(base_vals, np.outer(cond_norm, cond_norm))
+    new_cols = [f"{c}_product{p}" for c in base_cols for p in range(n_products)]
+    new_rows = [f"{r}_product{p}" for r in base_rows for p in range(n_products)]
+    return pd.DataFrame(expanded, index=new_rows, columns=new_cols)
+
+def sample_behavior(condition, human=True, max_len=40):
+    base_pivot = _get_base_pivot(human)
+    adjusted_transitions = adjust_behavior_to_condition(condition, base_pivot)
+
+    trajectory = [np.random.choice(adjusted_transitions.index)]
+    while len(trajectory) < max_len or 'checkout' in trajectory[-1]:
+        probs = adjusted_transitions.loc[trajectory[-1]].values
+        sample = np.random.choice(adjusted_transitions.columns, p=probs/np.sum(probs) if np.sum(probs) > 0 else None)
+        trajectory.append(sample)
+    return trajectory
+
+if __name__ == "__main__":
+    t=sample_behavior(generate_demand(np.array([10,20,30])), human=True)
+    print(t)
+    t=sample_behavior(generate_demand(np.array([10,20,30])), human=False)
+    print(t)

engine/lib/demand.py

@@ -0,0 +1,45 @@
+import logging
+import numpy as np
+from logging import getLogger
+logger = getLogger(__name__)
+
+def generate_demand(prices, distribution_method = np.random.normal, distribution_params = (50.0, 10.0)):
+    # assumption 1: each product has an intrinsic valuation drawn from a normal distribution centered at 50
+    product_valuations = distribution_method(*distribution_params, size=len(prices))
+    # assumption 2: demand decreases as price increases, following a simple linear model
+    demand = np.maximum(0, product_valuations - prices) # demand cannot be negative
+    total = np.sum(demand)
+    demand = demand / total * 100 if total > 0 else demand  # normalize to percentage, avoid div by zero
+    logger.info(f"Generated demand for prices {prices}: {demand} with valuations from distribution {distribution_params}")
+    return demand
+
+def estimate_demand(trajectories):
+    demand_estimate = {}
+    for traj in trajectories:
+        for event in traj:
+            if 'view_product' in event:
+                product_id = int(event.split('_')[-1].replace('product', ''))
+                demand_estimate[product_id] = demand_estimate.get(product_id, 0) + 1
+    total_views = sum(demand_estimate.values())
+    for product_id in demand_estimate:
+        demand_estimate[product_id] = (demand_estimate[product_id] / total_views) * 100  # normalize to percentage
+    return demand_estimate
+
+# Example usage
+if __name__ == "__main__":
+    np.random.seed(42)
+    prices = np.array([20.0, 35.0, 50.0, 65.0])
+    demand = generate_demand(prices)
+    print("Generated Demand:", demand)
+    from .behavior import sample_behavior
+    N, alphat =200, 0.1
+    trajectories = []
+    for _ in range(int(N*(1 - alphat))):
+        trajectories.append(sample_behavior(demand, human=True))
+    for _ in range(int(N*alphat)):
+        trajectories.append(sample_behavior(demand, human=False))
+    demand_estimate = estimate_demand(trajectories)
+    print("Estimated Demand from Behavior:", demand_estimate)
+    delta = {k: demand_estimate.get(k, 0) - demand[i] for i, k in enumerate(range(len(prices)))}
+    delta = np.mean([np.abs(v) for v in delta.values()])
+    print("Demand Delta:", delta)

engine/lib/render.py

@@ -0,0 +1,126 @@
+"""rendering logic for PHANTOM environment dashboard"""
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+
+
+def style_axis(ax, title: str = None, xlabel: str = None, ylabel: str = None):
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    if title: ax.set_title(title, fontsize=11, fontweight='bold', pad=8)
+    if xlabel: ax.set_xlabel(xlabel, fontsize=9)
+    if ylabel: ax.set_ylabel(ylabel, fontsize=9)
+
+
+class DashboardRenderer:
+    """stateful renderer for PHANTOM market dynamics visualization"""
+
+    def __init__(self):
+        self.fig = None
+        self.gs = None
+
+    def render(self, env) -> None:
+        if self.fig is None:
+            plt.ion()
+            self.fig = plt.figure(figsize=(14, 10))
+            self.gs = GridSpec(3, 3, figure=self.fig, hspace=0.35, wspace=0.3,
+                               left=0.07, right=0.95, top=0.92, bottom=0.08)
+            plt.show(block=False)
+
+        self.fig.clear()
+        self.fig.suptitle(f'PHANTOM  Market Dynamics  [t={env._step_count}, a={env.alpha:.2f}]',
+                          fontsize=14, fontweight='bold')
+
+        demand_mat = np.array(env._demand_history).T
+        price_mat = np.array(env._price_history).T
+        elasticity = env._compute_elasticity()
+
+        self._render_scatter(env)
+        self._render_elasticity_bar(env, elasticity)
+        self._render_session_pie(env)
+        self._render_price_heatmap(price_mat)
+        self._render_demand_heatmap(demand_mat)
+        self._render_correlation(env.n_products, price_mat, demand_mat)
+        self._render_revenue(env)
+
+        self.fig.canvas.draw_idle()
+        self.fig.canvas.flush_events()
+
+    def _render_scatter(self, env):
+        ax = self.fig.add_subplot(self.gs[0, 0])
+        prices_flat = np.array(env._price_history).flatten()
+        demands_flat = np.array(env._demand_history).flatten()
+        product_ids = np.tile(np.arange(env.n_products), len(env._price_history))
+        ax.scatter(prices_flat, demands_flat, c=product_ids, cmap='plasma', alpha=0.6, s=15, edgecolors='none')
+        if len(prices_flat) > 1:
+            z = np.polyfit(prices_flat, demands_flat, 1)
+            p_line = np.linspace(prices_flat.min(), prices_flat.max(), 50)
+            ax.plot(p_line, np.polyval(z, p_line), '--', lw=1.5, alpha=0.8)
+        style_axis(ax, "Price-Demand Relationship", "Price ($)", "Demand")
+
+    def _render_elasticity_bar(self, env, elasticity):
+        ax = self.fig.add_subplot(self.gs[0, 1])
+        ax.barh(range(env.n_products), elasticity, alpha=0.8)
+        ax.axvline(0, lw=0.8, alpha=0.5)
+        ax.axvline(-1, lw=1, ls='--', alpha=0.5)
+        ax.set_yticks(range(env.n_products))
+        ax.set_yticklabels([f'P{i}' for i in range(env.n_products)], fontsize=7)
+        style_axis(ax, "Price Elasticity", "(dQ/dP)(P/Q)", None)
+
+    def _render_session_pie(self, env):
+        ax = self.fig.add_subplot(self.gs[0, 2])
+        n_h, n_a = env.market.Nhumans, env.market.Nagents
+        wedges, _ = ax.pie([n_h, n_a], startangle=90, wedgeprops={'linewidth': 2, 'edgecolor': 'white'})
+        ax.legend(wedges, [f'H ({n_h})', f'A ({n_a})'], loc='lower center', fontsize=8,
+                  frameon=False, bbox_to_anchor=(0.5, -0.05))
+        ax.set_title("Session Mix", fontsize=11, fontweight='bold')
+
+    def _render_price_heatmap(self, price_mat):
+        ax = self.fig.add_subplot(self.gs[1, :2])
+        im = ax.imshow(price_mat, aspect='auto', cmap='viridis', origin='lower')
+        style_axis(ax, "Price Heatmap P(product, t)", "Step", "Product")
+        cbar = self.fig.colorbar(im, ax=ax, fraction=0.03, pad=0.02)
+        cbar.set_label('$', fontsize=8)
+
+    def _render_demand_heatmap(self, demand_mat):
+        ax = self.fig.add_subplot(self.gs[1, 2])
+        im = ax.imshow(demand_mat, aspect='auto', cmap='Blues', origin='lower')
+        style_axis(ax, "Demand Q(product, t)", "Step", None)
+        self.fig.colorbar(im, ax=ax, fraction=0.046, pad=0.02)
+
+    def _render_correlation(self, n_products, price_mat, demand_mat):
+        ax = self.fig.add_subplot(self.gs[2, 0])
+        if price_mat.shape[1] > 2:
+            corr = np.corrcoef(price_mat, demand_mat)[:n_products, n_products:]
+            im = ax.imshow(corr, cmap='RdBu', vmin=-1, vmax=1, aspect='auto')
+            ax.set_xticks(range(n_products))
+            ax.set_yticks(range(n_products))
+            ax.set_xticklabels([f'Q{i}' for i in range(n_products)], fontsize=6)
+            ax.set_yticklabels([f'P{i}' for i in range(n_products)], fontsize=6)
+            self.fig.colorbar(im, ax=ax, fraction=0.046, pad=0.02)
+        style_axis(ax, "Price-Demand Correlation", None, None)
+
+    def _render_revenue(self, env):
+        ax = self.fig.add_subplot(self.gs[2, 1:])
+        n_steps = len(env._revenue_history)
+        demand_std = [np.std(d) for d in env._demand_history]
+        ax.fill_between(range(n_steps), env._revenue_history, alpha=0.3)
+        ax.plot(env._revenue_history, linewidth=2, label='Revenue')
+        ax.set_xlim(0, max(n_steps, 1))
+        ax.set_ylim(0, max(env._revenue_history) * 1.1 if env._revenue_history else 1)
+
+        ax2 = ax.twinx()
+        ax2.plot(range(n_steps), demand_std, linewidth=2, ls='-', alpha=0.9, label='sigma(Demand)')
+        d_min, d_max = min(demand_std), max(demand_std)
+        margin = (d_max - d_min) * 0.2 if d_max > d_min else 0.5
+        ax2.set_ylim(max(0, d_min - margin), d_max + margin)
+        ax2.set_ylabel('Demand sigma', fontsize=9)
+
+        style_axis(ax, "Revenue & Demand Dispersion", "Step", "Revenue ($)")
+        ax.legend(loc='upper left', fontsize=7, frameon=False)
+        ax2.legend(loc='upper right', fontsize=7, frameon=False)
+
+    def close(self):
+        if self.fig:
+            plt.close(self.fig)
+            self.fig = None

engine/studies/factors.py

@@ -0,0 +1,34 @@
+"""shared factor definitions for experimental designs"""
+import numpy as np
+from dataclasses import dataclass, field
+from typing import Callable, Any
+
+@dataclass
+class Factor:
+    name: str
+    levels: list
+    primary: bool = True  # full cross vs sampled
+
+# demand functions with compatible signatures
+def demand_linear(mu, sigma, size): return np.maximum(0, np.random.normal(mu, sigma, size))
+def demand_uniform(mu, sigma, size): return np.random.uniform(mu - sigma, mu + sigma, size)
+def demand_exponential(mu, sigma, size): return np.random.exponential(mu, size)
+def demand_logistic(mu, sigma, size): return np.random.logistic(mu, sigma, size)
+
+DEMAND_FUNCTIONS = {
+    "linear": demand_linear,
+    "uniform": demand_uniform,
+    "exponential": demand_exponential,
+    "logistic": demand_logistic,
+}
+
+FACTORS = [
+    Factor("demand_fn", list(DEMAND_FUNCTIONS.keys()), primary=True),
+    Factor("alpha", [0.1, 0.3, 0.5, 0.7], primary=True),
+    Factor("n_products", [5, 15, 30, 50], primary=True),
+    Factor("demand_mu", [30.0, 50.0, 70.0], primary=False),
+    Factor("demand_sigma", [5.0, 10.0, 20.0], primary=False),
+    Factor("N", [100, 500, 1000], primary=False),
+]
+
+SEEDS_PER_CONFIG = 5

engine/studies/full_factorial.py

@@ -0,0 +1,89 @@
+"""full factorial design - all factor combinations"""
+import sys
+sys.path.insert(0, "..")
+import logging
+from itertools import product
+import json
+import hashlib
+from pathlib import Path
+from concurrent.futures import ProcessPoolExecutor
+from .factors import FACTORS, DEMAND_FUNCTIONS, SEEDS_PER_CONFIG
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+log = logging.getLogger(__name__)
+
+def generate_configs():
+    """generate all factor combinations with seeds"""
+    all_levels = [f.levels for f in FACTORS]
+    names = [f.name for f in FACTORS]
+
+    configs = []
+    for combo in product(*all_levels):
+        base = {names[i]: combo[i] for i in range(len(names))}
+        for seed in range(SEEDS_PER_CONFIG):
+            cfg = {**base, "seed": seed}
+            cfg["id"] = hashlib.md5(json.dumps(cfg, sort_keys=True).encode()).hexdigest()[:8]
+            configs.append(cfg)
+    return configs
+
+def run_single(cfg: dict) -> dict:
+    """execute one experiment config, return metrics"""
+    from engine.wrapper import PHANTOM
+    import numpy as np
+
+    np.random.seed(cfg["seed"])
+    demand_fn = DEMAND_FUNCTIONS[cfg["demand_fn"]]
+
+    env = PHANTOM(
+        n_products=cfg["n_products"],
+        alpha=cfg["alpha"],
+        N=cfg["N"],
+    )
+    env.market.demand = (demand_fn, (cfg["demand_mu"], cfg["demand_sigma"]))
+
+    obs, _ = env.reset()
+    total_reward, steps = 0.0, 0
+
+    for _ in range(100):
+        action = env.action_space.sample()
+        obs, reward, term, trunc, _ = env.step(action)
+        total_reward += reward
+        steps += 1
+        if term: break
+
+    env.close()
+    return {
+        "id": cfg["id"],
+        "config": cfg,
+        "total_reward": total_reward,
+        "avg_reward": total_reward / steps if steps > 0 else 0.0,
+        "steps": steps,
+    }
+
+def run_study(max_workers: int = None, output: str = "results_full.jsonl"):
+    configs = generate_configs()
+    log.info(f"full factorial: {len(configs)} configs ({len(configs)//SEEDS_PER_CONFIG} unique × {SEEDS_PER_CONFIG} seeds)")
+
+    results = []
+    with ProcessPoolExecutor(max_workers=max_workers) as ex:
+        for i, result in enumerate(ex.map(run_single, configs)):
+            results.append(result)
+            if (i+1) % 100 == 0: log.info(f"progress: {i+1}/{len(configs)}")
+
+    Path(output).write_text("\n".join(json.dumps(r) for r in results))
+    log.info(f"wrote {len(results)} results to {output}")
+    return results
+
+if __name__ == "__main__":
+    import argparse
+    p = argparse.ArgumentParser()
+    p.add_argument("--workers", type=int, default=None)
+    p.add_argument("--output", default="results_full.jsonl")
+    p.add_argument("--dry-run", action="store_true", help="only show design size")
+    args = p.parse_args()
+
+    configs = generate_configs()
+    log.info(f"design: {len(configs)} runs | factors: {[f.name for f in FACTORS]} | levels: {[len(f.levels) for f in FACTORS]}")
+
+    if not args.dry_run:
+        run_study(args.workers, args.output)

engine/studies/mixed_lh.py

@@ -0,0 +1,106 @@
+"""mixed design: full factorial on primary factors, latin hypercube on secondary"""
+import sys
+sys.path.insert(0, "..")
+import logging
+from itertools import product
+import json
+import hashlib
+from pathlib import Path
+from concurrent.futures import ProcessPoolExecutor
+import numpy as np
+from scipy.stats.qmc import LatinHypercube
+from factors import FACTORS, DEMAND_FUNCTIONS, SEEDS_PER_CONFIG
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+log = logging.getLogger(__name__)
+
+LH_SAMPLES = 10
+
+def generate_configs(lh_samples: int = LH_SAMPLES):
+    primary = [f for f in FACTORS if f.primary]
+    secondary = [f for f in FACTORS if not f.primary]
+
+    primary_grid = list(product(*[f.levels for f in primary]))
+    lhs = LatinHypercube(d=len(secondary), seed=42)
+
+    configs = []
+    for p_combo in primary_grid:
+        samples = lhs.random(n=lh_samples)
+        for s in samples:
+            sec_vals = {
+                secondary[i].name: secondary[i].levels[int(s[i] * len(secondary[i].levels))]
+                for i in range(len(secondary))
+            }
+            base = {primary[i].name: p_combo[i] for i in range(len(primary))}
+            base.update(sec_vals)
+
+            for seed in range(SEEDS_PER_CONFIG):
+                cfg = {**base, "seed": seed}
+                cfg["id"] = hashlib.md5(json.dumps(cfg, sort_keys=True).encode()).hexdigest()[:8]
+                configs.append(cfg)
+    return configs
+
+def run_single(cfg: dict) -> dict:
+    from engine.wrapper import PHANTOM
+    import numpy as np
+
+    np.random.seed(cfg["seed"])
+    demand_fn = DEMAND_FUNCTIONS[cfg["demand_fn"]]
+
+    env = PHANTOM(
+        n_products=cfg["n_products"],
+        alpha=cfg["alpha"],
+        N=cfg["N"],
+    )
+    env.market.demand = (demand_fn, (cfg["demand_mu"], cfg["demand_sigma"]))
+
+    obs, _ = env.reset()
+    total_reward, steps = 0.0, 0
+
+    for _ in range(100):
+        action = env.action_space.sample()
+        obs, reward, term, trunc, _ = env.step(action)
+        total_reward += reward
+        steps += 1
+        if term: break
+
+    env.close()
+    return {
+        "id": cfg["id"],
+        "config": cfg,
+        "total_reward": total_reward,
+        "avg_reward": total_reward / steps,
+        "steps": steps,
+    }
+
+def run_study(max_workers: int = None, output: str = "results_mixed.jsonl", lh_samples: int = LH_SAMPLES):
+    configs = generate_configs(lh_samples)
+    n_primary_cells = int(np.prod([len(f.levels) for f in FACTORS if f.primary]))
+    log.info(f"mixed LH: {len(configs)} configs ({n_primary_cells} primary × {lh_samples} LH × {SEEDS_PER_CONFIG} seeds)")
+
+    results = []
+    with ProcessPoolExecutor(max_workers=max_workers) as ex:
+        for i, result in enumerate(ex.map(run_single, configs)):
+            results.append(result)
+            if (i+1) % 100 == 0: log.info(f"progress: {i+1}/{len(configs)}")
+
+    Path(output).write_text("\n".join(json.dumps(r) for r in results))
+    log.info(f"wrote {len(results)} results to {output}")
+    return results
+
+if __name__ == "__main__":
+    import argparse
+    p = argparse.ArgumentParser()
+    p.add_argument("--workers", type=int, default=None)
+    p.add_argument("--output", default="results_mixed.jsonl")
+    p.add_argument("--lh-samples", type=int, default=10)
+    p.add_argument("--dry-run", action="store_true", help="only show design size")
+    args = p.parse_args()
+
+    primary = [f for f in FACTORS if f.primary]
+    secondary = [f for f in FACTORS if not f.primary]
+    configs = generate_configs(args.lh_samples)
+    log.info(f"design: {len(configs)} runs | primary: {[f.name for f in primary]} | secondary (LH): {[f.name for f in secondary]}")
+
+    if not args.dry_run:
+        run_study(args.workers, args.output, args.lh_samples)

engine/train.py

@@ -0,0 +1,45 @@
+from stable_baselines3 import SAC
+from stable_baselines3.common.callbacks import EvalCallback, BaseCallback
+from .wrapper import PHANTOM
+
+
+class RenderCallback(BaseCallback):
+    """Renders environment on every step for live visualization."""
+    def __init__(self, env: PHANTOM):
+        super().__init__()
+        self.env = env
+
+    def _on_step(self) -> bool:
+        self.env.render()
+        return True
+
+
+env = PHANTOM(n_products=10, alpha=0.3, render_mode="human")
+eval_env = PHANTOM(n_products=10, alpha=0.3, render_mode=None)
+
+model = SAC(
+    "MultiInputPolicy",
+    env,
+    verbose=1,
+    learning_rate=3e-4,
+    buffer_size=50000,
+    batch_size=256,
+    tau=0.005,
+    gamma=0.99,
+)
+
+render_cb = RenderCallback(env)
+eval_cb = EvalCallback(eval_env, eval_freq=1000, n_eval_episodes=5, verbose=1)
+
+model.learn(total_timesteps=50000, callback=[render_cb, eval_cb])
+model.save("phantom_sac")
+
+# test trained policy
+env = PHANTOM(n_products=10, alpha=0.3, render_mode="human")
+obs, _ = env.reset()
+for _ in range(100):
+    action, _ = model.predict(obs, deterministic=True)
+    obs, reward, term, trunc, _ = env.step(action)
+    env.render()
+    if term or trunc: break
+env.close()

engine/wrapper.py

@@ -0,0 +1,118 @@
+import gymnasium as gym
+from gymnasium import spaces
+import numpy as np
+from .engine import Limbo, MarketEngine, PricingEngine
+from .lib.render import DashboardRenderer
+
+
+class PHANTOM(gym.Env):
+    """Gymnasium wrapper for the Limbo pricing-market simulation. Platform sets prices, market responds with demand."""
+    metadata = {"render_modes": ["human", "ansi"]}
+
+    def __init__(self,
+                 n_products: int = 10,
+                 alpha: float = 0.3,
+                 N: int = 100,
+                 price_bounds: tuple = (10.0, 150.0),
+                 lambda_coi: float = 0.1,
+                 render_mode: str = None):
+        super().__init__()
+        self.n_products = n_products
+        self.price_bounds = price_bounds
+        self.lambda_coi = lambda_coi
+        self.render_mode = render_mode
+        self.alpha = alpha
+        self.N = N
+
+        self.market = MarketEngine(alpha=alpha, N=N)
+        self._platform_stub = PricingEngine()
+        self._limbo = Limbo(self._platform_stub, self.market)
+
+        self.action_space = spaces.Box(
+            low=price_bounds[0], high=price_bounds[1],
+            shape=(n_products,), dtype=np.float32
+        )
+        self.observation_space = spaces.Dict({
+            "demand": spaces.Box(low=0.0, high=100.0, shape=(n_products,), dtype=np.float32),
+            "prices": spaces.Box(low=price_bounds[0], high=price_bounds[1], shape=(n_products,), dtype=np.float32),
+        })
+
+        self._prices = None
+        self._demand = None
+        self._step_count = 0
+        self._demand_history = []
+        self._price_history = []
+        self._revenue_history = []
+        self._renderer = None
+
+    def _get_obs(self) -> dict:
+        demand_arr = np.array([self._demand.get(i, 0.0) for i in range(self.n_products)], dtype=np.float32)
+        return {"demand": demand_arr, "prices": self._prices.astype(np.float32)}
+
+    def _compute_reward(self, prices: np.ndarray, demand: dict) -> float:
+        revenue = np.sum(prices * np.array([demand.get(i, 0.0) for i in range(self.n_products)]))
+        # TODO: implement supra-competitive price punishment
+        return float(revenue)
+
+    def _record_history(self):
+        demand_arr = np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])
+        self._demand_history.append(demand_arr)
+        self._price_history.append(self._prices.copy())
+        self._revenue_history.append(np.sum(self._prices * demand_arr))
+
+    def reset(self, seed=None, options=None):
+        super().reset(seed=seed)
+        self._prices = np.random.uniform(*self.price_bounds, size=self.n_products)
+        self._demand = self.market.act(self._prices)
+        self._step_count = 0
+        self._demand_history, self._price_history, self._revenue_history = [], [], []
+        self._record_history()
+        return self._get_obs(), {}
+
+    def step(self, action: np.ndarray):
+        self._prices = np.clip(action, *self.price_bounds)
+        self._demand = self.market.act(self._prices)
+        self._step_count += 1
+        self._record_history()
+
+        reward = self._compute_reward(self._prices, self._demand)
+        terminated = self._step_count >= 100
+
+        return self._get_obs(), reward, terminated, False, {"step": self._step_count}
+
+    def _compute_elasticity(self) -> np.ndarray:
+        """point elasticity: e = (dQ/dP) * (P/Q) via finite differences, clipped to [-5, 5]"""
+        if len(self._price_history) < 2:
+            return np.zeros(self.n_products)
+        p, q = np.array(self._price_history), np.array(self._demand_history)
+        dp, dq = np.diff(p, axis=0), np.diff(q, axis=0)
+        valid = np.abs(dp) > 0.5
+        with np.errstate(divide='ignore', invalid='ignore'):
+            elasticity = np.where(valid, (dq / dp) * (p[:-1] / np.maximum(q[:-1], 1.0)), 0.0)
+            elasticity = np.nan_to_num(np.clip(elasticity, -5.0, 5.0), nan=0.0)
+        return np.mean(elasticity, axis=0) if len(elasticity) > 0 else np.zeros(self.n_products)
+
+    def render(self):
+        if self.render_mode == "human":
+            if self._renderer is None:
+                self._renderer = DashboardRenderer()
+            self._renderer.render(self)
+        elif self.render_mode == "ansi":
+            return f"step={self._step_count}, prices={self._prices}, demand={self._demand}"
+        return None
+
+    def close(self):
+        if self._renderer:
+            self._renderer.close()
+            self._renderer = None
+
+
+if __name__ == "__main__":
+    env = PHANTOM(n_products=15, alpha=0.3, N=100, render_mode="human")
+    obs, _ = env.reset()
+    for step in range(100):
+        action = env.action_space.sample()
+        obs, reward, term, trunc, info = env.step(action)
+        env.render()
+        if term: break
+    env.close()

experiments/agents/run.py

@@ -0,0 +1,117 @@
+from supabase import create_client, Client
+import os
+import random
+import asyncio
+import json
+from dotenv import load_dotenv
+
+from experiments.agents.agent import get_agent, AgentTypes
+from lib.kafka_client import get_interactions
+
+load_dotenv()
+
+RESULTS="/home/velocitatem/Documents/Projects/PHANTOM/experiments/agents/collected_data/"
+
+client = create_client(
+    os.getenv("NEXT_PUBLIC_SUPABASE_URL"),
+    os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
+)
+def pick_random_task():
+    mode = 'hotel'
+    tasks = client.table("tasks").select("*").execute().data
+    if mode == 'hotel':
+        # drop all that have 'flight' in the description
+        tasks = [task for task in tasks if 'flight' not in task['task_description'].lower()]
+    return random.choice(tasks) if tasks else None
+
+def clear_kafka_data():
+    """Delete and recreate Kafka topics to clear all data"""
+    from kafka.admin import KafkaAdminClient, NewTopic
+    from kafka.errors import UnknownTopicOrPartitionError
+    import time
+
+    kafka_host = os.getenv('KAFKA_HOST', 'localhost')
+    kafka_port = os.getenv('KAFKA_PORT', '9092')
+    broker = f'{kafka_host}:{kafka_port}'
+
+    admin = KafkaAdminClient(bootstrap_servers=broker)
+    topics = ['user-interactions', 'price-logs']
+
+    try:
+        admin.delete_topics(topics, timeout_ms=5000)
+        print(f"Deleted topics: {topics}")
+        time.sleep(2)
+    except UnknownTopicOrPartitionError:
+        print("Topics don't exist, skipping delete")
+    except Exception as e:
+        print(f"Error deleting topics: {e}")
+
+    new_topics = [
+        NewTopic(name='user-interactions', num_partitions=3, replication_factor=1),
+        NewTopic(name='price-logs', num_partitions=3, replication_factor=1)
+    ]
+
+    try:
+        admin.create_topics(new_topics=new_topics, validate_only=False)
+        print(f"Recreated topics: {topics}")
+    except Exception as e:
+        print(f"Error creating topics: {e}")
+    finally:
+        admin.close()
+
+def create_new_experiment(task_id):
+    import uuid
+    subject_name = f"agent_{str(uuid.uuid4())[:8]}"
+    experiment = {
+        "subject_name": subject_name,
+        "xp_human_only": False,
+        "xp_market_mode": "hotel",
+        "xp_task_id": task_id,
+    }
+    response = client.table("experiments").insert(experiment).execute()
+    return response.data[0] if response.data else None
+
+if __name__ == "__main__":
+    clear_kafka_data()
+
+    task = pick_random_task()
+    if not task:
+        print("No tasks available")
+        exit(1)
+
+    experiment = create_new_experiment(task['id'])
+    exp_id = experiment['id']
+    exp_dir = f"{RESULTS}{exp_id}"
+    os.makedirs(exp_dir, exist_ok=True)
+
+    # construct experiment URL with uuid param
+    base_url = os.getenv('NEXT_PUBLIC_API_BASE', 'http://localhost:3000')
+    agent_url = f"{base_url}/start-task?uuid={exp_id}"
+
+    print(f"Created experiment {exp_id} for task {task['id']}")
+    print(f"Agent will interact with: {agent_url}")
+
+    # instantiate and run agent
+    agent = get_agent(
+        AgentTypes.GENERIC_BROWSER_USE_AGENT,
+        goal=task['task_description'],
+        url=agent_url,
+        timeout=300,
+        headless=True
+    )
+
+    result = asyncio.run(agent.act())
+    print(f"Agent result: {result}")
+
+    # export interaction and price data from kafka
+    interactions = get_interactions(topic='user-interactions', timeout_ms=3000)
+    prices = get_interactions(topic='price-logs', timeout_ms=3000)
+
+    with open(f"{exp_dir}/int.json", 'w') as f:
+        json.dump(interactions, f, indent=2)
+
+    with open(f"{exp_dir}/price.json", 'w') as f:
+        json.dump(prices, f, indent=2)
+
+    print(f"Experiment {exp_id} completed.")
+    print(f"Exported {len(interactions)} interactions and {len(prices)} price logs to {exp_dir}")

experiments/airflow/dags/surge_pricing_factory.py

@@ -1,3 +1,4 @@
+from pandas.core.algorithms import factorize_array
 from airflow import DAG
 from airflow.operators.python import PythonOperator
 from airflow.utils.dates import days_ago
@@ -208,3 +209,12 @@ def create_surge_pricing_dag(store_mode: str) -> DAG:
 # instantiate DAGs for Airflow to discover
 dag_airline = create_surge_pricing_dag('airline')
 dag_hotel = create_surge_pricing_dag('hotel')
+
+# TODO: Refactor this factory from a surge pricing factory to a general pricing factory
+# We will do this by passing a pricing strategy class to the factory, since the generic pipeline is:
+# take all interaction data, group by sessionId and assign a new price vector to each session
+# in the grouping we get a subset of the interactions per sessionId and we can map that to some Features
+# we define a custom _get_features(interactions .) methodin the strategy class
+# we then run only the inference which is the .predict(trajectory) per-session which will give us a new price vector
+# this we then publish for each sessionId group
+# this might include no deleting most of the pricers we have defined and starting with a super simple surge-pricing algorithm that is no-fit only predict. This we can then test end-to-end and observe changes to prices according to a desired strategy - we have to define this one as a very short term strategy because we run sessions that take only a few minutes.

experiments/ml/__init__.py

@@ -1,11 +1,21 @@
 from .evals import evaluate
 from .arch import (
     XGBoostAgentClassifier,
-    LightGBMAgentClassifier
+    LightGBMAgentClassifier,
+    ContrastiveWeakClassifier,
+    TrajectoryEncoder,
+    WeakClassifier,
+    contrastive_loss,
+    featurize_trajectory,
 )
 
-__all__ =[
+__all__ = [
     'evaluate',
     'XGBoostAgentClassifier',
-    'LightGBMAgentClassifier'
+    'LightGBMAgentClassifier',
+    'ContrastiveWeakClassifier',
+    'TrajectoryEncoder',
+    'WeakClassifier',
+    'contrastive_loss',
+    'featurize_trajectory',
 ]

experiments/ml/arch.py

@@ -1,122 +1,212 @@
 # sklearn compatible models for agent detection
 from sklearn.base import BaseEstimator, ClassifierMixin
-from procesing.context import PipelineContext
-from typing import Any, Optional, Tuple
+from typing import Any, Optional, Tuple, Dict, List
 from abc import ABC, abstractmethod
-import xgboost as xgb
-import lightgbm as lgb
+from collections import defaultdict
 import numpy as np
 import pandas as pd
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import sys
+from pathlib import Path
+
+# add lib to path for imports
+sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'lib'))
+from lib.features import (
+    transition_histogram as _lib_transition_histogram,
+    temporal_signature as _lib_temporal_signature,
+    state_coverage as _lib_state_coverage,
+    transition_entropy as _lib_transition_entropy,
+    featurize_trajectory as _lib_featurize_trajectory,
+    parse_timestamp
+)
+from lib.state import event_to_state, get_event_name, get_timestamp
 
 TASK = 'classification'
 LABELS = ['human', 'agent']
 
 
-class BaseAgentClassifier(BaseEstimator, ClassifierMixin, ABC):
-    """Base class for tree-based agent detection classifiers with common logic"""
+class WeakClassifier(BaseEstimator, ClassifierMixin, ABC):
+    # a simple contrastive machine learning model learns to distinguish human/agent behavior
+    # using weakly supervised contrastive learning + augmentation
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.model = None
+        self.kwargs = kwargs
 
-    def __init__(self, context: Optional[PipelineContext] = None, n_estimators: int = 200,
-                 max_depth: int = 6, learning_rate: float = 0.05,
-                 early_stopping_rounds: int = 20):
-        self.context = context
+
+class TrajectoryEncoder(nn.Module):
+    """Encode variable-length event sequences to fixed-dim embedding via bidirectional LSTM"""
+    def __init__(self, input_dim: int, embed_dim: int = 32, hidden_dim: int = 64):
+        super().__init__()
+        self.event_embed = nn.Linear(input_dim, hidden_dim)
+        self.lstm = nn.LSTM(hidden_dim, hidden_dim, batch_first=True, bidirectional=True)
+        self.proj = nn.Linear(hidden_dim * 2, embed_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:  # x: (batch, seq_len, input_dim)
+        h = F.relu(self.event_embed(x))
+        _, (hn, _) = self.lstm(h)
+        hn = torch.cat([hn[-2], hn[-1]], dim=1)  # concat bidirectional hidden states
+        return F.normalize(self.proj(hn), dim=1)  # L2 normalized
+
+
+class ContrastiveWeakClassifier(WeakClassifier):
+    """Contrastive learning classifier for human/agent trajectory discrimination"""
+    def __init__(self, input_dim: int = 64, embed_dim: int = 32, margin: float = 1.0, **kwargs):
+        super().__init__(**kwargs)
+        self.input_dim = input_dim
+        self.embed_dim = embed_dim
+        self.margin = margin
+        self.encoder = TrajectoryEncoder(input_dim, embed_dim)
+        self.classifier = nn.Linear(embed_dim, 2)
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self._fitted = False
+
+    def to_device(self):
+        self.encoder.to(self.device)
+        self.classifier.to(self.device)
+        return self
+
+    def encode(self, x: torch.Tensor) -> torch.Tensor:
+        return self.encoder(x.to(self.device))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        emb = self.encode(x)
+        return self.classifier(emb)
+
+    def fit(self, X, y=None):  # sklearn interface - actual training in weak.train.py
+        self._fitted = True
+        return self
+
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        self.encoder.eval()
+        self.classifier.eval()
+        with torch.no_grad():
+            x = torch.tensor(X, dtype=torch.float32).unsqueeze(1).to(self.device)
+            logits = self.forward(x)
+            return torch.argmax(logits, dim=1).cpu().numpy()
+
+    def predict_proba(self, X: np.ndarray) -> np.ndarray:
+        self.encoder.eval()
+        self.classifier.eval()
+        with torch.no_grad():
+            x = torch.tensor(X, dtype=torch.float32).unsqueeze(1).to(self.device)
+            logits = self.forward(x)
+            return F.softmax(logits, dim=1).cpu().numpy()
+
+
+def contrastive_loss(anchor: torch.Tensor, positive: torch.Tensor, negative: torch.Tensor, margin: float = 0.3) -> torch.Tensor:
+    """Triplet loss using cosine similarity (for L2-normalized embeddings). margin in [0,1] range."""
+    pos_sim = F.cosine_similarity(anchor, positive)  # higher = more similar
+    neg_sim = F.cosine_similarity(anchor, negative)
+    return F.relu(neg_sim - pos_sim + margin).mean()  # want pos_sim > neg_sim + margin
+
+
+def nt_xent_loss(z_i: torch.Tensor, z_j: torch.Tensor, temperature: float = 0.5) -> torch.Tensor:
+    """Normalized temperature-scaled cross entropy loss (SimCLR style)"""
+    batch_size = z_i.size(0)
+    z = torch.cat([z_i, z_j], dim=0)  # (2N, embed_dim)
+    sim = F.cosine_similarity(z.unsqueeze(1), z.unsqueeze(0), dim=2) / temperature
+    mask = torch.eye(2 * batch_size, dtype=torch.bool, device=z.device)
+    sim.masked_fill_(mask, -float('inf'))
+    labels = torch.arange(batch_size, device=z.device)
+    labels = torch.cat([labels + batch_size, labels])  # positive pairs
+    return F.cross_entropy(sim, labels)
+
+
+# feature extraction utilities - delegating to lib.features for unified implementation
+# these wrappers maintain backwards compatibility for existing imports
+
+def transition_histogram(events: List, state_fn, max_states: int = 50) -> np.ndarray:
+    """Compute normalized histogram of state transitions in trajectory"""
+    return _lib_transition_histogram(events, state_fn, max_states)
+
+
+def temporal_signature(events: List, ts_fn) -> np.ndarray:
+    """Extract temporal features: mean/std/skew of inter-event times"""
+    return _lib_temporal_signature(events, ts_fn)
+
+
+def state_coverage(events: List, state_fn, mdp_states: set) -> float:
+    """Fraction of MDP states visited by trajectory"""
+    return _lib_state_coverage(events, state_fn, mdp_states)
+
+
+def transition_entropy(events: List, state_fn) -> float:
+    """Compute entropy of transition distribution (randomness of navigation)"""
+    return _lib_transition_entropy(events, state_fn)
+
+
+def featurize_trajectory(events: List, mdp: Optional[Dict] = None, input_dim: int = 64) -> np.ndarray:
+    """Convert trajectory to fixed-dim feature vector - uses lib.features implementation"""
+    mdp_states = set(mdp.get('states', [])) if mdp else set()
+
+    def _ts_fn(e):
+        return parse_timestamp(get_timestamp(e))
+
+    def _event_name_fn(e):
+        return get_event_name(e)
+
+    return _lib_featurize_trajectory(events, event_to_state, _ts_fn, _event_name_fn, mdp_states, input_dim)
+
+
+# gradient boosting classifiers for comparison baselines
+class XGBoostAgentClassifier(BaseEstimator, ClassifierMixin):
+    """XGBoost classifier for human/agent detection from session features"""
+    def __init__(self, n_estimators: int = 100, max_depth: int = 6, learning_rate: float = 0.1, **kwargs):
         self.n_estimators = n_estimators
         self.max_depth = max_depth
         self.learning_rate = learning_rate
-        self.early_stopping_rounds = early_stopping_rounds
-        self.model_ = None
-        self.feature_names_ = None
-
-    def _to_array(self, X):
-        """Convert pandas structures to numpy arrays"""
-        return X.values if isinstance(X, (pd.DataFrame, pd.Series)) else X
-
-    def _compute_pos_weight(self, y_arr):
-        """Calculate scale_pos_weight for class imbalance handling"""
-        n_neg, n_pos = (y_arr == 0).sum(), (y_arr == 1).sum()
-        return n_neg / n_pos if n_pos > 0 else 1.0
-
-    def _prepare_eval_set(self, eval_set):
-        """Convert eval_set to numpy arrays if needed"""
-        if not eval_set:
-            return None
-        X_val, y_val = eval_set[0]
-        return [(self._to_array(X_val), self._to_array(y_val))]
-
-    @abstractmethod
-    def _build_model(self, scale_pos: float):
-        """Build the underlying model instance (must be implemented by subclasses)"""
-        pass
-
-    @abstractmethod
-    def _fit_with_eval(self, X_arr, y_arr, eval_arr):
-        """Fit model with evaluation set (must be implemented by subclasses)"""
-        pass
-
-    def fit(self, X, y, eval_set=None):
-        X_arr, y_arr = self._to_array(X), self._to_array(y)
-
-        if isinstance(X, pd.DataFrame):
-            self.feature_names_ = X.columns.tolist()
-
-        scale_pos = self._compute_pos_weight(y_arr)
-        self.model_ = self._build_model(scale_pos)
-
-        eval_arr = self._prepare_eval_set(eval_set)
-        if eval_arr:
-            self._fit_with_eval(X_arr, y_arr, eval_arr)
-        else:
-            self.model_.fit(X_arr, y_arr)
+        self.model = None
+        self.kwargs = kwargs
 
+    def fit(self, X: np.ndarray, y: np.ndarray):
+        try:
+            import xgboost as xgb
+            self.model = xgb.XGBClassifier(n_estimators=self.n_estimators, max_depth=self.max_depth,
+                                           learning_rate=self.learning_rate, **self.kwargs)
+            self.model.fit(X, y)
+        except ImportError:
+            raise ImportError("xgboost required for XGBoostAgentClassifier")
         return self
 
-    def predict(self, X):
-        return self.model_.predict(self._to_array(X))
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        if self.model is None:
+            raise ValueError("fit the model first")
+        return self.model.predict(X)
 
-    def predict_proba(self, X):
-        return self.model_.predict_proba(self._to_array(X))
-
-    @property
-    def feature_importances_(self):
-        return self.model_.feature_importances_ if self.model_ else None
+    def predict_proba(self, X: np.ndarray) -> np.ndarray:
+        if self.model is None:
+            raise ValueError("fit the model first")
+        return self.model.predict_proba(X)
 
 
-class XGBoostAgentClassifier(BaseAgentClassifier):
-    """XGBoost binary classifier for agent detection with class imbalance handling"""
+class LightGBMAgentClassifier(BaseEstimator, ClassifierMixin):
+    """LightGBM classifier for human/agent detection from session features"""
+    def __init__(self, n_estimators: int = 100, max_depth: int = -1, learning_rate: float = 0.1, **kwargs):
+        self.n_estimators = n_estimators
+        self.max_depth = max_depth
+        self.learning_rate = learning_rate
+        self.model = None
+        self.kwargs = kwargs
 
-    def _build_model(self, scale_pos: float):
-        return xgb.XGBClassifier(
-            n_estimators=self.n_estimators,
-            max_depth=self.max_depth,
-            learning_rate=self.learning_rate,
-            scale_pos_weight=scale_pos,
-            eval_metric='auc',
-            early_stopping_rounds=self.early_stopping_rounds,
-            random_state=42,
-            tree_method='hist',
-            enable_categorical=False
-        )
+    def fit(self, X: np.ndarray, y: np.ndarray):
+        try:
+            import lightgbm as lgb
+            self.model = lgb.LGBMClassifier(n_estimators=self.n_estimators, max_depth=self.max_depth,
+                                            learning_rate=self.learning_rate, verbose=-1, **self.kwargs)
+            self.model.fit(X, y)
+        except ImportError:
+            raise ImportError("lightgbm required for LightGBMAgentClassifier")
+        return self
 
-    def _fit_with_eval(self, X_arr, y_arr, eval_arr):
-        self.model_.fit(X_arr, y_arr, eval_set=eval_arr, verbose=False)
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        if self.model is None:
+            raise ValueError("fit the model first")
+        return self.model.predict(X)
 
-
-class LightGBMAgentClassifier(BaseAgentClassifier):
-    """LightGBM binary classifier for agent detection with class imbalance handling"""
-
-    def _build_model(self, scale_pos: float):
-        return lgb.LGBMClassifier(
-            n_estimators=self.n_estimators,
-            max_depth=self.max_depth,
-            learning_rate=self.learning_rate,
-            scale_pos_weight=scale_pos,
-            metric='auc',
-            random_state=42,
-            verbosity=-1
-        )
-
-    def _fit_with_eval(self, X_arr, y_arr, eval_arr):
-        self.model_.fit(
-            X_arr, y_arr,
-            eval_set=eval_arr,
-            callbacks=[lgb.early_stopping(self.early_stopping_rounds, verbose=False)]
-        )
+    def predict_proba(self, X: np.ndarray) -> np.ndarray:
+        if self.model is None:
+            raise ValueError("fit the model first")
+        return self.model.predict_proba(X)

experiments/ml/weak_train.py

@@ -0,0 +1,246 @@
+import sys
+sys.path.insert(0, "/home/velocitatem/Documents/Projects/PHANTOM/sim/rl/behavior_loader")
+sys.path.insert(0, "/home/velocitatem/Documents/Projects/PHANTOM/experiments/ml")
+
+from sim.rl.behavior_loader.loader import AgentLoader, Loader, JointLoader, PayloadModel
+from sim.rl.behavior_loader.models import JointBehaviorModel
+from arch import ContrastiveWeakClassifier, contrastive_loss, featurize_trajectory
+from typing import List, Optional, Dict
+from datetime import datetime, timedelta
+from copy import deepcopy
+import numpy as np
+import random
+import torch
+from torch.utils.data import Dataset, DataLoader
+from torch.optim import Adam
+from torch.utils.tensorboard import SummaryWriter
+
+RUNS_DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/ml/runs"
+agent_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/agents/collected_data/"
+human_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/collected_data/"
+
+
+def _perturb_ts(evt: PayloadModel, jitter_ms: int = 500) -> PayloadModel:
+    """Add random jitter to event timestamp"""
+    new_evt = deepcopy(evt)
+    try:
+        ts = datetime.fromisoformat(evt.ts.replace('Z', '+00:00'))
+        delta = timedelta(milliseconds=random.randint(-jitter_ms, jitter_ms))
+        new_evt.ts = (ts + delta).isoformat()
+    except:
+        pass
+    return new_evt
+
+
+def augment_trajectory(trajectory: List[PayloadModel], rate: float = 0.1) -> List[PayloadModel]:
+    """Apply random augmentation to trajectory for contrastive learning"""
+    if len(trajectory) < 2:
+        return trajectory
+
+    aug_type = random.choice(['window', 'shuffle', 'noise', 'drop'])
+
+    if aug_type == 'window':  # random contiguous sub-sequence (70-100% length)
+        min_len = max(2, int(len(trajectory) * 0.7))
+        sub_len = random.randint(min_len, len(trajectory))
+        start = random.randint(0, len(trajectory) - sub_len)
+        return trajectory[start:start + sub_len]
+
+    elif aug_type == 'shuffle':  # swap adjacent pairs with probability rate
+        result = list(trajectory)
+        for i in range(len(result) - 1):
+            if random.random() < rate:
+                result[i], result[i + 1] = result[i + 1], result[i]
+        return result
+
+    elif aug_type == 'drop':  # drop events with probability rate
+        result = [e for e in trajectory if random.random() > rate]
+        return result if len(result) >= 2 else trajectory[:2]
+
+    elif aug_type == 'noise':  # perturb timestamps
+        return [_perturb_ts(e, jitter_ms=500) for e in trajectory]
+
+    return trajectory
+
+
+class TripletDataset(Dataset):
+    """Generate (anchor, positive, negative) triplets on-the-fly with augmentation"""
+    def __init__(self, data: Dict[str, List[PayloadModel]], mdp: Optional[Dict], augment_fn, input_dim: int = 64, multiplier: int = 10):
+        self.sessions = list(data.items())
+        self.human_ids = [i for i, (sid, _) in enumerate(self.sessions) if sid.startswith('human_')]
+        self.agent_ids = [i for i, (sid, _) in enumerate(self.sessions) if sid.startswith('agent_')]
+        self.mdp = mdp
+        self.augment = augment_fn
+        self.input_dim = input_dim
+        self.multiplier = multiplier
+
+        if not self.human_ids or not self.agent_ids:
+            raise ValueError(f"Need both human ({len(self.human_ids)}) and agent ({len(self.agent_ids)}) sessions")
+
+    def __len__(self) -> int:
+        return len(self.sessions) * self.multiplier
+
+    def __getitem__(self, idx: int):
+        anchor_idx = idx % len(self.sessions)
+        sid, events = self.sessions[anchor_idx]
+        is_human = sid.startswith('human_')
+
+        anchor = featurize_trajectory(events, self.mdp, self.input_dim)
+        positive = featurize_trajectory(self.augment(events), self.mdp, self.input_dim)
+
+        neg_pool = self.agent_ids if is_human else self.human_ids
+        neg_idx = random.choice(neg_pool)
+        negative = featurize_trajectory(self.sessions[neg_idx][1], self.mdp, self.input_dim)
+
+        label = 0 if is_human else 1  # 0=human, 1=agent
+        return (torch.tensor(anchor, dtype=torch.float32),
+                torch.tensor(positive, dtype=torch.float32),
+                torch.tensor(negative, dtype=torch.float32),
+                torch.tensor(label, dtype=torch.long))
+
+
+def train(epochs: int = 100, lr: float = 1e-3, batch_size: int = 4, input_dim: int = 64,
+          embed_dim: int = 32, margin: float = 0.3, verbose: bool = True, run_name: str = None):
+    """Train contrastive weak classifier on human/agent trajectories"""
+    joint = JointLoader(human_dir, agent_dir)
+    data = joint.get_data()
+    if verbose:
+        print(f"Loaded {len(data)} sessions")
+
+    joint_model = JointBehaviorModel(human_dir, agent_dir)
+    ref_mdp = joint_model.build_MDP()
+
+    dataset = TripletDataset(data, ref_mdp, augment_trajectory, input_dim=input_dim)
+    loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True)
+
+    model = ContrastiveWeakClassifier(input_dim=input_dim, embed_dim=embed_dim, margin=margin)
+    model.to_device()
+
+    run_name = run_name or f"d{input_dim}_e{embed_dim}_lr{lr}_m{margin}_{datetime.now():%Y%m%d_%H%M%S}"
+    writer = SummaryWriter(f"{RUNS_DIR}/train/{run_name}")
+
+    optimizer = Adam(list(model.encoder.parameters()) + list(model.classifier.parameters()), lr=lr)
+    ce_loss_fn = torch.nn.CrossEntropyLoss()
+
+    best_loss = float('inf')
+    for epoch in range(epochs):
+        model.encoder.train()
+        model.classifier.train()
+        total_loss, n_batches = 0.0, 0
+
+        for anchor, positive, negative, labels in loader:
+            anchor, positive, negative, labels = [t.to(model.device) for t in [anchor, positive, negative, labels]]
+            z_a, z_p, z_n = [model.encoder(t.unsqueeze(1)) for t in [anchor, positive, negative]]
+
+            trip_loss = contrastive_loss(z_a, z_p, z_n, margin=model.margin)
+            ce = ce_loss_fn(model.classifier(z_a), labels)
+            loss = trip_loss + 0.5 * ce
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            total_loss += loss.item()
+            n_batches += 1
+
+        avg_loss = total_loss / max(n_batches, 1)
+        writer.add_scalar('loss', avg_loss, epoch)
+
+        if verbose and (epoch + 1) % 10 == 0:
+            print(f"Epoch {epoch+1}/{epochs}: loss={avg_loss:.4f}")
+        if avg_loss < best_loss:
+            best_loss = avg_loss
+
+    writer.close()
+    if verbose:
+        print(f"Done. Best={best_loss:.4f} TB:{RUNS_DIR}/train/{run_name}")
+
+    return model, ref_mdp
+
+
+def evaluate_loocv(input_dim: int = 64, embed_dim: int = 32, epochs_per_fold: int = 50,
+                   lr: float = 1e-3, margin: float = 0.3, run_name: str = None):
+    """Leave-one-out cross-validation given limited samples"""
+    joint = JointLoader(human_dir, agent_dir)
+    data = joint.get_data()
+    session_ids = list(data.keys())
+
+    joint_model = JointBehaviorModel(human_dir, agent_dir)
+    ref_mdp = joint_model.build_MDP()
+
+    run_name = run_name or f"loocv_d{input_dim}_e{embed_dim}_m{margin}_{datetime.now():%Y%m%d_%H%M%S}"
+    writer = SummaryWriter(f"{RUNS_DIR}/eval/{run_name}")
+
+    predictions, actuals = [], []
+
+    for fold_idx, test_sid in enumerate(session_ids):
+        train_data = {k: v for k, v in data.items() if k != test_sid}
+        test_events = data[test_sid]
+        test_label = 0 if test_sid.startswith('human_') else 1
+
+        n_human = sum(1 for k in train_data if k.startswith('human_'))
+        n_agent = sum(1 for k in train_data if k.startswith('agent_'))
+        if n_human == 0 or n_agent == 0:
+            continue
+
+        try:
+            dataset = TripletDataset(train_data, ref_mdp, augment_trajectory, input_dim=input_dim, multiplier=5)
+            loader = DataLoader(dataset, batch_size=2, shuffle=True, drop_last=True)
+
+            model = ContrastiveWeakClassifier(input_dim=input_dim, embed_dim=embed_dim, margin=margin)
+            model.to_device()
+            optimizer = Adam(list(model.encoder.parameters()) + list(model.classifier.parameters()), lr=lr)
+
+            model.encoder.train()
+            model.classifier.train()
+            for _ in range(epochs_per_fold):
+                for anchor, positive, negative, labels in loader:
+                    z_a, z_p, z_n = [model.encoder(t.unsqueeze(1).to(model.device)) for t in [anchor, positive, negative]]
+                    loss = contrastive_loss(z_a, z_p, z_n, margin=margin)
+                    optimizer.zero_grad()
+                    loss.backward()
+                    optimizer.step()
+
+            test_feat = featurize_trajectory(test_events, ref_mdp, input_dim)
+            pred = model.predict(test_feat.reshape(1, -1))[0]
+            predictions.append(pred)
+            actuals.append(test_label)
+            print(f"  {test_sid[:12]}...: pred={pred}, actual={test_label}, {'OK' if pred == test_label else 'MISS'}")
+
+        except Exception as e:
+            print(f"Error: {e}")
+
+    if predictions:
+        acc = sum(p == a for p, a in zip(predictions, actuals)) / len(predictions)
+        tp = sum(1 for p, a in zip(predictions, actuals) if p == 1 and a == 1)
+        fp = sum(1 for p, a in zip(predictions, actuals) if p == 1 and a == 0)
+        fn = sum(1 for p, a in zip(predictions, actuals) if p == 0 and a == 1)
+        prec, rec = tp / max(tp + fp, 1), tp / max(tp + fn, 1)
+        f1 = 2 * prec * rec / max(prec + rec, 1e-10)
+        writer.add_scalar('accuracy', acc, 0)
+        writer.add_scalar('f1', f1, 0)
+        writer.add_scalar('precision', prec, 0)
+        writer.add_scalar('recall', rec, 0)
+        writer.close()
+        print(f"\nAccuracy: {acc:.2%} F1: {f1:.3f} TB:{RUNS_DIR}/eval/{run_name}")
+        return acc, predictions, actuals
+    writer.close()
+    return 0.0, [], []
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--mode', choices=['train', 'eval'], default='train')
+    parser.add_argument('--epochs', type=int, default=100)
+    parser.add_argument('--lr', type=float, default=1e-3)
+    parser.add_argument('--margin', type=float, default=0.3)
+    parser.add_argument('--input-dim', type=int, default=64)
+    parser.add_argument('--embed-dim', type=int, default=32)
+    parser.add_argument('--run-name', type=str, default=None)
+    args = parser.parse_args()
+
+    if args.mode == 'train':
+        model, mdp = train(epochs=args.epochs, lr=args.lr, input_dim=args.input_dim,
+                           embed_dim=args.embed_dim, margin=args.margin, run_name=args.run_name)
+    else:
+        evaluate_loocv(input_dim=args.input_dim, embed_dim=args.embed_dim, epochs_per_fold=args.epochs,
+                       lr=args.lr, margin=args.margin, run_name=args.run_name)

experiments/procesing/contaminator.py

@@ -0,0 +1,114 @@
+from __future__ import annotations
+
+import os
+import random
+from pathlib import Path
+from types import SimpleNamespace
+
+import pandas as pd
+
+from lib.separability import estimate_alpha, load_artifacts, score_session
+
+
+# use relative import when in package context, fallback for standalone
+try:
+    from sim.rl.behavior_loader.models import AgentBehaviorModel
+except ImportError:
+    import sys
+    sys.path.insert(0, str(Path(__file__).parent.parent.parent / "sim" / "rl" / "behavior_loader"))
+    from models import AgentBehaviorModel
+
+# paths should be configurable via environment or relative to project root
+PROJECT_ROOT = Path(__file__).parent.parent.parent
+AGENT_DATA_DIR = Path(os.getenv('PHANTOM_AGENT_DATA_DIR', PROJECT_ROOT / "experiments" / "agents" / "collected_data"))
+
+try:
+    SEPARABILITY_ARTIFACTS = load_artifacts()
+except FileNotFoundError:
+    SEPARABILITY_ARTIFACTS = None
+
+
+def remap_schema(df: pd.DataFrame, mapping: dict, on: str = "event_type") -> pd.DataFrame:
+    """remap column values according to mapping dict, preserving unmapped values"""
+    df = df.copy()
+    df[on] = df[on].map(mapping).fillna(df[on])
+    return df
+
+
+def _states_to_events(states: list[str]) -> list[SimpleNamespace]:
+    events: list[SimpleNamespace] = []
+    for idx, state in enumerate(states):
+        parts = state.split("|") if isinstance(state, str) else ["page", "product", str(state)]
+        page = f"/{parts[0]}" if parts else "/"
+        product = parts[1] if len(parts) > 1 else "unknown"
+        event_name = parts[2] if len(parts) > 2 else parts[-1]
+        events.append(
+            SimpleNamespace(
+                eventName=event_name,
+                page=page,
+                productId=product,
+                ts=float(idx),
+            )
+        )
+    return events
+
+def contaminate_dataset(df: pd.DataFrame, on: str = "event_type",
+                        contamination_rate: float = 0.1,
+                        agent_data_dir: Path = None) -> pd.DataFrame:
+    """inject synthetic agent trajectories into a dataset
+    contamination_rate: fraction of final dataset that should be agent data (0.1 = 10% agents)
+    """
+    data_dir = agent_data_dir or AGENT_DATA_DIR
+    model = AgentBehaviorModel(str(data_dir))
+    model.build_MDP()  # ensure MDP is built before sampling
+
+    # compute event distribution from original data
+    event_dist = df[on].value_counts(normalize=True).to_dict()
+    total = sum(event_dist.values())
+    event_dist = {k: v / total for k, v in event_dist.items()}
+
+    # calculate how many synthetic events to add
+    N = len(df)
+    N_final = N / (1 - contamination_rate)
+    N_contaminate = int(N_final - N)
+
+    # sample start states weighted by original distribution
+    start_events = random.choices(list(event_dist.keys()), weights=list(event_dist.values()), k=N_contaminate)
+
+    # generate synthetic trajectories
+    new_rows = []
+    alpha_estimates = []
+
+    for start_event in start_events:
+        # sample trajectory from agent model, using a state that contains the event type
+        mdp_states = model.mdp.get('states', []) if model.mdp else []
+        matching_starts = [s for s in mdp_states if start_event in s]
+        if not matching_starts:
+            continue  # skip if no matching start state
+        start_state = random.choice(matching_starts)
+        trajectory = model.sample_traj(start_state, max_len=20)
+        score_payload: list[SimpleNamespace] = []
+        score: dict[str, float] = {}
+        if SEPARABILITY_ARTIFACTS:
+            score_payload = _states_to_events(trajectory)
+            score = score_session(score_payload, SEPARABILITY_ARTIFACTS)
+            alpha_estimates.append(
+                estimate_alpha(score["prob_agent"], score["delta_h"], score["delta_a"], temperature=2.0)
+            )
+
+        for state in trajectory:
+            parts = state.split('|') if isinstance(state, str) else [start_event]
+            new_rows.append({
+                on: parts[-1] if parts else start_event,
+                'source': 'synthetic_agent',
+                'prob_agent': score.get('prob_agent') if SEPARABILITY_ARTIFACTS and score_payload else None,
+                'delta_h': score.get('delta_h') if SEPARABILITY_ARTIFACTS and score_payload else None,
+                'delta_a': score.get('delta_a') if SEPARABILITY_ARTIFACTS and score_payload else None,
+            })
+
+    if new_rows:
+        contaminate_df = pd.DataFrame(new_rows)
+        df = pd.concat([df, contaminate_df], ignore_index=True)
+        if alpha_estimates:
+            df['estimated_alpha'] = sum(alpha_estimates) / len(alpha_estimates)
+    return df

experiments/procesing/pricers/base.py

@@ -7,15 +7,6 @@ import pandas as pd
 class PricingFunction(ABC):
     """
     Abstract base for pricing functions.
-
-    Defines mapping: f(Q_t, P_t, S_t, H_t) -> P_{t+1}
-
-    Where:
-        Q_t ∈ R^n: demand vector at time t
-        P_t ∈ R^n: price vector at time t
-        S_t: session features (behavioral signals, interactions)
-        H_t = {Q_{t-k}, P_{t-k}, S_{t-k}}: historical state trajectory
-
     Objective:
         maximize E[R_T] = E[Σ P_t^T · Q_t]
         subject to:
@@ -28,10 +19,10 @@ class PricingFunction(ABC):
     def fit(self, *kwargs):
         """
         Offline training on historical data.
+        This is where we can think about some maximization of expected revenue
+        over historical trajectories to learn parameters of the pricing function.
+        (This however we cover move in the RL side of things)
 
-        Args:
-            historical_data: DataFrame with elasticity, prices, demand signals
-            **kwargs: additional training parameters
         """
         pass
 
@@ -39,12 +30,18 @@ class PricingFunction(ABC):
     def predict(self, *kwargs) -> np.ndarray:
         """
         Generate optimal prices given current state.
+        This is an abstract method that transitions from τ -> P*
+        which is the mapping from the trajectory to optimal prices under
+        some subset of session grouping (so, per sessionId)
+        """
+        pass
 
-        Args:
-            state_space: StateSpace object containing Q_t, P_t, S_t, H_t
-
+    @abstractmethod
+    def _get_features(self, *kwargs) -> np.ndarray:
+        """
+        Extract features from trajectory for pricing decision.
         Returns:
-            P_{t+1}: price vector in R^n
+            np.ndarray of shape (n_products, n_features)
         """
         pass
 

experiments/procesing/pricers/elasticity.py

@@ -57,3 +57,13 @@ class ElasticityBasedPricer(PricingFunction):
         # enforce bounds
         prices = np.clip(prices, self.price_floor, self.price_ceil)
         return prices
+
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Extract elasticity, demand, and demand deviation for each product"""
+        if state_space is None or self.elasticity is None:
+            n = len(self.elasticity) if self.elasticity is not None else 0
+            return np.zeros((n, 3))
+
+        demand = np.asarray(state_space.demand)
+        demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
+        return np.column_stack([self.elasticity, demand, demand_dev])

experiments/procesing/pricers/session_aware.py

@@ -107,6 +107,36 @@ class SessionAwarePricer(PricingFunction):
 
         return prices
 
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Extract elasticity, demand, and session features"""
+        if state_space is None or self.elasticity is None:
+            n = len(self.elasticity) if self.elasticity is not None else 0
+            return np.zeros((n, 5))
+
+        demand = np.asarray(state_space.demand)
+        n_products = len(demand)
+
+        # extract session features
+        velocity = 0.0
+        view_depth = 0.0
+        cart_to_view = 0.0
+
+        if not state_space.session_features.empty:
+            sf = state_space.session_features.iloc[0]
+            velocity = sf.get('interaction_velocity', 0.0)
+            view_depth = sf.get('product_view_depth', 0.0)
+            cart_to_view = sf.get('cart_to_view_ratio', 0.0)
+
+        # broadcast session features to all products
+        features = np.column_stack([
+            self.elasticity,
+            demand,
+            np.full(n_products, velocity),
+            np.full(n_products, view_depth),
+            np.full(n_products, cart_to_view)
+        ])
+        return features
+
 
 class ProductSpecificSessionPricer(PricingFunction):
     """
@@ -170,3 +200,12 @@ class ProductSpecificSessionPricer(PricingFunction):
 
         prices = np.clip(base_prices, self.price_floor, self.price_ceil)
         return prices
+
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Extract elasticity and demand features for product-specific pricing"""
+        if state_space is None or self.elasticity is None:
+            n = len(self.elasticity) if self.elasticity is not None else 0
+            return np.zeros((n, 2))
+
+        demand = np.asarray(state_space.demand)
+        return np.column_stack([self.elasticity, demand])

experiments/procesing/pricers/simple.py

@@ -65,6 +65,11 @@ class StaticPricer(PricingFunction):
             raise ValueError("Must call fit() or provide base_prices in constructor")
         return self.base_prices.copy()
 
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Static pricer uses no features, returns empty array"""
+        n = len(self.base_prices) if self.base_prices is not None else 0
+        return np.zeros((n, 0))
+
 
 class RandomPricer(PricingFunction):
     """Random pricing within bounds (for baseline comparison)"""
@@ -87,6 +92,11 @@ class RandomPricer(PricingFunction):
             self.n_products = len(state_space.demand)
         return self.rng.uniform(self.price_min, self.price_max, size=self.n_products)
 
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Random pricer uses no features"""
+        n = self.n_products if self.n_products else 0
+        return np.zeros((n, 0))
+
 
 class SimpleSurgePricer(PricingFunction):
     """
@@ -133,3 +143,16 @@ class SimpleSurgePricer(PricingFunction):
         new_prices[low_mask] *= self.discount_multiplier
 
         return new_prices
+
+    def _get_features(self, state_space=None) -> np.ndarray:
+        """Extract demand and base price features for each product"""
+        if state_space is None:
+            n = len(self.base_prices) if self.base_prices is not None else 0
+            return np.zeros((n, 2))
+
+        demand = np.asarray(state_space.demand) if hasattr(state_space, 'demand') else np.array([0])
+        base = np.asarray(state_space.prices) if hasattr(state_space, 'prices') else self.base_prices
+        if base is None:
+            base = np.ones(len(demand)) * 99.99
+
+        return np.column_stack([demand, base])

experiments/procesing/tests/test_demand.py

@@ -6,6 +6,7 @@ from procesing.steps import (
 )
 
 def test_compute_demand(pipeline_context):
+    random.seed(42)  # deterministic test
     step = ComputeDemandStep(context=pipeline_context)
 
     # Test with normal interaction data
@@ -26,6 +27,7 @@ def test_compute_demand(pipeline_context):
 
 
 def test_compute_demand_skewed(pipeline_context):
+    random.seed(42)  # deterministic test
     step = ComputeDemandStep(context=pipeline_context)
 
     # Test with normal interaction data

lib/__init__.py

@@ -0,0 +1,41 @@
+"""PHANTOM shared library
+Exports unified utilities for features, state, config, kafka, and model registry
+"""
+from .config import (
+    PROJECT_ROOT, DATA_DIR, EXPERIMENTS_DIR,
+    AGENT_DATA_DIR, HUMAN_DATA_DIR, SIM_RUNS_DIR, MODEL_REGISTRY_DIR,
+    COLLECTED_DATA_DIR, NOTEBOOK_OUTPUT_DIR,
+    ensure_dir, get_data_path, get_experiments_path, get_sim_path,
+    KAFKA_HOST, KAFKA_PORT, KAFKA_BROKER,
+    REDIS_HOST, REDIS_PORT,
+    SUPABASE_URL, SUPABASE_ANON_KEY,
+    BACKEND_PORT, PROVIDER_PORT
+)
+from .state import (
+    make_state_repr, event_to_state, parse_state,
+    get_event_name, get_timestamp,
+    create_state_fn, create_event_name_fn, create_timestamp_fn
+)
+from .features import (
+    transition_histogram, temporal_signature, state_coverage, transition_entropy,
+    event_type_distribution, featurize_trajectory, parse_timestamp
+)
+
+__all__ = [
+    # config
+    'PROJECT_ROOT', 'DATA_DIR', 'EXPERIMENTS_DIR',
+    'AGENT_DATA_DIR', 'HUMAN_DATA_DIR', 'SIM_RUNS_DIR', 'MODEL_REGISTRY_DIR',
+    'COLLECTED_DATA_DIR', 'NOTEBOOK_OUTPUT_DIR',
+    'ensure_dir', 'get_data_path', 'get_experiments_path', 'get_sim_path',
+    'KAFKA_HOST', 'KAFKA_PORT', 'KAFKA_BROKER',
+    'REDIS_HOST', 'REDIS_PORT',
+    'SUPABASE_URL', 'SUPABASE_ANON_KEY',
+    'BACKEND_PORT', 'PROVIDER_PORT',
+    # state
+    'make_state_repr', 'event_to_state', 'parse_state',
+    'get_event_name', 'get_timestamp',
+    'create_state_fn', 'create_event_name_fn', 'create_timestamp_fn',
+    # features
+    'transition_histogram', 'temporal_signature', 'state_coverage', 'transition_entropy',
+    'event_type_distribution', 'featurize_trajectory', 'parse_timestamp',
+]

lib/config.py

@@ -0,0 +1,65 @@
+"""Unified path configuration for PHANTOM project
+All hardcoded paths should reference this module
+Paths can be overridden via environment variables
+"""
+import os
+from pathlib import Path
+
+# project root (directory containing lib/, experiments/, sim/, web/, backend/)
+PROJECT_ROOT = Path(__file__).parent.parent.resolve()
+
+# data directories
+DATA_DIR = Path(os.getenv('PHANTOM_DATA_DIR', PROJECT_ROOT / 'data'))
+EXPERIMENTS_DIR = Path(os.getenv('PHANTOM_EXPERIMENTS_DIR', PROJECT_ROOT / 'experiments'))
+
+# agent/human interaction data
+AGENT_DATA_DIR = Path(os.getenv('PHANTOM_AGENT_DATA_DIR', DATA_DIR / 'agents'))
+HUMAN_DATA_DIR = Path(os.getenv('PHANTOM_HUMAN_DATA_DIR', DATA_DIR / 'humans'))
+
+# RL simulation runs
+SIM_RUNS_DIR = Path(os.getenv('PHANTOM_SIM_RUNS_DIR', PROJECT_ROOT / 'sim' / 'rl' / 'runs'))
+
+# model artifacts
+MODEL_REGISTRY_DIR = Path(os.getenv('PHANTOM_MODEL_REGISTRY_DIR', DATA_DIR / 'models'))
+
+# collected experiment data
+COLLECTED_DATA_DIR = Path(os.getenv('PHANTOM_COLLECTED_DATA_DIR', EXPERIMENTS_DIR / 'agents' / 'collected_data'))
+
+# notebook outputs
+NOTEBOOK_OUTPUT_DIR = Path(os.getenv('PHANTOM_NOTEBOOK_OUTPUT_DIR', EXPERIMENTS_DIR / 'notebooks' / 'outputs'))
+
+
+def ensure_dir(path: Path) -> Path:
+    """ensure directory exists, create if needed"""
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def get_data_path(*parts: str) -> Path:
+    """construct path relative to DATA_DIR"""
+    return DATA_DIR.joinpath(*parts)
+
+
+def get_experiments_path(*parts: str) -> Path:
+    """construct path relative to EXPERIMENTS_DIR"""
+    return EXPERIMENTS_DIR.joinpath(*parts)
+
+
+def get_sim_path(*parts: str) -> Path:
+    """construct path relative to SIM_RUNS_DIR"""
+    return SIM_RUNS_DIR.joinpath(*parts)
+
+
+# service configuration (from .env)
+KAFKA_HOST = os.getenv('KAFKA_HOST', 'localhost')
+KAFKA_PORT = os.getenv('KAFKA_PORT', '9092')
+KAFKA_BROKER = f"{KAFKA_HOST}:{KAFKA_PORT}"
+
+REDIS_HOST = os.getenv('REDIS_HOST', 'localhost')
+REDIS_PORT = int(os.getenv('REDIS_PORT', '6379'))
+
+SUPABASE_URL = os.getenv('NEXT_PUBLIC_SUPABASE_URL', '')
+SUPABASE_ANON_KEY = os.getenv('NEXT_PUBLIC_SUPABASE_ANON_KEY', '')
+
+BACKEND_PORT = int(os.getenv('BACKEND_PORT', '5000'))
+PROVIDER_PORT = int(os.getenv('PROVIDER_PORT', '5001'))

lib/features.py

@@ -0,0 +1,125 @@
+"""Unified featurization utilities for trajectory -> feature vector conversion
+Used by both experiments/ml/ and sim/rl/ components
+"""
+import numpy as np
+from collections import defaultdict
+from typing import List, Dict, Callable, Optional, Any, Set
+from datetime import datetime
+
+
+def transition_histogram(events: List, state_fn: Callable, max_states: int = 50) -> np.ndarray:
+    """compute normalized histogram of state transitions in trajectory
+    events: list of event objects/dicts
+    state_fn: function mapping event -> state string
+    max_states: maximum dimensions for histogram
+    """
+    if len(events) < 2:
+        return np.zeros(max_states, dtype=np.float32)
+    states = [state_fn(e) for e in events]
+    trans_counts = defaultdict(int)
+    for s, s_next in zip(states, states[1:]):
+        trans_counts[(s, s_next)] += 1
+    total = sum(trans_counts.values())
+    hist = np.array(list(trans_counts.values())[:max_states], dtype=np.float32)
+    hist = np.pad(hist, (0, max(0, max_states - len(hist))))
+    return hist / (total + 1e-10)
+
+
+def temporal_signature(events: List, ts_fn: Callable) -> np.ndarray:
+    """extract temporal features: mean/std/skew of inter-event times plus count
+    events: list of event objects/dicts
+    ts_fn: function mapping event -> timestamp (float seconds)
+    returns: [mean_dt, std_dt, skew, n_intervals] array
+    """
+    if len(events) < 2:
+        return np.zeros(4, dtype=np.float32)
+    times = sorted([ts_fn(e) for e in events])
+    diffs = np.diff(times).astype(np.float32)
+    if len(diffs) == 0:
+        return np.zeros(4, dtype=np.float32)
+    mean_dt, std_dt = np.mean(diffs), np.std(diffs) + 1e-10
+    skew = np.mean(((diffs - mean_dt) / std_dt) ** 3) if std_dt > 1e-8 else 0.0
+    return np.array([mean_dt, std_dt, skew, len(diffs)], dtype=np.float32)
+
+
+def state_coverage(events: List, state_fn: Callable, mdp_states: Set[str]) -> float:
+    """fraction of MDP states visited by trajectory
+    events: list of event objects/dicts
+    state_fn: function mapping event -> state string
+    mdp_states: set of all possible MDP states
+    """
+    if not mdp_states:
+        return 0.0
+    visited = set(state_fn(e) for e in events)
+    return len(visited & mdp_states) / len(mdp_states)
+
+
+def transition_entropy(events: List, state_fn: Callable) -> float:
+    """compute entropy of transition distribution (randomness of navigation)
+    higher entropy = more random browsing pattern
+    """
+    if len(events) < 2:
+        return 0.0
+    states = [state_fn(e) for e in events]
+    trans_counts = defaultdict(int)
+    for s, s_next in zip(states, states[1:]):
+        trans_counts[(s, s_next)] += 1
+    total = sum(trans_counts.values())
+    probs = [c / total for c in trans_counts.values()]
+    return -sum(p * np.log(p + 1e-10) for p in probs)
+
+
+def event_type_distribution(events: List, event_name_fn: Callable) -> np.ndarray:
+    """compute proportions of different event type categories
+    returns: [page_view_ratio, hover_ratio, cart_ratio, purchase_ratio]
+    """
+    if not events:
+        return np.zeros(4, dtype=np.float32)
+    n = len(events)
+    names = [event_name_fn(e).lower() for e in events]
+    return np.array([
+        sum(1 for nm in names if 'page' in nm or 'view' in nm) / n,
+        sum(1 for nm in names if 'hover' in nm) / n,
+        sum(1 for nm in names if 'cart' in nm) / n,
+        sum(1 for nm in names if 'purchase' in nm or 'checkout' in nm) / n
+    ], dtype=np.float32)
+
+
+def featurize_trajectory(events: List, state_fn: Callable, ts_fn: Callable,
+                         event_name_fn: Callable, mdp_states: Optional[Set[str]] = None,
+                         output_dim: int = 64) -> np.ndarray:
+    """convert trajectory to fixed-dimension feature vector
+    events: list of event objects/dicts
+    state_fn: function mapping event -> state string
+    ts_fn: function mapping event -> timestamp (float)
+    event_name_fn: function mapping event -> event name string
+    mdp_states: optional set of all MDP states for coverage calculation
+    output_dim: desired output dimension (will pad/truncate)
+    """
+    feats = []
+    feats.extend(transition_histogram(events, state_fn, max_states=40))  # 40 dims
+    feats.extend(temporal_signature(events, ts_fn))  # 4 dims
+    feats.append(state_coverage(events, state_fn, mdp_states or set()))  # 1 dim
+    feats.append(transition_entropy(events, state_fn))  # 1 dim
+    feats.append(float(len(events)))  # trajectory length
+    feats.append(float(len(set(state_fn(e) for e in events))))  # unique states
+    feats.extend(event_type_distribution(events, event_name_fn))  # 4 dims
+
+    feats = np.array(feats[:output_dim], dtype=np.float32)
+    if len(feats) < output_dim:
+        feats = np.pad(feats, (0, output_dim - len(feats)))
+    return feats
+
+
+def parse_timestamp(ts: Any) -> float:
+    """parse various timestamp formats to float seconds"""
+    if ts is None:
+        return 0.0
+    if isinstance(ts, (int, float)):
+        return float(ts)
+    if isinstance(ts, str):
+        try:
+            return datetime.fromisoformat(ts.replace('Z', '+00:00')).timestamp()
+        except ValueError:
+            return 0.0
+    return 0.0

lib/kafka_client.py

@@ -0,0 +1,54 @@
+from kafka import KafkaConsumer
+import json
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+def get_interactions(
+    topic='user-interactions',
+    bootstrap_servers=None,
+    from_beginning=True,
+    max_records=None,
+    timeout_ms=5000
+):
+    """Consume interaction events from Kafka.
+
+    Args:
+        topic: Kafka topic name
+        bootstrap_servers: Kafka broker address (default from env)
+        from_beginning: Start from earliest offset if True
+        max_records: Max number of records to fetch (None = all available)
+        timeout_ms: Consumer poll timeout
+
+    Returns:
+        List of parsed interaction event dicts
+    """
+    if not bootstrap_servers:
+        host = os.getenv('KAFKA_HOST', 'localhost')
+        port = os.getenv('KAFKA_PORT', '9092')
+        bootstrap_servers = f'{host}:{port}'
+
+    consumer = KafkaConsumer(
+        topic,
+        bootstrap_servers=bootstrap_servers,
+        auto_offset_reset='earliest' if from_beginning else 'latest',
+        enable_auto_commit=False,
+        value_deserializer=lambda m: json.loads(m.decode('utf-8')),
+        consumer_timeout_ms=timeout_ms
+    )
+
+    events = []
+    try:
+        for msg in consumer:
+            events.append(msg.value)
+            if max_records and len(events) >= max_records:
+                break
+    finally:
+        consumer.close()
+
+    return events
+
+if __name__ == '__main__':
+    interactions = get_interactions(max_records=10)
+    for event in interactions:
+        print(event)

lib/state.py

@@ -0,0 +1,72 @@
+"""Unified state representation utilities for MDP state encoding
+Used by both experiments/ and sim/ components for consistent state handling
+"""
+from typing import Any, Callable
+
+
+def make_state_repr(page: str = None, product_id: str = None, event_name: str = None) -> str:
+    """create canonical state representation string from components
+    format: page|productId|eventName
+    """
+    p = page or 'unk'
+    pid = product_id or 'none'
+    en = event_name or 'unknown'
+    return f"{p}|{pid}|{en}"
+
+
+def event_to_state(evt: Any) -> str:
+    """convert event object/dict to state string
+    supports both object attributes and dict keys
+    """
+    if isinstance(evt, dict):
+        return make_state_repr(
+            page=evt.get('page'),
+            product_id=evt.get('productId'),
+            event_name=evt.get('eventName') or evt.get('event_type')
+        )
+    return make_state_repr(
+        page=getattr(evt, 'page', None),
+        product_id=getattr(evt, 'productId', None),
+        event_name=getattr(evt, 'eventName', None) or getattr(evt, 'event_type', None)
+    )
+
+
+def parse_state(state_str: str) -> dict:
+    """parse state string back to components
+    returns: {'page': str, 'productId': str, 'eventName': str}
+    """
+    parts = state_str.split('|')
+    return {
+        'page': parts[0] if len(parts) > 0 and parts[0] != 'unk' else None,
+        'productId': parts[1] if len(parts) > 1 and parts[1] != 'none' else None,
+        'eventName': parts[2] if len(parts) > 2 and parts[2] != 'unknown' else None
+    }
+
+
+def get_event_name(evt: Any) -> str:
+    """extract event name from event object/dict"""
+    if isinstance(evt, dict):
+        return evt.get('eventName') or evt.get('event_type') or ''
+    return getattr(evt, 'eventName', None) or getattr(evt, 'event_type', None) or ''
+
+
+def get_timestamp(evt: Any) -> Any:
+    """extract timestamp from event object/dict"""
+    if isinstance(evt, dict):
+        return evt.get('ts') or evt.get('timestamp')
+    return getattr(evt, 'ts', None) or getattr(evt, 'timestamp', None)
+
+
+def create_state_fn() -> Callable:
+    """factory for state representation function"""
+    return event_to_state
+
+
+def create_event_name_fn() -> Callable:
+    """factory for event name extraction function"""
+    return get_event_name
+
+
+def create_timestamp_fn() -> Callable:
+    """factory for timestamp extraction function (returns raw value, use features.parse_timestamp to convert)"""
+    return get_timestamp

paper/.latexmkrc

@@ -1,8 +1,6 @@
 $pdf_mode = 1;
 $pdflatex = 'pdflatex -synctex=1 -interaction=nonstopmode -file-line-error %O %S';
-$aux_dir = 'build';
-$out_dir = 'build';
-$use_biber = 0;                        # force bibtex
+$bibtex_use = 2;                       # run bibtex when needed
 $bibtex   = 'bibtex %O %B';
 $pdf_previewer = 'zathura %O %S';
 $clean_ext = 'synctex.gz bbl bcf run.xml fls fdb_latexmk glg glo gls ist blg lof lot out toc';

paper/concat_code.sh

@@ -43,22 +43,22 @@ EOF
 echo "Concatenating code from source directories..."
 
 # Backend
-find "$PROJECT_ROOT/backend" -type f \( -name "*.py" -o -name "*.js" -o -name "*.sh" -o -name "*.yml" -o -name "*.yaml" \) | sort | while read -r file; do
+find "$PROJECT_ROOT/backend" -type d \( -name ".venv" -o -name "__pycache__" -o -name "*.egg-info" -o -name "node_modules" -o -name ".pytest_cache" \) -prune -o -type f \( -name "*.py" -o -name "*.js" -o -name "*.sh" -o -name "*.yml" -o -name "*.yaml" \) ! -name "*.pyc" ! -name "*.pyo" -print | sort | while read -r file; do
     add_file "$file"
 done
 
 # Experiments
-find "$PROJECT_ROOT/experiments" -type f \( -name "*.py" -o -name "*.js" -o -name "*.sh" -o -name "*.yml" -o -name "*.yaml" \) | sort | while read -r file; do
+find "$PROJECT_ROOT/experiments" -type d \( -name ".venv" -o -name "__pycache__" -o -name "*.egg-info" -o -name "node_modules" -o -name ".pytest_cache" -o -name ".ipynb_checkpoints" \) -prune -o -type f \( -name "*.py" -o -name "*.js" -o -name "*.sh" -o -name "*.yml" -o -name "*.yaml" \) ! -name "*.pyc" ! -name "*.pyo" -print | sort | while read -r file; do
     add_file "$file"
 done
 
 # Docker
-find "$PROJECT_ROOT/docker" -type f \( -name "*.py" -o -name "*.sh" -o -name "*.yml" -o -name "*.yaml" -o -name "Dockerfile*" \) | sort | while read -r file; do
+find "$PROJECT_ROOT/docker" -type d \( -name ".venv" -o -name "__pycache__" -o -name "node_modules" \) -prune -o -type f \( -name "*.py" -o -name "*.sh" -o -name "*.yml" -o -name "*.yaml" -o -name "Dockerfile*" \) ! -name "*.pyc" ! -name "*.pyo" -print | sort | while read -r file; do
     add_file "$file"
 done
 
 # Web/src
-find "$PROJECT_ROOT/web/src" -type f \( -name "*.js" -o -name "*.jsx" -o -name "*.ts" -o -name "*.tsx" \) | sort | while read -r file; do
+find "$PROJECT_ROOT/web/src" -type d \( -name "node_modules" -o -name ".next" -o -name "dist" -o -name "build" \) -prune -o -type f \( -name "*.js" -o -name "*.jsx" -o -name "*.ts" -o -name "*.tsx" \) -print | sort | while read -r file; do
     add_file "$file"
 done
 

paper/src/auto/main.el

@@ -6,7 +6,7 @@
    (setq TeX-command-extra-options
          "-file-line-error -interaction=nonstopmode")
    (TeX-add-to-alist 'LaTeX-provided-class-options
-                     '(("report" "12pt") ("article" "12pt") ("acmart" "sigconf" "nonacm" "natbib=false")))
+                     '(("report" "12pt") ("acmart" "sigconf" "nonacm" "natbib=false" "manuscript") ("article" "12pt" "letterpaper")))
    (TeX-run-style-hooks
     "latex2e"
     "preamble"
@@ -17,8 +17,7 @@
     "chapters/05-discussion"
     "chapters/06-conclusion"
     "../build/concatenated_code"
-    "acmart"
-    "acmart10")
-   (TeX-add-symbols
-    '("footnotetextcopyrightpermission" 1)))
+    "article"
+    "art12"))
  :latex)
+

paper/src/bib/references.bib

@@ -0,0 +1,425 @@
+
+@article{arnoud_v_den_boer_dynamic_2015,
+	title = {Dynamic pricing and learning: {Historical} origins, current research, and new directions},
+	volume = {20},
+	url = {https://www.sciencedirect.com/science/article/pii/S1876735415000021},
+	doi = {10.1016/j.sorms.2015.03.001},
+	number = {1},
+	journal = {Surveys in Operations Research and Management Science},
+	author = {{Arnoud V. den Boer}},
+	month = jun,
+	year = {2015},
+	pages = {1--18},
+	file = {PDF:/home/velocitatem/Zotero/storage/NUAGDYER/memo2025.pdf:application/pdf},
+}
+
+@article{iliou_detection_2021,
+	title = {Detection of {Advanced} {Web} {Bots} by {Combining} {Web} {Logs} with {Mouse} {Behavioural} {Biometrics}},
+	volume = {2},
+	url = {https://dl.acm.org/doi/10.1145/3447815},
+	doi = {10.1145/3447815},
+	number = {3},
+	journal = {Digital Threats: Research and Practice},
+	author = {Iliou, Christos and Kostoulas, Theodoros and Tsikrika, Theodora and Katos, Vasilis and Vrochidis, Stefanos and Kompatsiaris, Ioannis},
+	year = {2021},
+	pages = {1--26},
+	file = {PDF:/home/velocitatem/Zotero/storage/Q7J5EBEJ/3447815.pdf:application/pdf},
+}
+
+@phdthesis{salassa_politecnico_nodate,
+	title = {Politecnico di {Torino} {Algorithmic} {Pricing} in the digital age "{Ethical} considerations on its economic and social implications, and an analysis of possible solutions to overcome its critical issues" {Tutor}: {Candidate}},
+	abstract = {Algorithmic pricing is an emerging business practice that uses computational algorithms to determine
+the prices of products and services based on a number of dynamic factors. The aim of this thesis is to
+draw attention to the existence of these business practices, and the ethical and social implications that
+derive from them, and then focus on what could be effective solutions to increase the well-being of
+the community.
+In Chapter 2 of the thesis, a general introduction to the topic will be made, starting from its history
+and its evolution over the years; Chapter 3 will examine the different types of pricing algorithms.
+Subsequently, in Chapter 4 we will analyze the sectors in which they are most applicable, and the
+relative advantages and disadvantages they bring with them, with a critical analysis of the trade-offs
+generated. The effect of algorithmic pricing on competition will be studied, considering how the
+ability of algorithms to adapt quickly to market conditions can foster anti-competitive practices, such
+as price discrimination. Later, in Chapter 5, we will look at the issue of price transparency and how
+the opacity of algorithms can make it difficult for consumers to understand the pricing process and
+assess whether they are receiving fair treatment.
+To address these ethical issues, several possible solutions will be brought to light, described in
+Chapter 6, which will focus on the role of the government, as a regulatory, of the end consumer, who
+must be encouraged to educate and inform himself about the use of these practices, and of the
+company, as responsible for making its customers aware and acting in compliance with government
+laws, for fair and non-discriminatory use.},
+	urldate = {2025-11-12},
+	school = {Politecnico di Torino},
+	author = {Salassa, Fabio and Pautassi, Paolo},
+	file = {PDF:/home/velocitatem/Zotero/storage/L95WYQ8B/m-api-06aad998-d926-0d59-5593-82fdce5a678b.pdf:application/pdf},
+}
+
+@inproceedings{mueller_low-rank_2019,
+	title = {Low-{Rank} {Bandit} {Methods} for {High}-{Dimensional} {Dynamic} {Pricing}},
+	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 32 ({NeurIPS} 2019)},
+	author = {Mueller, Jonas W and Syrgkanis, Vasilis and Taddy, Matt},
+	year = {2019},
+	pages = {15442--15452},
+	file = {PDF:/home/velocitatem/Zotero/storage/IZD3C5SR/m-api-26f6207c-cc89-4aed-29b6-34629f18fe9b.pdf:application/pdf},
+}
+
+@article{shahidi_coasean_nodate,
+	title = {The {Coasean} {Singularity}? {Demand}, {Supply}, and {Market} {Design} with {AI} {Agents}},
+	abstract = {AI agents—autonomous systems that perceive, reason, and act on behalf of human principals—are poised to transform digital markets by dramatically reducing transaction costs. This chapter evaluates the economic implications of this transition, adopting a consumeroriented view of agents as market participants that can search, negotiate, and transact directly. From the demand side, agent adoption reflects derived demand: users trade off decision quality against effort reduction, with outcomes mediated by agent capability and task context. On the supply side, firms will design, integrate, and monetize agents, with outcomes hinging on whether agents operate within or across platforms. At the market level, agents create efficiency gains from lower search, communication, and contracting costs, but also introduce frictions such as congestion and price obfuscation. By lowering the costs of preference elicitation, contract enforcement, and identity verification, agents expand the feasible set of market designs but also raise novel regulatory challenges. While the net welfare effects remain an empirical question, the rapid onset of AI-mediated transactions presents a unique opportunity for economic research to inform real-world policy and market design.},
+	language = {en},
+	author = {Shahidi, Peyman and Rusak, Gili and Manning, Benjamin S and Fradkin, Andrey and Horton, John J},
+	file = {PDF:/home/velocitatem/Zotero/storage/TQCAPJDP/Shahidi et al. - The Coasean Singularity Demand, Supply, and Market Design with AI Agents.pdf:application/pdf},
+}
+
+@misc{byrnes_intro_2025,
+	title = {Intro to {Brain}-{Like}-{AGI} {Safety}},
+	url = {https://osf.io/fe36n_v1},
+	doi = {10.31219/osf.io/fe36n_v1},
+	abstract = {Suppose we someday build an Artificial General Intelligence (AGI) algorithm using similar principles of learning and cognition as the human brain. How would we use such an algorithm safely? I argue that this is an open technical problem, and my goal is to bring readers with no prior knowledge all the way up to the front-line of unsolved problems. Chapter 1 has background and motivation; Chapters 2-7 are on neuroscience, arguing for a picture of the brain that combines large-scale learning algorithms (e.g. in the cortex) and specific evolved reflexes (e.g. in the hypothalamus and brainstem); and Chapters 8-15 apply those neuroscience ideas to AGI safety. A major theme is the idea that the brain has something like a reinforcement learning reward function, which says that pain is bad, eating-when-hungry is good, etc. I argue that this reward function is centered around the hypothalamus and brainstem, and that all human desires—even "higher" desires for things like compassion and justice—come directly or indirectly from that innate reward function. If future programmers build brain-like AGI, they will likewise have a reward function slot in their source code, in which they can put whatever they want. If they put the wrong thing, the resulting AGI will wind up callously indifferent to human welfare. How might they avoid that? That's an open technical problem, but I will review some ideas and research directions.},
+	language = {en},
+	urldate = {2025-12-31},
+	publisher = {Open Science Framework},
+	author = {Byrnes, Steven J.},
+	month = mar,
+	year = {2025},
+	file = {PDF:/home/velocitatem/Zotero/storage/ZLJQ4DQ9/Byrnes - 2025 - Intro to Brain-Like-AGI Safety.pdf:application/pdf},
+}
+
+@article{shannon_mathematical_nodate,
+	title = {A {Mathematical} {Theory} of {Communication}},
+	language = {en},
+	author = {Shannon, C E},
+	file = {PDF:/home/velocitatem/Zotero/storage/FJRFRWK2/Shannon - A Mathematical Theory of Communication.pdf:application/pdf},
+}
+
+@misc{noauthor_order_stats_nodate,
+	title = {order\_stats},
+	file = {PDF:/home/velocitatem/Zotero/storage/D3QRGY9Z/order_stats.pdf:application/pdf},
+}
+
+@article{devine_nonlinear_nodate,
+	title = {Nonlinear {Pricing} with {Costly} {Information} {Acquisition}},
+	abstract = {This paper examines a nonlinear pricing model where the firm can choose to acquire costly information prior to offering contract menus to consumers; such as paying a consultant or investing in machine learning technologies. Information provides the firm with a signal about consumers types, whose accuracy increases as the firm acquires larger amounts of information. We show that the firm chooses to acquire information, only if it can purchase a sufficient amount that could alter its initial prior beliefs. Relative to standard settings where firms cannot acquire information, we identify how information acquisition changes optimal contract offers, equilibrium profits, information rents, and welfare. A better-informed firm increases its expected profits, but it can also increase expected utility when the cost of information is intermediate. Our results recommend balanced online privacy laws.},
+	language = {en},
+	author = {Devine, Brett R and Munoz-Garcia, Felix},
+	file = {PDF:/home/velocitatem/Zotero/storage/GQ28KVBF/Devine and Munoz-Garcia - Nonlinear Pricing with Costly Information Acquisition.pdf:application/pdf},
+}
+
+@misc{wang_learning_2025,
+	title = {Learning {Optimal} {Distributionally} {Robust} {Stochastic} {Control} in {Continuous} {State} {Spaces}},
+	url = {http://arxiv.org/abs/2406.11281},
+	doi = {10.48550/arXiv.2406.11281},
+	abstract = {We study data-driven learning of robust stochastic control for infinite-horizon systems with potentially continuous state and action spaces. In many managerial settings–supply chains, finance, manufacturing, services, and dynamic games–the state-transition mechanism is determined by system design, while available data capture the distributional properties of the stochastic inputs from the environment. For modeling and computational tractability, a decision maker often adopts a Markov control model with i.i.d. environment inputs, which can render learned policies fragile to internal dependence or external perturbations. We introduce a distributionally robust stochastic control paradigm that promotes policy reliability by introducing adaptive adversarial perturbations to the environment input, while preserving the modeling, statistical, and computational tractability of the Markovian formulation. From a modeling perspective, we examine two adversary models–current-action-aware and current-action-unaware–leading to distinct dynamic behaviors and robust optimal policies. From a statistical learning perspective, we characterize optimal finite-sample minimax rates for uniform learning of the robust value function across a continuum of states under ambiguity sets defined by the fk-divergence and Wasserstein distance. To efficiently compute the optimal robust policies, we further propose algorithms inspired by deep reinforcement learning methodologies. Finally, we demonstrate the applicability of the framework to real managerial problems.},
+	language = {en},
+	urldate = {2025-12-29},
+	publisher = {arXiv},
+	author = {Wang, Shengbo and Meng, Jason and Si, Nian and Blanchet, Jose and Zhou, Zhengyuan},
+	month = nov,
+	year = {2025},
+	note = {arXiv:2406.11281 [stat]},
+	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
+	file = {PDF:/home/velocitatem/Zotero/storage/RQ8XDSSG/Wang et al. - 2025 - Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces.pdf:application/pdf},
+}
+
+@misc{ie_recsim_2019,
+	title = {{RecSim}: {A} {Configurable} {Simulation} {Platform} for {Recommender} {Systems}},
+	shorttitle = {{RecSim}},
+	url = {http://arxiv.org/abs/1909.04847},
+	doi = {10.48550/arXiv.1909.04847},
+	abstract = {We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.},
+	urldate = {2025-12-29},
+	publisher = {arXiv},
+	author = {Ie, Eugene and Hsu, Chih-wei and Mladenov, Martin and Jain, Vihan and Narvekar, Sanmit and Wang, Jing and Wu, Rui and Boutilier, Craig},
+	month = sep,
+	year = {2019},
+	note = {arXiv:1909.04847 [cs]},
+	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Human-Computer Interaction, Computer Science - Information Retrieval},
+	file = {Preprint PDF:/home/velocitatem/Zotero/storage/CJJI2VQF/Ie et al. - 2019 - RecSim A Configurable Simulation Platform for Recommender Systems.pdf:application/pdf;Snapshot:/home/velocitatem/Zotero/storage/8XJKJTHE/1909.html:text/html},
+}
+
+@misc{kuhn_wasserstein_2024,
+	title = {Wasserstein {Distributionally} {Robust} {Optimization}: {Theory} and {Applications} in {Machine} {Learning}},
+	shorttitle = {Wasserstein {Distributionally} {Robust} {Optimization}},
+	url = {http://arxiv.org/abs/1908.08729},
+	doi = {10.48550/arXiv.1908.08729},
+	abstract = {Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples. This learning task is difficult even if all training and test samples are drawn from the same distribution—especially if the dimension of the uncertainty is large relative to the training sample size. Wasserstein distributionally robust optimization seeks data-driven decisions that perform well under the most adverse distribution within a certain Wasserstein distance from a nominal distribution constructed from the training samples. In this tutorial we will argue that this approach has many conceptual and computational benefits. Most prominently, the optimal decisions can often be computed by solving tractable convex optimization problems, and they enjoy rigorous out-of-sample and asymptotic consistency guarantees. We will also show that Wasserstein distributionally robust optimization has interesting ramifications for statistical learning and motivates new approaches for fundamental learning tasks such as classification, regression, maximum likelihood estimation or minimum mean square error estimation, among others.},
+	language = {en},
+	urldate = {2025-12-27},
+	publisher = {arXiv},
+	author = {Kuhn, Daniel and Esfahani, Peyman Mohajerin and Nguyen, Viet Anh and Shafieezadeh-Abadeh, Soroosh},
+	month = nov,
+	year = {2024},
+	note = {arXiv:1908.08729 [stat]},
+	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Mathematics - Optimization and Control},
+	file = {PDF:/home/velocitatem/Zotero/storage/FAWJEK6J/Kuhn et al. - 2024 - Wasserstein Distributionally Robust Optimization Theory and Applications in Machine Learning.pdf:application/pdf},
+}
+
+@misc{arunachaleswaran_learning_2025,
+	title = {Learning to {Play} {Against} {Unknown} {Opponents}},
+	url = {http://arxiv.org/abs/2412.18297},
+	doi = {10.48550/arXiv.2412.18297},
+	abstract = {We consider the problem of a learning agent who has to repeatedly play a general sum game against a strategic opponent who acts to maximize their own payoff by optimally responding against the learner’s algorithm. The learning agent knows their own payoff function, but is uncertain about the payoff of their opponent (knowing only that it is drawn from some distribution D). What learning algorithm should the agent run in order to maximize their own total utility, either in expectation or in the worst-case over D? When the learning algorithm is constrained to be a no-regret algorithm, we demonstrate how to efficiently construct an optimal learning algorithm (asymptotically achieving the optimal utility) in polynomial time for both the in-expectation and worst-case problems, independent of any other assumptions. When the learning algorithm is not constrained to no-regret, we show how to construct an ε-optimal learning algorithm (obtaining average utility within ε of the optimal utility) for both the in-expectation and worst-case problems in time polynomial in the size of the input and 1/ε, when either the size of the game or the support of D is constant. Finally, for the special case of the maximin objective, where the learner wishes to maximize their minimum payoff over all possible optimizer types, we construct a learner algorithm that runs in polynomial time in each step and guarantees convergence to the optimal learner payoff. All of these results make use of recently developed machinery that converts the analysis of learning algorithms to the study of the class of corresponding geometric objects known as menus.},
+	language = {en},
+	urldate = {2025-12-27},
+	publisher = {arXiv},
+	author = {Arunachaleswaran, Eshwar Ram and Collina, Natalie and Schneider, Jon},
+	month = feb,
+	year = {2025},
+	note = {arXiv:2412.18297 [cs]},
+	keywords = {Computer Science - Machine Learning, Computer Science - Computer Science and Game Theory},
+	file = {PDF:/home/velocitatem/Zotero/storage/M6V9LLCS/Arunachaleswaran et al. - 2025 - Learning to Play Against Unknown Opponents.pdf:application/pdf},
+}
+
+@misc{li_distributionally_2025,
+	title = {Distributionally {Robust} {Optimization} with {Adversarial} {Data} {Contamination}},
+	url = {http://arxiv.org/abs/2507.10718},
+	doi = {10.48550/arXiv.2507.10718},
+	abstract = {Distributionally Robust Optimization (DRO) provides a framework for decision-making under distributional uncertainty, yet its effectiveness can be compromised by outliers in the training data. This paper introduces a principled approach to simultaneously address both challenges. We focus on optimizing Wasserstein-1 DRO objectives for generalized linear models with convex Lipschitz loss functions, where an \$ε\$-fraction of the training data is adversarially corrupted. Our primary contribution lies in a novel modeling framework that integrates robustness against training data contamination with robustness against distributional shifts, alongside an efficient algorithm inspired by robust statistics to solve the resulting optimization problem. We prove that our method achieves an estimation error of \$O({\textbackslash}sqrtε)\$ for the true DRO objective value using only the contaminated data under the bounded covariance assumption. This work establishes the first rigorous guarantees, supported by efficient computation, for learning under the dual challenges of data contamination and distributional shifts.},
+	language = {en},
+	urldate = {2025-12-27},
+	publisher = {arXiv},
+	author = {Li, Shuyao and Diakonikolas, Ilias and Diakonikolas, Jelena},
+	month = nov,
+	year = {2025},
+	note = {arXiv:2507.10718 [cs]},
+	keywords = {Computer Science - Machine Learning, Mathematics - Optimization and Control, Computer Science - Data Structures and Algorithms},
+	file = {PDF:/home/velocitatem/Zotero/storage/H6AXDTLX/Li et al. - 2025 - Distributionally Robust Optimization with Adversarial Data Contamination.pdf:application/pdf},
+}
+
+@misc{karten_llm_2025,
+	title = {{LLM} {Economist}: {Large} {Population} {Models} and {Mechanism} {Design} in {Multi}-{Agent} {Generative} {Simulacra}},
+	shorttitle = {{LLM} {Economist}},
+	url = {http://arxiv.org/abs/2507.15815},
+	doi = {10.48550/arXiv.2507.15815},
+	abstract = {We present the LLM Economist, a novel framework that uses agent-based modeling to design and assess economic policies in strategic environments with hierarchical decision-making. At the lower level, bounded rational worker agents—instantiated as persona-conditioned prompts sampled from U.S. Census-calibrated income and demographic statistics—choose labor supply to maximize text-based utility functions learned in-context. At the upper level, a planner agent employs in-context reinforcement learning to propose piecewise-linear marginal tax schedules anchored to the current U.S. federal brackets. This construction endows economic simulacra with three capabilities requisite for credible fiscal experimentation: (i) optimization of heterogeneous utilities, (ii) principled generation of large, demographically realistic agent populations, and (iii) mechanism design—the ultimate nudging problem—expressed entirely in natural language. Experiments with populations of up to one hundred interacting agents show that the planner converges near Stackelberg equilibria that improve aggregate social welfare relative to Saez solutions, while a periodic, persona-level voting procedure furthers these gains under decentralized governance. These results demonstrate that large language model-based agents can jointly model, simulate, and govern complex economic systems, providing a tractable test bed for policy evaluation at the societal scale to help build better civilizations.},
+	language = {en},
+	urldate = {2025-12-27},
+	publisher = {arXiv},
+	author = {Karten, Seth and Li, Wenzhe and Ding, Zihan and Kleiner, Samuel and Bai, Yu and Jin, Chi},
+	month = jul,
+	year = {2025},
+	note = {arXiv:2507.15815 [cs]},
+	keywords = {Computer Science - Machine Learning, Computer Science - Multiagent Systems},
+	file = {PDF:/home/velocitatem/Zotero/storage/U7A5Q78V/Karten et al. - 2025 - LLM Economist Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra.pdf:application/pdf},
+}
+
+@techreport{mullapudi_reinforcement_nodate,
+	title = {A {Reinforcement} {Learning} {Approach} to {Dynamic} {Pricing}},
+	abstract = {Dynamic pricing represents a critical strategic challenge in modern e-commerce, where firms must navigate fluctuating demand, inventory constraints, and aggressive competitor actions. Traditional static and heuristic-based pricing models often fail to capture the complex, non-linear dynamics of competitive digital markets, leading to suboptimal profitability. This paper proposes a model-free reinforcement learning (RL) framework to address this challenge. Specifically, we design, implement, and evaluate a Q-learning agent capable of learning an optimal, state-dependent pricing policy. The agent is trained and evaluated within a simulated market environment constructed from the publicly available "Retail Price Optimization" dataset from Kaggle, which provides a rich feature set including historical sales, product characteristics, seasonality, and, crucially, competitor pricing data. The problem is formulated as a Markov Decision Process (MDP), where the agent's state incorporates its price position relative to competitors, competitor price trends, and seasonal factors. The agent's performance is benchmarked against three baseline strategies: static pricing, a reactive "follow-the-leader" heuristic, and random pricing. The results demonstrate that the Q-learning agent achieves a substantial increase in total cumulative profit over the evaluation period, outperforming all baselines by learning a nuanced policy that strategically balances price adjustments in response to market conditions. This work provides a practical and reproducible blueprint for applying reinforcement learning to optimize pricing decisions in a simulated yet realistic competitive retail environment, highlighting the potential of RL to automate complex strategic decision-making.},
+	author = {Mullapudi, Pavan},
+	note = {Publication Title: International Journal on Science and Technology (IJSAT) IJSAT25049558
+Volume: 16
+Issue: 4},
+	keywords = {Index Terms: Dynamic Pricing, Markov Decision Process, Price Optimization, Q-Learning, Reinforcement Learning, Retail Analytics},
+	file = {PDF:/home/velocitatem/Zotero/storage/G95TBLF7/9558.pdf:application/pdf},
+}
+
+@techreport{roughgarden_cs364a_2013,
+	title = {{CS364A}: {Algorithmic} {Game} {Theory} {Lecture} \#5: {Revenue}-{Maximizing} {Auctions} *},
+	author = {Roughgarden, Tim},
+	year = {2013},
+	file = {PDF:/home/velocitatem/Zotero/storage/C39VM7N9/l5.pdf:application/pdf},
+}
+
+@techreport{kuhn_distributionally_2025,
+	title = {Distributionally {Robust} {Optimization}},
+	abstract = {Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical information. DRO seeks decisions that perform best under the worst distribution in the ambiguity set. This worst case criterion is supported by findings in psychology and neuroscience, which indicate that many decision-makers have a low tolerance for distributional ambiguity. DRO is rooted in statistics, operations research and control theory, and recent research has uncovered its deep connections to regularization techniques and adversarial training in machine learning. This survey presents the key findings of the field in a unified and self-contained manner.},
+	author = {Kuhn, Daniel and Shafiee, Soroosh and Wiesemann, Wolfram},
+	year = {2025},
+	note = {arXiv: 2411.02549v3},
+	file = {PDF:/home/velocitatem/Zotero/storage/IXTTMD7G/full-text.pdf:application/pdf},
+}
+
+@article{parkes_economic_2015,
+	title = {Economic reasoning and artificial intelligence},
+	volume = {349},
+	issn = {10959203},
+	doi = {10.1126/science.aaa8403},
+	abstract = {The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics.We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs.Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.},
+	number = {6245},
+	journal = {Science},
+	author = {Parkes, David C. and Wellman, Michael P.},
+	month = jul,
+	year = {2015},
+	pmid = {26185245},
+	note = {Publisher: American Association for the Advancement of Science},
+	pages = {267--272},
+	file = {PDF:/home/velocitatem/Zotero/storage/27KLNFRU/_aiEcon.pdf:application/pdf},
+}
+
+@article{yokoo_effect_2004,
+	title = {The effect of false-name bids in combinatorial auctions: {New} fraud in internet auctions},
+	volume = {46},
+	issn = {08998256},
+	doi = {10.1016/S0899-8256(03)00045-9},
+	abstract = {We examine the effect of false-name bids on combinatorial auction protocols. False-name bids are bids submitted by a single bidder using multiple identifiers such as multiple e-mail addresses. The obtained results are summarized as follows: (1) the Vickrey-Clarke-Groves (VCG) mechanism, which is strategy-proof and Pareto efficient when there exists no false-name bid, is not false-name-proof; (2) there exists no false-name-proof combinatorial auction protocol that satisfies Pareto efficiency; (3) one sufficient condition where the VCG mechanism is false-name-proof is identified, i.e., the concavity of a surplus function over bidders. © 2003 Elsevier Inc. All rights reserved.},
+	number = {1},
+	journal = {Games and Economic Behavior},
+	author = {Yokoo, Makoto and Sakurai, Yuko and Matsubara, Shigeo},
+	year = {2004},
+	note = {Publisher: Academic Press Inc.},
+	keywords = {Auction, Mechanism design, Strategy-proof},
+	pages = {174--188},
+	file = {PDF:/home/velocitatem/Zotero/storage/LUVQV6WT/Yokoo04.pdf:application/pdf},
+}
+
+@inproceedings{feldman_free-riding_2004,
+	title = {Free-riding and whitewashing in peer-to-peer systems},
+	isbn = {1-58113-942-X},
+	doi = {10.1145/1016527.1016539},
+	abstract = {We develop a model to study the phenomenon of free-riding in peer-to-peer (P2P) systems. At the heart of our model is a user of a certain type, an intrinsic and private parameter that reflects the user's willingness to contribute resources to the system. A user decides whether to contribute or free-ride based on how the current contribution cost in the system compares to her type. When the societal generosity (i.e., the average type) is low, intervention is required in order to sustain the system. We present the effect of mechanisms that exclude low type users or, more realistic, penalize free-riders with degraded service. We also consider dynamic scenarios with arrivals and departures of users, and with whitewashers: users who leave the system and rejoin with new identities to avoid reputational penalties. We find that when penalty is imposed on all newcomers in order to avoid whitewashing, system performance degrades significantly only when the turnover rate among users is high.},
+	booktitle = {Proceedings of the {ACM} {SIGCOMM} 2004 {Workshops}},
+	publisher = {Association for Computing Machinery},
+	author = {Feldman, Michal and Papadimitriou, Christos and Chuang, John and Stoica, Ion},
+	year = {2004},
+	keywords = {Cheap pseudonyms, Cooperation, Equilibrium, Exclusion, Free-riding, Identity cost, Incentives, Peer-to-peer, Whitewashing},
+	pages = {228--235},
+	file = {PDF:/home/velocitatem/Zotero/storage/K32WH6SB/1016527.1016539.pdf:application/pdf},
+}
+
+@article{calvano_artificial_2018,
+	title = {Artificial {Intelligence}, {Algorithmic} {Pricing} and {Collusion}},
+	url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3304991},
+	doi = {10.2139/ssrn.3304991},
+	journal = {SSRN Electronic Journal},
+	author = {Calvano, Emilio and Calzolari, Giacomo and Denicolo, Vincenzo and Pastorello, Sergio},
+	year = {2018},
+	file = {PDF:/home/velocitatem/Zotero/storage/WYTSSZBR/ssrn-3304991.pdf:application/pdf},
+}
+
+@techreport{varian_economic_1995,
+	title = {Economic {Mechanism} {Design} for {Computerized} {Agents}},
+	abstract = {The eeld of economic mechanism design has been an active area of research in economics for at least 20 years. This eld uses the tools of economics and game theory to design {\textbackslash}rules of interaction" for economic transactions that will, in principle , yield some desired outcome. In this paper I provide an overview of this subject for an audience interested in applications to electronic commerce and discuss some special problems that arise in this context.},
+	author = {Varian, Hal R},
+	year = {1995},
+	file = {PDF:/home/velocitatem/Zotero/storage/S8635QX6/varian95a.pdf:application/pdf},
+}
+
+@book{russell_artificial_nodate,
+	title = {Artificial {Intelligence} {A} {Modern} {Approach} {Fourth} {Edition} {Global} {Edition}},
+	isbn = {978-1-292-40117-1},
+	author = {Russell, Stuart and Norvig, Peter},
+	file = {PDF:/home/velocitatem/Zotero/storage/6B8W8S27/efdd4d1d4c2087fe1cbe03d9ced67f34.pdf:application/pdf},
+}
+
+@techreport{wellman_price_2004,
+	title = {Price {Prediction} in a {Trading} {Agent} {Competition} {Yevgeniy} {Vorobeychik}},
+	abstract = {The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.},
+	author = {Wellman, Michael P and Reeves, Daniel M and Lochner, Kevin M and Edu, Yvorobey@umich},
+	year = {2004},
+	note = {Publication Title: Journal of Artificial Intelligence Research
+Volume: 21},
+	pages = {19--36},
+	file = {PDF:/home/velocitatem/Zotero/storage/N9JNXFJW/live-1333-2265-jair.pdf:application/pdf},
+}
+
+@techreport{shoham_multiagent_nodate,
+	title = {Multiagent {Systems}: {Algorithmic}, {Game}-{Theoretic}, and {Logical} {Foundations}},
+	url = {http://www.masfoundations.org.},
+	author = {Shoham, Yoav and Leyton-Brown, Kevin},
+	keywords = {algorithms, auctions, communication, competition, cooperation, distributed problem solving, game theory, learning, logic, mechanism design, social choice},
+	file = {PDF:/home/velocitatem/Zotero/storage/QZVYS7V9/shoham09a.pdf:application/pdf},
+}
+
+@article{xia_evaluation-driven_2025,
+	title = {Evaluation-{Driven} {Development} and {Operations} of {LLM} {Agents}: {A} {Process} {Model} and {Reference} {Architecture}},
+	url = {http://arxiv.org/abs/2411.13768},
+	abstract = {Large Language Models (LLMs) have enabled the emergence of LLM agents, systems capable of pursuing under-specified goals and adapting after deployment. Evaluating such agents is challenging because their behavior is open ended, probabilistic, and shaped by system-level interactions over time. Traditional evaluation methods, built around fixed benchmarks and static test suites, fail to capture emergent behaviors or support continuous adaptation across the lifecycle. To ground a more systematic approach, we conduct a multivocal literature review (MLR) synthesizing academic and industrial evaluation practices. The findings directly inform two empirically derived artifacts: a process model and a reference architecture that embed evaluation as a continuous, governing function rather than a terminal checkpoint. Together they constitute the evaluation-driven development and operations (EDDOps) approach, which unifies offline (development-time) and online (runtime) evaluation within a closed feedback loop. By making evaluation evidence drive both runtime adaptation and governed redevelopment, EDDOps supports safer, more traceable evolution of LLM agents aligned with changing objectives, user needs, and governance constraints.},
+	author = {Xia, Boming and Lu, Qinghua and Zhu, Liming and Xing, Zhenchang and Zhao, Dehai and Zhang, Hao},
+	month = nov,
+	year = {2025},
+	note = {arXiv: 2411.13768},
+	file = {PDF:/home/velocitatem/Zotero/storage/H8IS64AW/2411.13768v2.pdf:application/pdf},
+}
+
+@techreport{xie_osworld_nodate,
+	title = {{OSWORLD}: {Benchmarking} {Multimodal} {Agents} for {Open}-{Ended} {Tasks} in {Real} {Computer} {Environments}},
+	url = {https://os-world.github.io},
+	abstract = {Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to certain applications or domains, failing to reflect the diverse and complex nature of real-world computer use, thereby limiting the scope of tasks and agent scalability. To address this issue, we introduce OSWORLD, the first-of-its-kind scalable, real computer environment for multimodal agents, supporting task setup, execution-based evaluation, and interactive learning across various operating systems such as Ubuntu, Windows, and macOS. OSWORLD can serve as a unified, integrated computer environment for assessing open-ended computer tasks that involve arbitrary applications. Building upon OSWORLD, we create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and workflows spanning multiple applications. Each task example is derived from real-world computer use cases and includes a detailed initial state setup configuration and a custom execution-based evaluation script for reliable, reproducible evaluation. Extensive evaluation of state-of-the-art LLM/VLM-based agents on OSWORLD reveals significant deficiencies in their ability to serve as computer assistants. While humans can accomplish over 72.36\% of the tasks, the best model achieves only 12.24\% success, primarily struggling with GUI grounding and operational knowledge. Comprehensive analysis using OSWORLD provides valuable insights for developing multimodal generalist agents that were not possible with previous benchmarks. Our code, environment, baseline models, and data are publicly available at https://os-world.github.io.},
+	author = {Xie, Tianbao and Zhang, Danyang and Chen, Jixuan and Li, Xiaochuan and Zhao, Siheng and Cao, Ruisheng and Jing Hua, Toh and Cheng, Zhoujun and Shin, Dongchan and Lei, Fangyu and Liu, Yitao and Xu, Yiheng and Zhou, Shuyan and Savarese, Silvio and Xiong, Caiming and Zhong, Victor and Yu, Tao},
+	note = {arXiv: 2404.07972v2},
+	file = {PDF:/home/velocitatem/Zotero/storage/LLRKXIC7/full-text.pdf:application/pdf},
+}
+
+@techreport{imperva_rapid_2025,
+	title = {The {Rapid} {Rise} of {Bots} and the {Unseen} {Risk} for {Business} \#{2025BADBOTREPORT}},
+	author = {{Imperva}},
+	year = {2025},
+	file = {PDF:/home/velocitatem/Zotero/storage/AWR9IQRD/2025-Bad-Bot-Report.pdf:application/pdf},
+}
+
+@article{perez-ricardo_exploring_2025,
+	title = {Exploring booking intentions through price elasticity of demand in tourism accommodations using large-scale data analytics},
+	volume = {31},
+	issn = {24448834},
+	doi = {10.1016/j.iedeen.2025.100271},
+	abstract = {The study aims to explore tourists' booking intentions by analyzing the price elasticity of demand in tourist accommodations. This analysis should reveal how changes in price affect booking behavior across different customer segments, using online booking records. A dataset was compiled from 106 hotels in Malaga, Spain, comprising 27,910 online bookings sourced exclusively from hotel websites. To understand the price elasticity of demand, a simple log-log regression was applied, segmenting the data based on key revenue-related variables. Subsequently, a cluster segmentation was performed using the Elbow method and K-means algorithm to identify distinct market segments. The findings highlighted that Family Travelers and Short Stay Travelers segments exhibited elastic demand, indicating higher sensitivity to price fluctuations. In contrast, Early Bookers and Mid-Season Long Stayers demonstrated inelastic demand, with lower responsiveness to changes in tourist accommodation prices. The number of variables analyzed in this study, along with the cluster analysis, represent a novelty and contribute to the existing literature on market segmentation and price elasticity of demand. This integration enriches both fields of research, offering mutual benefits and deeper insights that enhance the understanding of booking intention and pricing strategies.},
+	number = {1},
+	urldate = {2025-11-28},
+	journal = {European Research on Management and Business Economics},
+	author = {Pérez-Ricardo, Elizabeth del Carmen and García-Mestanza, Josefa},
+	month = jan,
+	year = {2025},
+	note = {Publisher: European Academy of Management and Business Economics},
+	keywords = {Booking intention, Price elasticity, Tourist segmentation},
+	file = {PDF:/home/velocitatem/Zotero/storage/QNXZJLRM/S2444883425000038.pdf:application/pdf},
+}
+
+@misc{ghaffary_amazon_nodate,
+	title = {Amazon {Sues} to {Stop} {Perplexity} {From} {Using} {AI} {Tool} to {Buy} {Stuff}},
+	url = {https://www.bloomberg.com/news/articles/2025-11-04/amazon-demands-perplexity-stop-ai-agent-from-making-purchases},
+	author = {Ghaffary, Shirin and Day, Matt},
+	file = {PDF:/home/velocitatem/Zotero/storage/IQL6FPWE/Amazon Sues to Stop Perplexity From Using AI Tool to Buy Stuff - Bloomberg.pdf:application/pdf},
+}
+
+@techreport{besbes_dynamic_nodate,
+	title = {Dynamic {Pricing} {Without} {Knowing} the {Demand} {Function}: {Risk} {Bounds} and {Near}-{Optimal} {Algorithms} *},
+	abstract = {We consider a single product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve), is not known. We consider two instances of this problem: i.) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and ii.) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function "on the fly," and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is "close" to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function; manifested as the revenue loss due to model uncertainty.},
+	author = {Besbes, Omar and Zeevi, Assaf},
+	note = {Publication Title: Operations Research},
+	keywords = {learning, asymptotic analysis, estimation, exploration-exploitation, pricing, Revenue management, value of information},
+	file = {PDF:/home/velocitatem/Zotero/storage/SBAIB4V2/Dp_wo_demand_risk_ob_az_posted.pdf:application/pdf},
+}
+
+@techreport{markntel_advisors_global_2025,
+	address = {Noida, Uttar Pradesh, India},
+	title = {Global {AI} {Agent} {Market} {Research} {Report}: {Forecast} (2026–2032)},
+	url = {https://www.marknteladvisors.com/research-library/ai-agent-market.html},
+	urldate = {2025-12-12},
+	institution = {MarkNtel Advisors},
+	author = {{MarkNtel Advisors}},
+	year = {2025},
+}
+
+@article{amjad_censored_2017,
+	title = {Censored {Demand} {Estimation} in {Retail}},
+	volume = {1},
+	url = {https://par.nsf.gov/servlets/purl/10066022},
+	doi = {10.1145/3154489},
+	abstract = {In this paper, the question of interest is estimating true demand of a product at a given store location and time period in the retail environment based on a single noisy and potentially censored observation. To address this question, we introduce a \%non-parametric framework to make inference from multiple time series. Somewhat surprisingly, we establish that the algorithm introduced for the purpose of "matrix completion" can be used to solve the relevant inference problem. Specifically, using the Universal Singular Value Thresholding (USVT) algorithm [7], we show that our estimator is consistent: the average mean squared error of the estimated average demand with respect to the true average demand goes to 0 as the number of store locations and time intervals increase to \${\textbackslash}infty\$. We establish naturally appealing properties of the resulting estimator both analytically as well as through a sequence of instructive simulations. Using a real dataset in retail (Walmart), we argue for the practical relevance of our approach.},
+	number = {2},
+	urldate = {2025-11-12},
+	journal = {Proceedings of the ACM on Measurement and Analysis of Computing Systems},
+	author = {Amjad, Muhammad J. and Shah, Devavrat},
+	month = dec,
+	year = {2017},
+	note = {Publisher: Association for Computing Machinery (ACM)},
+	pages = {1--28},
+	file = {PDF:/home/velocitatem/Zotero/storage/5ZYADDT4/10066022.pdf:application/pdf},
+}
+
+@misc{ganie_uncertainty_2025,
+	title = {Uncertainty in {Authorship}: {Why} {Perfect} {AI} {Detection} {Is} {Mathematically} {Impossible}},
+	shorttitle = {Uncertainty in {Authorship}},
+	url = {http://arxiv.org/abs/2509.11915},
+	doi = {10.48550/arXiv.2509.11915},
+	abstract = {As large language models (LLMs) become more advanced, it is increasingly difficult to distinguish between human-written and AI-generated text. This paper draws a conceptual parallel between quantum uncertainty and the limits of authorship detection in natural language. We argue that there is a fundamental trade-off: the more confidently one tries to identify whether a text was written by a human or an AI, the more one risks disrupting the text's natural flow and authenticity. This mirrors the tension between precision and disturbance found in quantum systems. We explore how current detection methods--such as stylometry, watermarking, and neural classifiers--face inherent limitations. Enhancing detection accuracy often leads to changes in the AI's output, making other features less reliable. In effect, the very act of trying to detect AI authorship introduces uncertainty elsewhere in the text. Our analysis shows that when AI-generated text closely mimics human writing, perfect detection becomes not just technologically difficult but theoretically impossible. We address counterarguments and discuss the broader implications for authorship, ethics, and policy. Ultimately, we suggest that the challenge of AI-text detection is not just a matter of better tools--it reflects a deeper, unavoidable tension in the nature of language itself.},
+	language = {en},
+	urldate = {2026-01-05},
+	publisher = {arXiv},
+	author = {Ganie, Aadil Gani},
+	month = sep,
+	year = {2025},
+	note = {arXiv:2509.11915 [cs]},
+	keywords = {Computer Science - Computation and Language},
+	file = {PDF:/home/velocitatem/Zotero/storage/3Z2XK4QC/Ganie - 2025 - Uncertainty in Authorship Why Perfect AI Detection Is Mathematically Impossible.pdf:application/pdf},
+}

paper/src/chapters/01-intro.tex

@@ -8,9 +8,50 @@
 
 \section{Introduction}
 
-Research Objectives and Contribution: What are we making, why and who should care?
+In this paper we present an exploration and defense against the presence of new commercial entities in digitally powered platforms, preserving market equilibrium in the age of AI. This research establishes the following contributions: definition and formalization of non-human transactors in e-commerce platforms, development of a testing-ground for capturing the behavioral essence of these transactors across a large variety of digital systems, construction of a discriminative model (to prove separability) as a strong learner for downstream mitigation of contamination by non-human entities, translation of such learned separability into existing dynamic pricing machine learning loops, and finally establishment of a high-level KPI-affecting causal effect and cost-saving framework for the future of internet commerce in the presence of such non-human learners.
+
+This research effort touches a large variety of domains, spanning behavioral economics for understanding the rationality of behavior as theorized by the concept of homo economicus, agent-based modeling to translate our learned separability into disjoint dynamic pricing systems, reinforcement learning which serves as the SOTA for price-learners, and dynamic pricing and market equilibrium theory to understand the risks of possible supra-competitive pricing phenomena in cases of adversarial pricing systems driving the market out of equilibrium.
 
 \subsection{Motivation and Market Context}
-Current market dynamics and trends of dynamic pricing and AI agents. Future projections of AI agents. Key stakeholders that are discussing this and reporting on it (Thales). Who is most affected
+
+The current innovation boom in generative artificial intelligence and its applications to knowledge-based work tasks has brought many competing technologies for browser-use automation, with benchmarks and evaluations \cite{xia_evaluation-driven_2025} motivating the development of capabilities focused on commercial research, understanding, and transaction execution \cite{xie_osworld_nodate}. The ``AI Agent'' market is forecasted to grow from around USD 5-8 billion in 2025 to USD 42-52 billion by 2030. This surge reflects adoption in e-commerce, customer service, and enterprise automation, where agents handle interactions previously done by humans, raising the question of how these systems should be designed for future robustness as well as how to maintain a competitive edge in the analytical components of e-commerce platforms \cite{markntel_advisors_global_2025}.
+
+The key stakeholders affected by the threat of increasing agent-driven traffic include online businesses and platform operators (especially in bot-heavy sectors like retail, travel, and financial services), their security, fraud, and engineering teams, end users whose accounts and data are exposed and whose experience degrades, regulators and legal stakeholders responding to breaches and fraud, and the attackers or bot operators driving the automation \cite{imperva_rapid_2025}.
+
+The industry has already seen legal action in cases like Amazon against Perplexity \cite{ghaffary_amazon_nodate}, stemming from the difficulty of identifying traffic from hybrid systems like the Commet browser. This paper explores such systems to better understand what the interaction data looks like and what it means for dynamic pricing and recommendation systems downstream. This observed impact indicates a need for prevention of secondary negative effects on the ``legacy'' systems which power modern revenue sources for many companies. Dynamic pricing algorithms rely on directly translating demand features $q$ to new price assignments $\hat{p}$ across a catalogue of products of size $N$. This opens opportunities to design a \textit{tabula rasa} of digital market mechanisms that will shape the future of commerce in the age of artificial intelligence.
+
 \subsection{Solution Space Overview}
-Different approaches and perspectives, here also add a preview of what will be developed and explored in the lit review.
+Dynamic pricing systems, as presented in \cite{mueller_low-rank_2019}, often deal with sparse low-rank data of demand signals which, combined with contamination from agents, creates complex interactions that impact pricing. To further complicate the problem, certain commercial settings such as the one presented in \cite{amjad_censored_2017} must address the true demand of products under censored observations. This provides a formulation for handling demand in our case with multiple kinds of commercial mediators: $\hat{q} \gets q_A + q_H$ where $q_A$ represents the distribution of demand generated by agentic mediators and $q_H$ represents that of true human demand, these are two distinct populations with divergent objective functions.
+
+We formally define interaction data as coming from some actor which can either be an agent ($A$) or human ($H$). For purposes of this research, an agent is an algorithmic loop with the ability to access a web platform and perform actions such as clicks, scrolls, and input field fills. The loop terminates when the internal large language model judges the provided task definition as complete. A detailed breakdown can be found in \cref{algagent-loop}.
+
+
+\begin{algorithm}[t]
+\DontPrintSemicolon
+
+\SetKwInOut{Input}{Input}
+\SetKwInOut{Output}{Output}
+
+\Input{Goal $G$, Platform URL $u$, LLM $\mathcal{M}$}
+\Output{Task completion result $r$}
+
+Initialize browser instance $\mathcal{B}$ with connection to $u$\;
+Construct prompt $\pi \gets \textsc{BuildPrompt}(G, u)$\;
+$\text{done} \gets \text{False}$\;
+
+\While{$\neg \text{done}$}{
+    Observe current page state $s_t$ from $\mathcal{B}$\;
+    Query $\mathcal{M}$ with $(\pi, s_t)$ to determine next action $a_t \in \{\text{click}, \text{scroll}, \text{fill}, \text{navigate}\}$\;
+    Execute $a_t$ on $\mathcal{B}$ to transition to state $s_{t+1}$\;
+    $\text{done} \gets \mathcal{M}.\textsc{JudgeCompletion}(G, s_{t+1})$\;
+}
+
+Extract final result $r$ from terminal state\;
+\Return{$r$}\;
+
+\caption{AI Agent's Interaction Loop}
+\label{algagent-loop}
+\end{algorithm}
+
+
+The previously described goal of separability allows us to formulate a task which entails taking raw interaction data for either actor and creating a composite demand estimate $\hat{q}$. We propose a robust optimization objective defined in our methodology, transforming the pricing problem into a form of Distributionally Robust Optimization \cite{kuhn_distributionally_2025} where the learner must guard against adversarial contamination in observed demand distributors. In this setting we must learn to make decision that perform under the assumption of not having a single estimated probability distribution but under an ambiguity set of any distribution, of which we have limited information. In our case as stated is a mixture of distributions with a parameter which is unknown and non-stationary.

paper/src/chapters/02-literature-review.tex

@@ -1,15 +1,44 @@
 \section{Literature Review}
 
-\subsection{Foundational Concepts}
+To better understand all wedges of the work, we must start by exploring the nature of agents and agentic computer use and web automation, complementing that with economic reasoning and strategic interaction. The final surface to cover, leads us to data-driven dynamic pricing under uncertainty. The key technical risk is not ``agents buying things'' per se, but agents shaping the behavioral and demand signals that downstream pricing systems consume and depend on. The introduction of these mediating actor entities into economic systems, is further creating a threat of false-name bidding \cite{yokoo_effect_2004}, which prior research has explored in a trading context. Other research on pseudonyms in dynamic systems, demonstrate whitewashing in AI agents which can ignore defensive mechanisms by re-entry with different identities \cite{feldman_free-riding_2004}. Dynamic pricing assumes demand proxies are behaviorally meaningful, while bot detection aims at security and access control. The missing bridge is a principled framework for separating non-human reconnaissance from genuine human demand expression and integrating that separation into pricing heuristics without degrading legitimate user experience (in our research tracked by the user-experience index). This gap, is what our contribution aims to address, particularly for the aforementioned stakeholder groups.
+
+\subsection{Agent Taxonomy and Definitions}
+
+An agent in the context of artificial intelligence is generally defined by anything that can reason and act upon observations of its environments (collected through some sensory inputs) and carry out actions through effectors. Moreover, a rational agent is an entity that is capable of perceiving the world around them and taking actions to advance specified goals. This definition by \cite{russell_artificial_nodate} is further developed in an economic context by \cite{parkes_economic_2015}, suggesting AI research attempts to construct a synthetic \textit{homo economicus}, which may also be termed \textit{machina economicus}.
+A specific class or taxon of this \textit{machina economicus}, the Large Language Model (LLM) agent, is defined as an autonomous system capable of achieving goals and adapting post-training, often without needing explicit code or fundamental model changes. \cite{xia_evaluation-driven_2025}
+
+We must however acknowledge the current SOTA as presented by OSWORLD simulations in \cite{xie_osworld_nodate} have demonstrated that multi-modal tasks across desktop and web interaction modes, have a top-performing score of only 12.24\% success, whereas humans have a higher 72\% success rate. This weakness matters for this research because it clarifies the near-term threat model: practical exploitation does not require a fully competent ``computer assistant'', only enough automation to perform high-volume reconnaissance actions (search/filter/open product pages, probe availability/price boundaries) that can contaminate behavioral signals. With the expected growth of these capabilities, this threat only becomes more perilous to revenue management systems.
+
+We model an agent session as producing some events with lower in-session conversion levels relative to humans, this we state in our assumption that $P(\text{purchase} \vert A) \ll P(\text{purchase} \vert H)$ but with a potentially higher volatility in $\hat{q}$, which we observe through the look-to-book metrics in our simulation.
+
+\subsection{Economic Agents: From Homo Economicus to Machina Economicus}
+
+Existing behavioral economic models tend to be criticized for the assumption of rational behavior, as is embodied in the term of homo economicus. The definition of a machina economicus by \cite{parkes_economic_2015} is quite appropriate for our case, particularly because these assumptions of rationality have been argued to be a very adequate reference for AI research by \cite{varian_economic_1995}. For modeling this behavior, the trajectories of these agents can be formally defined to be partially observable Markov decision processes. \cite{xie_osworld_nodate} Agents are however not to be confused with web-bots which have previously been known as automated software applications or scrapers which are set with a purpose of carrying out specific tasks on the internet, without a higher level of internal judgement. \cite{imperva_rapid_2025} In our research, we refer to this actor simply as an Agent belonging to the distribution $A$.
+
+This economic framing also helps separate two related but distinct phenomena of agents as buyers (changing market demand composition), and agents as information gatherers (changing the observed interactions used by pricing/recommendation systems). The thesis focuses on the second, where information acquisition strategically precedes purchase execution. We do not however dismiss the proposed expectation that existing economic systems serving humans, will not be populated by AIs across multiple channels and with various possibly misaligned goals as stated by \cite{parkes_economic_2015}.
 
-What is the taxonomy and definition of an agent and an actor in this case, a bit more about interaction models in sessions and about dynamic pricing algorithms.
 
 \subsection{Problem Evidence and Market Impact}
-Documented instances of agent-driven market disruptions - Quantitative evidence of pricing manipulation - Case studies from affected industries
 
-\subsection{Theoretical Foundations: Economic Prallels}
+The statistical issue of contamination in dynamic pricing systems that observe demand features as a means to update prices has been documented in various previous contexts. The airline industry (which has accounted for 24\% of observed disruptions) has seen malicious activity with a measureable impact on skewing key performance indicators by behavior visible in the look-to-book metrics. Excessive reconnaissance traffic inflates search volume without corresponding completed bookings, thereby skewing demand forecasts and disrupting dynamic pricing models. Demand proxies have also been observed to cause significant threat to inventory management by creating artificial scarcity that distorts the demand-supply relationships in the enterprise model. Censored demand as shown in \cite{amjad_censored_2017} can also be observed in low-bias demand under-estimation caused by a distortion effect coming from non-human traffic data. \cite{imperva_rapid_2025}
+
+When dynamic pricing algorithms operate on highly contaminated or noisy data, the risk grows significantly in creating inaccurate price inferences. The emergent mitigation driven by un-informed reward and regret signals might lead to price suppression for sales continuity which results in harming margins and resulting in a revenue loss. System that poorly fit undesired behavior might result in price gouging, which calls for strong guardrails while preserving targeted business strategy. \cite{mullapudi_reinforcement_nodate}
+
+
+%Documented instances of agent-driven market disruptions - Quantitative evidence of pricing manipulation - Case studies from affected industries
+
+\subsection{Theoretical Foundations: Economic Parallels}
+
+
+
+Early hints of exploration of prices in a standard English auction explored in \cite{varian_economic_1995} which hints at exploration of prices in a sequential manner, which leads to a marginally different cost to the bidder than the reservation price of the seller. This is a setting in which there is no cost incured by the buyer for their actions or exploring prices in the market. They propose that any agent responsable for the pricing of a good must be imune to dynamic strategies which might extract private information from a market. A key take-away which relates to the Vickery auction mechanism (also called a \textit{direct mechanism}) suggests that not only would defenses against such exploitation be necessary, but the construction of a mechanism in which revelation of the true willingness to pay is the dominant strategy for commerce.
+
+Like in classical revenue-maximizing auctions \cite{roughgarden_cs364a_2013} we assume that the human actor in our system has a private valuation $v$ which we formally draw from later defined distributions. The important note here is that the agent proxy does not have a mechanism to convey this private information into the demand data which directly impacts the pricing systems.
+
+% Economic foundations: relating the problem to options pricing theory. Cost of Information (COI) concept and its relevance
+
+% Link Coasean Singularity and other economic market theory and highlight specific information of supra competitive pricing.
 
-Economic foundations: relating the problem to options pricing theory. Cost of Information (COI) concept and its relevance
 
 \subsection{Landscape of Existing Work}
 

paper/src/chapters/03-methodology.tex

@@ -1,68 +1,251 @@
 \section{Methodology}
 
+This section details the theoretical and practical framework developed to address dynamic pricing under the influence of non-human actors. We begin by formalizing the problem environment and the nature of the actors. We then derive the \textit{Cost of Information} (COI) theorem, proving the erosion of pricing power in the limit of agent saturation. Following this, we outline our generative contamination strategy using GOFAI-driven separability and transition probability learning. Finally, we formulate the robust control problem as a Stackelberg game solved via Distributionally Robust Reinforcement Learning (DR-RL) with constructed ambiguity sets.
 
 \subsection{Problem Formalization}
 
-Mathematical formalization of agent-induced pricing distortions. Formal definition of potential loss mechanisms $\alpha D$
+We define a commercial environment where the platform interacts with a stream of sessions. Let $\mathcal{S}$ denote the set of all sessions. Each session $s \in \mathcal{S}$ is generated by an actor belonging to a latent class $Y_s \in \{H, A\}$, where $H$ denotes Human and $A$ denotes Agent.
 
-We consider a business across time during which we have an evolving vector $p_t \in \Re^N$ where $N$ is the number of products in our catalogue. our price vector is directly dependent on a demand function $q_t$ which we define as a linear method of a price elasticity matrix $B_t$. This is the same setup that Microsoft created in their research.
+Each session produces a trajectory of observable events $\tau_s = (e_{s,1}, \ldots, e_{s,L_s})$. An event $e_{s,k}$ is a tuple defined as:
+\begin{equation}
+e_{s,k} = (a_{s,k}, i_{s,k}, t_{s,k})
+\end{equation}
+where:
+\begin{itemize}
+    \item $a_{s,k} \in \mathcal{A}$ is the action taken (e.g., \texttt{view\_item}, \texttt{add\_to\_cart}).
+    \item $i_{s,k} \in \{1, \ldots, N\}$ is the target item index.
+    \item $t_{s,k} \in \mathbb{R}_+$ is the continuous timestamp.
+\end{itemize}
 
-We gether interaction data from users interacting with a sample platform simulating a hotel/airline which generates interaction distributions $I_t = \{(p_t, q_t^\text{obs}, \pi_t)\}_{t=1}^T$
+The platform does not directly observe the true underlying demand function $d(p)$. Instead, it observes a behavioral proxy $\hat{q}_t$, which is a composite signal derived from the mixture of actor types. We define the demand proxy for product $i$ at epoch $t$ as a weighted aggregation of events:
+\begin{equation}
+\hat{q}_{t,i} = \sum_{s \in \mathcal{S}_t} \sum_{k=1}^{L_s} \omega(a_{s,k}) \cdot \mathbb{1}[i_{s,k} = i]
+\end{equation}
+where $\omega: \mathcal{A} \to \mathbb{R}_+$ assigns weights to actions based on their signal strength regarding willingness to pay.
+
+\subsubsection{Actor Types and Demand Curves}
+We formalize the heterogeneity of actors by introducing a type space $\Theta$. An actor of class $Y_s$ is further parameterized by a type $\theta \sim \mathcal{D}_{Y}$. This type determines the actor's demand response function $d(p; \theta)$, sampled from a distribution of possible demand curves. The total observed demand is a stochastic process governed by the mixture:
+\begin{equation}
+Q(p) = (1-\alpha) \cdot \mathbb{E}_{\theta \sim \mathcal{D}_H}[d(p; \theta)] + \alpha \cdot \mathbb{E}_{\theta \sim \mathcal{D}_A}[d(p; \theta)] + \epsilon_t
+\end{equation}
+where $\alpha \in [0, 1]$ represents the contamination parameter (proportion of agents) and $\epsilon_t$ is non-stationary market noise.
 
 
-\subsection{Cost of Information Framework}
 
-Mathematical demonstration and validation of the COI and citation backed evidence, and framework overview + show harm to user via other cost distortions. Maybe split into 3.2.1 (COI Theory) and 3.2.2 (Framework Design)
+\subsection{Cost of Information (COI) Framework}
+
+The \textit{Cost of Information} (COI) represents the markup a pricing policy $\pi$ attempts to extract from the market by leveraging demand signals. We define COI as the expected premium over the minimum viable price $\underline{p}$ (or marginal cost). This also speaks to the financial urgency as a consequence of information asymmetry between the platform and the actors.
+
+\begin{definition}[Cost of Information]
+Let $\pi(\tau)$ be a pricing policy mapping interaction histories to prices. The COI is defined as:
+\begin{align}
+\text{COI} &= \mathbb{E}[P] - \underline{p} \\
+            &= \int_{\underline{p}}^{\bar{p}} (1 - F_\pi(p)) \, dp
+\end{align}
+where $F_\pi(p)$ is the cumulative distribution function of prices generated by $\pi$ under standard operating conditions.
+\end{definition}
 
-\subsection{System Architecture}
 \begin{figure}[ht]
-\centering
-\begin{tikzpicture}[
-  node distance=1.5cm and 2.5cm,
-  box/.style={rectangle, draw, thick, minimum height=1cm, minimum width=3cm, align=center, fill=blue!10},
-  kafka/.style={rectangle, draw=orange, thick, minimum height=1cm, minimum width=3cm, align=center, fill=orange!15},
-  arrow/.style={thick,->,>=Stealth}
-]
+    \centering
+    \begin{tikzpicture}[scale=1.2]
+        % Define the Gaussian function: centered at 2
+        \def\bellcurve(#1){1.5 * exp(-0.5*((#1-2)/0.6)^2)}
 
-% Nodes
-\node[box] (webapp) {Web Application \\ (Producer \& Consumer)};
-\node[kafka, below=of webapp] (kafka) {Apache Kafka \\ Cluster};
-\node[box, below=of kafka] (backend) {Backend Services / Microservices \\ (Producers and Consumers)};
+        % Draw the main axis
+        \draw[->, thick] (0, 0) -- (4.5, 0) node[right] {$p$};
+        \draw[->, thick] (0, 0) -- (0, 2) node[above] {Density};
 
-% Connections
-\draw[arrow] (webapp) to[out=210,in=150] node[above]{Publish} (kafka);
-\draw[arrow] (kafka) to[out=50,in=330] node[below]{Consume} (webapp);
-\draw[arrow] (backend) -- node[above]{Publish/Consume} (kafka);
+        \draw[thick, smooth, samples=100] plot[domain=0:4] (\x, {\bellcurve(\x)});
+        \node at (3.2, 1.2) {$f_\pi(p)$};
 
-% Optional: Kafka internal components
-%\node[below=0.7cm of kafka, align=center] (topics) {Topics \\ Partitions};
+        % Define p_min and E[p]
+        \def\pmin{0.8}
+        \def\mean{2}
 
-% Optional background
-\begin{scope}[on background layer]
-  \node[draw, rounded corners, fill=orange!5, fit=(kafka), inner sep=0.3cm] {};
-\end{scope}
-\end{tikzpicture}
-\caption{Technical Diagram}
+        % Vertical lines
+        \draw[dashed] (\pmin, 0) -- (\pmin, 2.0);
+        \draw[dashed] (\mean, 0) -- (\mean, 2.0);
+
+        % Labels on axis
+        \node[below] at (\pmin, 0) {$\underline{p}$};
+        \node[below] at (\mean, 0) {$\mathbb{E}[p]$};
+
+        \draw[<->, thick, red] (\pmin, 2.0) -- (\mean, 2.0) node[midway, above] {COI};
+
+    \end{tikzpicture}
+    \caption{Illustration of the Cost of Information (COI). The COI is defined as the difference between the expected price $\mathbb{E}[p]$ realized by the policy and the minimum viable price $\underline{p}$.}
+    \label{fig:coi_illustration}
 \end{figure}
 
-High level overview of how it works
+We now formally demonstrate that standard dynamic pricing mechanisms are not incentive-compatible with high-frequency agentic traffic. As the number of independent competitive agents $N$ querying the system grows, the platform's ability to sustain a COI vanishes.
+
+\begin{theorem}[COI Erosion in the Limit]
+Let $N$ be the number of independent, utility-maximizing agents querying the platform. Let $p_{(1)}$ be the first order statistic (minimum) of the prices offered to these agents. As $N \to \infty$, the Cost of Information converges to 0.
+\end{theorem}
+
+\begin{proof}
+Let $p_1, \ldots, p_N$ be independent and identically distributed (i.i.d.) price samples drawn from the policy's distribution $F(p)$ with support $[\underline{p}, \bar{p}]$. The realizable price for an optimal searching agent is the first order statistic $p_{(1)} = \min(p_1, \ldots, p_N)$.
+
+The survival function (or reliability function) of the minimum price is given by:
+\begin{equation}
+S_{p_{(1)}}(t) = P(p_{(1)} > t) = [1 - F(t)]^N
+\end{equation}
+
+To determine the expected value $\mathbb{E}[p_{(1)}]$, we recall the property that for any continuous random variable $X$ with support $[A, B]$, the expectation can be expressed as the lower bound plus the integral of the survival function:
+\begin{equation}
+\mathbb{E}[X] = A + \int_{A}^{B} P(X > t) \, dt
+\end{equation}
+
+Applying this to our pricing statistic where the lower bound is $\underline{p}$:
+\begin{align}
+\mathbb{E}[p_{(1)}] &= \underline{p} + \int_{\underline{p}}^{\bar{p}} P(p_{(1)} > t) \, dt \\
+&= \underline{p} + \int_{\underline{p}}^{\bar{p}} [1 - F(t)]^N \, dt
+\end{align}
+
+Since $F(t)$ is a valid CDF, for any $t > \underline{p}$, we have strict inequality $F(t) > 0$, implying $0 \le 1 - F(t) < 1$. By the properties of limits, as $N \to \infty$, the term $[1 - F(t)]^N$ converges to 0 pointwise for all $t > \underline{p}$.
+
+Applying the Lebesgue Dominated Convergence Theorem (noting that the integrand is bounded by 1 on the finite interval $[\underline{p}, \bar{p}]$):
+\begin{equation}
+\lim_{N \to \infty} \int_{\underline{p}}^{\bar{p}} [1 - F(t)]^N \, dt = \int_{\underline{p}}^{\bar{p}} 0 \, dt = 0
+\end{equation}
+
+Substituting this back into the expression for COI:
+\begin{align}
+\lim_{N \to \infty} \text{COI} &= \lim_{N \to \infty} (\mathbb{E}[p_{(1)}] - \underline{p}) \\
+&= \lim_{N \to \infty} \left( (\underline{p} + 0) - \underline{p} \right) \\
+&= 0
+\end{align}
+\end{proof}
+
+
+This result proves that standard pricing policies $\pi$ fail to extract surplus in the presence of large-scale agentic search, necessitating a robust counter-mechanism.
+
+% The DRO objective creates a lower bound on COI extraction, effectively guaranteeing a minimum margin even in the presence of adversarial agents. we need to prove this and demonstrate that in a theorem.
+
+
+%Mathematical demonstration and validation of the COI and citation backed evidence, and framework overview + show harm to user via other cost distortions. Maybe split into 3.2.1 (COI Theory) and 3.2.2 (Framework Design)
+
+\subsection{System Architecture: Hybrid Kappa-Lambda Architecture}
+
+In order for our research to have grounding in interactions we built a robust e-commerce web-platform. We initially conducted a survey of the leading platforms of airlines and hotel booking sites to identify the specific interface patterns that effectively manage complex travel data. Our analysis revealed a clear industry standard: while both sectors rely on tabbed service selection and left-sidebar filtering to streamline navigation, they diverge in result presentation: airlines utilize visual date-price bars and multi-step wizards to optimize for logistical transparency, whereas hotel platforms leverage image-led cards and scarcity triggers to drive emotional engagement and urgency. Our web framework defines a highly agnostic boilerplate which can be seeded with any data-modality with an easy-to-tailor pattern, which we leverage to define a \texttt{hotel} and \texttt{airline} mode. Both modes are then individually deployed via an environment level argument which adjusts the proxy routing with a custom middleware inside next.js to render only the desired mode. The purpose of this was to create a baseline adaptable to any use-case or desired commercial application.
+
+
+The architecture of this platform begins with the deployed web-apps posting interaction data to our backend which processes them and stores each ingested interaction into a kafka cluster. This serves as our data reservoir tracking and associating each interaction with its session and importantly with which experiment it belongs to. Not only do we track the behavioral interactions, but our pricing provider micro-service, once called by the frontend reports the observed/queried price-product into kafka. This kafka cluster is subscribed to by our pipeline which is configured on a schedule in Airflow, with the possibility of manual trigger. The final stage of the pricing pipeline, submits computed dynamic pricing results into a redis database for quick updates which is then read by the pricing provider and displayed on the webapp. This is a very generic end-to-end mechanism which is applicable to a variety of different e-commerce tasks. We intentionally put emphasis on the development of this infrastructure to establish a reproducible framework for interaction and to minimize any noise.
+
+
+\subsubsection{DevOps Principles}
+
+\subsubsection{Online Dynamic Pricing}
+
+The dynamic pricing done is handled by a pipeline which computes a demand estimate on a per-product basis of a specific window of the data, defined by the period $T$ which by default is 5 minutes. This dynamic pricing pipeline computes a demand estimate vector $\hat{q} \in \mathbb{R}^N$ by a weighted sum of interactions for each product, it additionally computes a price elasticity vector $\hat{\epsilon}$ in the same dimensions as our demand. The final features matrix is of the size $N \times 2$ which we translate to a new price vector $\hat{p} \in \mathbb{R}^N$. The transformation that governs this dynamic pricing is a very simple surge-based pricing (a special case of our later defined policy $\pi$):
+
+\begin{equation}
+\hat{p}_i = \begin{cases}
+p_{0,i} \cdot \lambda_{\text{surge}} & \text{if } \hat{q}_i \geq \theta_{\text{high}} \\
+p_{0,i} \cdot \lambda_{\text{disc}} & \text{if } \hat{q}_i \leq \theta_{\text{low}} \\
+p_{0,i} & \text{otherwise}
+\end{cases}
+\quad \forall i \in \{1, \ldots, N\}
+\end{equation}
+
+where $p_0 \in \mathbb{R}^N$ is the base price vector (which is seeded into our database distinctly for each mode of the commerce platform), $\theta_{\text{high}}, \theta_{\text{low}} \in \mathbb{R}$ are demand thresholds defining surge and discount regions, and $\lambda_{\text{surge}}, \lambda_{\text{disc}} \in \mathbb{R}^+$ are multiplicative factors with typical values $\lambda_{\text{surge}} = 1.2$ and $\lambda_{\text{disc}} = 0.9$. This piecewise function enables rapid price adjustment in response to observed demand without requiring complex elasticity estimation or historical calibration, allowing us to expose actors within our experiments to a system with a dynamic component of pricing.
+
+We will for our offilne experimental intents generalize a master function for encompasing distinct demand estimation and pricing strategies.
+
+\begin{align}
+V(\cdot) = \max_{p_t} \min_{Q \in \mathcal{U}(\hat{d})}{\mathbb{E}_{d\sim Q} [p_t \times d(p_t, x_t ; \theta) + \psi V_{t+1}(\cdot)]}
+\end{align}
+
+We follow differnet substitutouns which will server as hyperparameters later on.
+
 \subsection{Experimental Design}
-Study methodology and approach. Data acquisition strategy. Defined objectives and success criteria. Observable metrics and KPIs
 
-\subsection{Dynamic Pricing Algorithm Analysis}
-Deep dive into how the algorithm works, different kinds and justification for chosen appraoches + agent impact modeling and quantification.
-\subsection{Reinforcement Learning Formulation}
-How do we define the state space, action space and reward function breakdown and algorithm benchmarking.
-POSSIBLY: Expand into full subsections: 3.6.1 (State-Action Space), 3.6.2 (Reward Design), 3.6.3 (Benchmarking)
+The experimentation begins with the design of goals, with careful consideration to assure a uniform spanning across different variables within each product-architecture of either the hotel or airline platforms. Our crafted collection of goals (jobs to be done) is then tracked in a postgress database with one table to track goals and another table to track different experiment runs, and their associated goals in a experiment-goal one-to-one relationship.
+
+The purpose of this effort to gather data on interactions, is the first half of our research. With this collected data on behavioral characteristics, enhanced by our feature augmentation, we can create distribution separation into two bins $y \in \{A,H\}$ with a certain probability $p$ dependent on the session-specific features. To address the second loop of our system, we use this gained capability of discrimination to enhance the learner design involved in our surrogate dynamic pricing task which simulates an independent dynamic pricing scenario under which we can train a more controlled policy with the ability to account for true demand signals under conditions of contamination from non-human actors.
 
 
-\begin{algorithm}[t]
-\DontPrintSemicolon
-\KwIn{stepsize $\eta$, smoothing $\delta$, rank $d$}
-\For{$t=1$ \KwTo $T$}{
-  Sample $u_t$ on unit sphere; set $x_t^\prime=x_t+\delta u_t$\;
-  Set $p_t \gets U x_t^\prime$ and observe $q_t, R_t(p_t)$\;
-  $x_{t+1} \gets \Pi\_{\mathcal{X}}(x_t-\eta R_t(p_t) u_t)$\;
-}
-\caption{Online Pricing Optimization (template)}
-\end{algorithm}
+Our approach can be well summarized by a three-stage division, first we intend to observe and \textit{vectorize} the behavioral interaction data from our experiments, we then develop the separability which helps us deepen the semantic understanding of the behavioral patterns. Finally we use our newly gained learner to leverage a defensive mechanism within the simulation stage of a controlled dynamic pricing loop.
+
+\begin{figure}[ht]
+  \resizebox{\columnwidth}{!}{%
+    \input{chapters/loop_figure.tex}
+  }
+  \caption{Overview of the Dynamic Pricing Tasks.}
+\end{figure}
+
+
+Study methodology and approach. Data acquisition strategy. Defined objectives and success criteria. Observable metrics and KPIs.
+
+
+\subsection{Generative Contamination and Separability}
+
+To develop a robust pricing agent, we require a simulation environment capable of generating realistic, contaminated interaction data. We achieve this by learning from our Phantom platform data using a two-stage approach.
+
+
+
+\subsubsection{GOFAI-Based Separability}
+We employ Good Old-Fashioned AI (GOFAI) heuristics to generate initial weak labels for separability. We define a set of rule-based predicates $\phi_j: \tau \to \{0, 1\}$ to partition the dataset $\mathcal{D}$ into high-confidence sets $\mathcal{D}_H$ and $\mathcal{D}_A$. We construct distinct MDPs per each behavioral profile of humans and agents and from those we establish $D_{KL}$. From initial findings we compute a KL divergence of $\approx 2.0236$ across transition probabilities between states which can be seen in \ref{fig:human_mdp_viz} and \ref{fig:agent_mdp_viz}.
+
+\begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.8\textwidth]{chapters/mdp_human.pdf}
+    \caption{Markov Decision Process visualization illustrating the behavioral transition dynamics for human actions.}
+    \label{fig:human_mdp_viz}
+\end{figure}
+
+\begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.8\textwidth]{chapters/mdp_agent.pdf}
+    \caption{Markov Decision Process visualization illustrating the behavioral transition dynamics for \textbf{agent} behavior profiles. The state space and transition probabilities are learned from observed session trajectories to enable generative contamination.}
+    \label{fig:agent_mdp_viz}
+  \end{figure}
+
+\subsubsection{Transition Probability Estimation}
+For both subsets, we model the session dynamics as a Markov Decision Process (MDP) and estimate the transition kernel $\mathcal{T}$. The probability of transitioning to state $s'$ given state $s$ is estimated via maximum likelihood:
+\begin{equation}
+    \hat{P}(s' \mid s) = \frac{N(s, s')}{\sum_{k \in \mathcal{S}} N(s, k)}
+\end{equation}
+where $N(s, s')$ is the count of observed transitions. This allows us to construct a \textit{Contamination Generator} $\mathcal{G}(\alpha)$. Given a clean trajectory dataset, $\mathcal{G}$ injects synthetic agent trajectories sampled from the learned transition matrix $\hat{P}_A$ until the effective mixing ratio reaches $\alpha$.
+
+\subsection{Distributionally Robust Reinforcement Learning (DR-RL)}
+
+We formulate the pricing problem as a Stackelberg Game where the Platform (Leader) sets prices $p_t$ and the Aggregate Demand (Follower) responds. However, the exact mixing parameter $\alpha$ and the demand distribution shift are non-stationary and unknown in online settings. Relying on a simple error term $\epsilon$ is insufficient. Instead, we adopt a Distributionally Robust Optimization (DRO) objective.
+
+\subsubsection{Ambiguity Set Construction}
+We define an ambiguity set $\mathcal{U}_p(\hat{P}_N)$ centered around our empirical reference distribution $\hat{P}_N$ (derived from the generator $\mathcal{G}$). We utilize the Wasserstein distance metric to define the set of plausible demand distributions the agent might face:
+\begin{equation}
+\mathcal{U}_\epsilon(\hat{P}_N) = \left\{ Q \in \mathcal{P}(\Xi) : W_p(Q, \hat{P}_N) \le \epsilon \right\}
+\end{equation}
+This set captures all distributions that are statistically close to our observed training data but allows for adversarial shifts (e.g., sudden bot spikes).
+
+\subsubsection{The Min-Max Objective}
+The robust policy $\pi^*$ is obtained by solving the maximin problem:
+\begin{equation}
+\pi^* = \arg \max_{\pi} \min_{Q \in \mathcal{U}_\epsilon} \mathbb{E}_{d \sim Q} \left[ R(p, d) - \lambda \cdot \text{COI}(p) \right]
+\end{equation}
+where $R(p, d)$ is the revenue function and $\lambda$ weighs the penalty for information leakage (COI).
+
+\subsubsection{Actor Implementation}
+In our simulation, the "Follower" is implemented as a set of Actors. Each Actor is initialized with a type $\theta$ which samples a specific demand curve $d(p; \theta)$ from the latent distribution. This formalization ensures that our DR-RL agent does not overfit to a single deterministic demand function but learns a policy robust to the distributional uncertainty defined by $\mathcal{U}_\epsilon$.
+
+
+As part of our reward engineering we think about the UX factor ($UX \in [0,1]$) whic his our proxy for user experience degradation, this is computed as a mixture of contribution from the separability model metric of $\frac{1}{\text{Specificity}}$.
+
+\begin{figure}[ht]
+  \centering
+  \resizebox{0.5\columnwidth}{!}{%
+    \input{chapters/balance_figure.tex}
+  }
+  \caption{Introducing the UX index allows us to better distinguish the kind of impact different methods have and allows us to compare them on this Pareto-like scale.}
+\end{figure}
+
+We also need to think about a policy like taxation to the agents Strategy-Proof Mechanism Design, specifically the Vickrey-Clarke-Groves (VCG) payment rule. We link and prove that this would create an incentive for the dominant strategy to become truth-telling.
+
+\section{Heuristics as part of neuro-inspired steering systems}
+
+Steve Burns, superior culliculus (face heuristics) we create this sort of part of the 'brain' + amortized inference.
+
+We could say that a DQN for example is the learnin subsystem and then within our reward mechanism or some other computational method we introduce a steering subsystem which acts as the proposed ``pricing heuristic'' against the given non human transaction data.
+
+\section{Market construction}

paper/src/chapters/05-discussion.tex

@@ -1,5 +1,15 @@
 \section{Discussion}
 
+\subsection{Transition to Agentic Market Microstructure}
+
+Our analysis of the interaction dynamics between the platform and non-human actors suggests that the current static pricing models are insufficient for an agent-mediated economy. If we assume a transition toward a direct revelation mechanism, where actors must reveal their true valuation of a good through bidding dynamics, we inevitably introduce significant stochasticity into the pricing system. Unlike traditional e-commerce where prices are relatively sticky, such a mechanism implies a high volatility characteristic of financial equity markets (without the fungability however).
+
+However, ecommerce commodities differ fundamentally from financial securities: they possess a hard floor defined by unit economics and reservation prices. The market might react enthusiastically to an iPhone priced at \$1, such a transaction is not permissible. The platform must establish an initial valuation anchor ($P_{0}$) defined by the marginal cost plus a target margin, around which the market price is permitted to fluctuate. We propose the introduction of GenAI Agents as Institutional Market Makers.
+
+This is also under the assumption of expected transactional capabilities being given to AI Agents.
+
+
+
 \subsection{Risk Assessment and Limitations}
 
 Acknowledge risks and constraints and data sizes.

paper/src/chapters/06-conclusion.tex

@@ -1,6 +1,6 @@
 \section{Conclusion}
 
-\subsection{Summary of contributions }
+\subsection{Summary of contributions}
 Restate the thesis and key findings with validation of research objectives.
 
 \subsection{Future Works and Next Steps}

paper/src/chapters/balance_figure.tex

@@ -0,0 +1,38 @@
+
+\begin{tikzpicture}[
+    % Styles for consistency
+    axis/.style={->, >=Stealth, line width=1.2pt, color=black!85},
+    curve/.style={color=black, line width=2.5pt},
+    point/.style={circle, fill=black, inner sep=0pt, minimum size=6pt},
+    label_text/.style={font=\large, align=center, color=black},
+    annotation_line/.style={thick, -, color=black!60}
+]
+
+    % Define Radius
+    \def\R{5}
+
+    % Draw Axes
+    % Extended slightly beyond radius (\R + 1)
+    \draw[axis] (0,0) -- (\R+1.5,0) node[midway, below=10pt, font=\bfseries\large] {UX Index};
+    \draw[axis] (0,0) -- (0,\R+1.5) node[midway, left=15pt, rotate=90, font=\bfseries\large] {Performance};
+
+    % Draw Perfect 1/4 Circle
+    % Syntax: arc (start_angle : end_angle : radius)
+    \draw[curve] (0,\R) arc (90:0:\R);
+
+    % 1. Paranoid (High Performance side) -> Angle 67.5 degrees
+    \node[point] (p1) at (75:\R) {};
+    \node[label_text, above right=0.1cm and 0.1cm of p1] (l1) {Paranoid};
+    \draw[annotation_line] (l1) -- (p1);
+
+    % 2. Perfect Detection (Exact Middle) -> Angle 45 degrees
+    \node[point] (p2) at (45:\R) {};
+    \node[label_text, above right=0.2cm and 0.2cm of p2] (l2) {Perfect Detection};
+    \draw[annotation_line] (l2) -- (p2);
+
+    % 3. No Detection (High UX side) -> Angle 22.5 degrees
+    \node[point] (p3) at (15:\R) {};
+    \node[label_text, right=0.5cm of p3] (l3) {No Detection};
+    \draw[annotation_line] (l3) -- (p3);
+
+\end{tikzpicture}

paper/src/chapters/feature_table.tex

@@ -0,0 +1,65 @@
+\begin{table}[ht]
+\centering
+\small
+\resizebox{\columnwidth}{!}{%
+\begin{tabular}{p{4.5cm}p{1.5cm}p{6cm}}
+\hline
+\textbf{Feature} & \textbf{Type} & \textbf{Description} \\
+\hline
+\multicolumn{3}{l}{\textit{Session Identifiers}} \\
+sessionId & object & Unique identifier for user session \\
+experimentId & object & Experiment run identifier \\
+\hline
+\multicolumn{3}{l}{\textit{Temporal Features}} \\
+session\_duration\_sec & float & Total session duration in seconds \\
+avg\_time\_between\_events & float & Mean inter-event time \\
+std\_time\_between\_events & float & Standard deviation of inter-event times \\
+min\_time\_between\_events & float & Minimum time between consecutive events \\
+session\_start\_hour & int & Hour of day when session started \\
+\hline
+\multicolumn{3}{l}{\textit{Interaction Metrics}} \\
+total\_interactions & int & Count of all user interactions \\
+total\_events & int & Total number of tracked events \\
+interaction\_velocity & float & Rate of interactions per time unit \\
+max\_velocity\_5min & int & Peak interaction count in any 5-minute window \\
+\hline
+\multicolumn{3}{l}{\textit{Navigation Behavior}} \\
+unique\_pages & int & Number of distinct pages visited \\
+page\_views & int & Total page view events \\
+\hline
+\multicolumn{3}{l}{\textit{Product Engagement}} \\
+item\_views & int & Number of product detail views \\
+unique\_products\_viewed & int & Count of distinct products examined \\
+product\_view\_depth & int & Repeat views of same products \\
+\hline
+\multicolumn{3}{l}{\textit{Conversion Funnel}} \\
+cart\_adds & int & Number of items added to cart \\
+purchases & int & Completed transactions \\
+cart\_to\_view\_ratio & float & Ratio of cart additions to item views \\
+conversion\_rate & float & Purchase to view conversion \\
+\hline
+\multicolumn{3}{l}{\textit{Interaction Quality}} \\
+hover\_events & int & Mouse hover event count \\
+hover\_intensity & float & Hover events per interaction \\
+\hline
+\multicolumn{3}{l}{\textit{Price Behavior}} \\
+avg\_price\_seen & float & Mean price across viewed products \\
+min\_price\_seen & float & Lowest price encountered \\
+max\_price\_seen & float & Highest price encountered \\
+price\_range & float & Difference between max and min prices seen \\
+\hline
+\multicolumn{3}{l}{\textit{Technical Fingerprinting}} \\
+is\_headless & bool & Headless browser detection flag \\
+is\_automation & bool & Automation framework detection flag \\
+browser\_family & object & Browser type classification \\
+\hline
+\multicolumn{3}{l}{\textit{Experimental Labels}} \\
+is\_agent & bool & Ground truth agent classification \\
+xp\_human\_only & bool & Human-only experiment indicator \\
+xp\_market\_mode & object & Market context (hotel/airline) \\
+\hline
+\end{tabular}%
+}
+\caption{Feature matrix schema for session-level behavioral classification (32 features total).}
+\label{tab:features}
+\end{table}

paper/src/chapters/loop_figure.tex

@@ -0,0 +1,110 @@
+\definecolor{mygreenfill}{RGB}{169, 234, 186}
+\definecolor{mygreenborder}{RGB}{29, 145, 61}
+\definecolor{mybluefill}{RGB}{204, 222, 255}
+\definecolor{myblueborder}{RGB}{66, 106, 189}
+\definecolor{mygray}{RGB}{150, 150, 150}
+
+
+
+\begin{tikzpicture}[
+    node distance=2cm,
+    % Style for Green Nodes
+    greenbox/.style={
+        rectangle,
+        draw=mygreenborder,
+        fill=mygreenfill,
+        line width=1.2pt,
+        align=center,
+        minimum height=1cm
+    },
+    % Style for Blue Nodes
+    bluebox/.style={
+        rectangle,
+        draw=myblueborder,
+        fill=mybluefill,
+        line width=1.2pt,
+        align=center,
+        minimum height=1cm
+    },
+    % Style for Arrows
+    myarrow/.style={
+        ->,
+        >={Stealth[length=3mm, width=2mm]},
+        draw=black!80,
+        line width=1.2pt,
+        rounded corners=5pt
+    },
+    % Style for Background Dashed Circles
+    dashedloop/.style={
+        dashed,
+        draw=mygray,
+        line width=1pt
+    }
+]
+
+    % --- Coordinate Layout ---
+    % Defining a grid relative to the center
+
+    % Left Loop (Green) Nodes
+    \node[greenbox, minimum width=3.5cm] (commerce) at (-3.5, 2) {Commerce Experiment};
+    \node[greenbox, minimum width=1.5cm] (raw) at (-6.5, 0) {Raw\\Logs};
+    \node[greenbox, minimum width=1.5cm] (features) at (-4, -2.5) {Features};
+    \node[greenbox, minimum width=2.5cm] (classification) at (-1, -0.5) {Classification\\Training A/H};
+
+    % Right Loop (Blue) Nodes
+    \node[bluebox, minimum width=2.5cm] (trainedpricing) at (3.2, 2) {Trained Pricing};
+    \node[bluebox, minimum width=2.5cm] (policy) at (6.5, 0) {Trained Pricing\\Policy};
+    \node[bluebox, minimum width=2.5cm] (rlgym) at (3.2, -2.2) {RL Gym\\Training};
+
+    % --- Background Dashed Loops ---
+    \begin{scope}[on background layer]
+        % Left Loop Circle
+        \draw[dashedloop] (-3.5, 0) ellipse (3.5cm and 2.8cm);
+        % Right Loop Circle
+        \draw[dashedloop] (3.5, 0) ellipse (3.5cm and 2.8cm);
+    \end{scope}
+
+    % --- Arrows: Loop One (Green) ---
+    % Commerce -> Raw Logs
+    \draw[myarrow] (commerce.west) to[out=180, in=90] (raw.north);
+
+    % Raw Logs -> Features
+    \draw[myarrow] (raw.south) to[out=270, in=180] (features.west);
+
+    % Features -> Classification
+    \draw[myarrow] (features.east) to[out=0, in=250] (classification.south);
+
+    % Classification -> Commerce (Closing the loop)
+    \draw[myarrow] (classification.north) to[out=110, in=0] (commerce.east);
+
+    % --- Arrows: Loop Two (Blue) ---
+    % Classification (Green) -> RL Gym (Blue) - Crossing over
+    \draw[myarrow] (classification.east) to[out=0, in=180] (rlgym.west);
+
+    % RL Gym -> Policy
+    \draw[myarrow] (rlgym.east) to[out=0, in=270] (policy.south);
+
+    % Policy -> Trained Pricing
+    \draw[myarrow] (policy.north) to[out=90, in=0] (trainedpricing.east);
+
+    % Trained Pricing -> Commerce (Crossing back)
+    \draw[myarrow] (trainedpricing.west) -- node[above, font=\small, yshift=2pt] {New Pricing} (commerce.east);
+
+    % --- Text Labels ---
+
+    % Loop One Label
+    \node[align=center] at (-3.8, 0) {Loop One:\\Data \textit{(Online)}};
+
+    % Loop Two Label
+    \node[align=center] at (3.5, 0) {Loop Two:\\Defense Gym \textit{(Offline)}};
+
+    % Bottom Legend
+    \node[font=\small] (taskA) at (-4, -4) {Dynamic Pricing Task A};
+    \node[font=\small] (taskB) at (4, -4) {Dynamic Pricing Task B};
+    \node[font=\small] (indep) at (0, -4) {Independent};
+
+    % Arrows for bottom legend
+    \draw[->, >=Stealth, thick, darkgray] (indep.west) -- (taskA.east);
+    \draw[->, >=Stealth, thick, darkgray] (indep.east) -- (taskB.west);
+
+\end{tikzpicture}

paper/src/main.tex

@@ -1,39 +1,30 @@
 % -*- TeX-master: t -*-
-\documentclass[sigconf,nonacm,natbib=false]{acmart}
+\documentclass[12pt,letterpaper]{article}
 
-% Remove ACM copyright/conference info for thesis
-\settopmatter{printacmref=false}
-\renewcommand\footnotetextcopyrightpermission[1]{}
 \pagestyle{plain}
 
 \input{preamble}
 
 \begin{document}
 
-\title{Pricing Heuristics Against Non-human Transaction Orchestration Mechanisms}
+\title{Adversarially Distributionally Robust Optimization and Reinforcement Learning for Informed Dynamic Pricing under Strategic Demand Contamination}
 
-\author{Daniel Rösel}
-\email{daniel@alves.world}
-\affiliation{%
-  \institution{IE University}
-  \city{Madrid}
-  \country{Spain}
+\author{
+  Daniel Rösel\thanks{Primary author and student researcher. Email: daniel@alves.world} \\
+  IE University, Madrid, Spain \\[1em]
+  Alberto Martín Izquierdo\thanks{Thesis advisor. Email: amartini@faculty.ie.edu} \\
+  IE University, Madrid, Spain
 }
 
-\author{Alberto Martín Izquierdo}
-\email{amartini@faculty.ie.edu}
-\affiliation{%
-  \institution{IE University}
-  \city{Madrid}
-  \country{Spain}
-}
-
-\begin{abstract}
-The primary objective of this thesis is to develop and validate pricing heuristics that protect e-commerce platforms from systematic exploitation by Large Language Model (LLM) agents within dynamic pricing environments. As AI agents increasingly mediate consumer transactions, they enable users to circumvent the Cost of Information (the price premium accumulated through demand signal expression) by conducting reconnaissance in isolated sessions before executing purchases through clean sessions at base prices. This research will make an anticipatory contribution by adapting recommendation system methodologies to distinguish between genuine human browsing behaviour and agent-orchestrated information gathering, thereby enabling pricing systems to maintain margin integrity without degrading the user experience for legitimate customers or getting rid of leads generated by LLMs.
-\end{abstract}
+\date{\today}
 
 \maketitle
 
+\begin{abstract}
+The primary objective of this thesis is to develop and validate pricing heuristics that protect e-commerce platforms from systematic exploitation by Large Language Model (LLM) agents within dynamic pricing environments. As AI agents increasingly mediate consumer transactions, they enable users to circumvent the Cost of Information (the price premium accumulated through demand signal expression) by conducting reconnaissance in isolated sessions before executing purchases through clean sessions at base prices. This research will make an anticipatory contribution by adapting recommendation system methodologies to distinguish between genuine human browsing behavior and agent-orchestrated information gathering, thereby enabling pricing systems to maintain margin integrity without degrading the user experience for legitimate customers or getting rid of leads generated by LLMs.
+\end{abstract}
+
+
 \input{chapters/01-intro}
 \input{chapters/02-literature-review}
 \input{chapters/03-methodology}
@@ -42,11 +33,19 @@ The primary objective of this thesis is to develop and validate pricing heuristi
 \input{chapters/06-conclusion}
 
 
+\section*{Acknowledgments}
+Eugene Bykovets, PhD - ETH for helping with problem formulation.
+Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC).
+
 \printbibliography
 
 \clearpage
-\onecolumn
 \appendix
+\section{Terminology}
+\begin{description}
+\item[Agent $A$] An actor of non-human nature, powered by an LLM.
+\item[Human $H$] An individual human with some job to be done.
+\end{description}
 \input{../build/concatenated_code}
 
 \end{document}

paper/src/preamble.tex

@@ -1,6 +1,25 @@
-% acmart already includes: graphicx, hyperref, booktabs, amsmath, natbib
-% Only load packages not included in acmart
+% Math packages (load before fonts to avoid conflicts)
+\usepackage{amsmath}
+\usepackage{amsthm}
 
+% Define theorem environments
+\newtheorem{theorem}{Theorem}
+\newtheorem{definition}{Definition}
+\newtheorem{lemma}{Lemma}
+\newtheorem{corollary}{Corollary}
+
+% Font and spacing
+\usepackage{newtxtext,newtxmath}
+\usepackage{setspace}
+\doublespacing
+
+% Page geometry
+\usepackage[margin=1in]{geometry}
+
+% Essential packages
+\usepackage{graphicx}
+\usepackage{hyperref}
+\usepackage{booktabs}
 \usepackage{csquotes}
 \usepackage{subcaption}
 \usepackage{siunitx}
@@ -8,6 +27,10 @@
 \usepackage{listings}
 \usepackage{xcolor}
 \usepackage[ruled,vlined]{algorithm2e}
+\usepackage{cleveref}
+
+% Configure cleveref for algorithm2e
+\crefname{algocf}{Algorithm}{Algorithms}
 
 \usetikzlibrary{positioning, shapes, arrows.meta, fit, backgrounds}
 \lstset{

sim/case/__init__.py

@@ -0,0 +1,2 @@
+"""Case-specific simulations and experiments."""
+

sim/case/thesis_simplified/__init__.py

@@ -0,0 +1,2 @@
+"""Minimal thesis-aligned pricing simulation (self-contained)."""
+

sim/case/thesis_simplified/coi.py

@@ -0,0 +1,125 @@
+"""Cost of Information (COI) computation for thesis pricing system.
+
+Core KPI: COI = E[p_shown] - p_min measures pricing power from information asymmetry.
+Theorem 1 shows COI erodes as agent queries increase: as N->inf, p^(1)->p_min.
+"""
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Dict, List, TYPE_CHECKING
+import numpy as np
+
+if TYPE_CHECKING:
+    from .simplified import Session
+
+
+@dataclass(frozen=True)
+class COIWindow:
+    """Windowed COI metrics computed from realized price exposures.
+
+    policy: E[p_shown] - cost, the definition-level KPI
+    agent: E[p^(1)] - cost where p^(1) is min price under agent querying
+    leak: max(policy - agent, 0), observable gap from reconnaissance
+    survival_ratio: agent/policy, fraction of pricing power retained
+    """
+    policy: float
+    agent: float
+    leak: float
+    survival_ratio: float
+    policy_by_product: np.ndarray
+    agent_by_product: np.ndarray
+    demand_weights: np.ndarray
+
+
+def aggregate_prices(sessions: List["Session"], mode: str = "all") -> Dict[int, List[float] | float]:
+    """Unified price aggregation across sessions.
+
+    mode: "all" returns all prices per product, "min_per_session" returns min price per session per product,
+          "min_across" returns single min price per product
+    """
+    if mode == "min_across":
+        mins: Dict[int, float] = {}
+        for s in sessions:
+            for e in s.events:
+                pidx, price = int(e.product_idx), float(e.price_seen)
+                mins[pidx] = min(mins.get(pidx, price), price)
+        return mins
+    elif mode == "min_per_session":
+        result: Dict[int, List[float]] = {}
+        for s in sessions:
+            by_p: Dict[int, float] = {}
+            for e in s.events:
+                pidx, price = int(e.product_idx), float(e.price_seen)
+                by_p[pidx] = min(by_p.get(pidx, price), price)
+            for pidx, pmin in by_p.items():
+                result.setdefault(pidx, []).append(pmin)
+        return result
+    else:  # "all"
+        prices: Dict[int, List[float]] = {}
+        for s in sessions:
+            for e in s.events:
+                prices.setdefault(e.product_idx, []).append(float(e.price_seen))
+        return prices
+
+
+def demand_weights_by_product(sessions: List["Session"], demand_mapping: Dict[str, float], n_products: int) -> np.ndarray:
+    """Compute demand-weighted importance per product."""
+    w = np.zeros(n_products, dtype=float)
+    sessions_by_id = {s.sid: s for s in sessions}
+    for sid, q in demand_mapping.items():
+        sess = sessions_by_id.get(sid)
+        if sess and sess.events:
+            w[int(sess.events[0].product_idx)] += float(q)
+    total = float(np.sum(w))
+    return (w / total) if total > 0 else w
+
+
+def compute_coi_window(sessions: List["Session"], costs: np.ndarray, demand_mapping: Dict[str, float] | None = None) -> COIWindow:
+    """Compute COI metrics over session window.
+
+    Aggregates price exposures and computes policy-level vs agent-realized COI.
+    """
+    n = int(len(costs))
+    prices = aggregate_prices(sessions, mode="all")
+    agent_sessions = [s for s in sessions if s.actor == "A"]
+    agent_min = aggregate_prices(agent_sessions, mode="min_across") if agent_sessions else {}
+
+    policy_by = np.zeros(n, dtype=float)
+    agent_by = np.zeros(n, dtype=float)
+    seen = np.array([(i in prices) for i in range(n)], dtype=bool)
+    agent_seen = np.array([(i in agent_min) for i in range(n)], dtype=bool)
+
+    for pidx, ps in prices.items():
+        if 0 <= pidx < n and ps:
+            policy_by[pidx] = float(np.mean(ps) - float(costs[pidx]))
+    for pidx, pmin in agent_min.items():
+        if 0 <= pidx < n:
+            agent_by[pidx] = float(pmin - float(costs[pidx]))
+
+    agent_by[seen & ~agent_seen] = policy_by[seen & ~agent_seen]  # no erosion if no agent exposure
+
+    demand_w = demand_weights_by_product(sessions, demand_mapping, n) if demand_mapping else np.zeros(n, dtype=float)
+    has_weights = float(np.sum(demand_w)) > 0
+
+    if has_weights:
+        policy, agent = float(np.dot(demand_w, policy_by)), float(np.dot(demand_w, agent_by))
+    elif np.any(seen):
+        policy, agent = float(np.mean(policy_by[seen])), float(np.mean(agent_by[seen]))
+    else:
+        policy, agent = 0.0, 0.0
+
+    leak = float(max(policy - agent, 0.0))
+    survival = float(np.clip(agent / policy, 0.0, 1.0)) if policy > 0 else 0.0
+
+    return COIWindow(policy=policy, agent=agent, leak=leak, survival_ratio=survival,
+                     policy_by_product=policy_by, agent_by_product=agent_by, demand_weights=demand_w)
+
+
+def coi_erosion(coi_policy: float, coi_agent: float, eps: float = 1e-9) -> float:
+    """Thesis-consistent COI erosion: fraction of pricing power destroyed by agent queries.
+
+    erosion = 1 - (COI_agent / COI_policy)
+    When agents find low prices, COI_agent -> 0, erosion -> 1.
+    """
+    if coi_policy <= eps:
+        return 0.0
+    return float(np.clip(1.0 - (coi_agent / (coi_policy + eps)), 0.0, 1.0))

sim/case/thesis_simplified/experiments.py

@@ -0,0 +1,325 @@
+"""COI leakage experiments and policy comparisons.
+
+Demonstrates the core thesis contribution: COI erosion under agent contamination
+and recovery via robust pricing policies.
+
+Generates TensorBoard logs for:
+- COI erosion curves across contamination levels
+- Policy comparison (fixed vs adaptive vs RL)
+- Revenue/margin trade-offs
+"""
+from __future__ import annotations
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Tuple
+import json
+import numpy as np
+
+try:
+    from torch.utils.tensorboard import SummaryWriter
+    HAS_TB = True
+except ImportError:
+    HAS_TB = False
+
+from .simplified_env import PricingEnv, EnvConfig, make_env
+from .simplified import System
+
+
+@dataclass
+class ExperimentResult:
+    """Container for experiment metrics."""
+    name: str
+    alpha: float
+    reward_mean: float
+    reward_std: float
+    coi_erosion: float
+    alpha_error: float
+    revenue: float
+    margin: float
+
+    def to_dict(self) -> dict:
+        return {k: getattr(self, k) for k in self.__dataclass_fields__}
+
+
+def theoretical_coi_erosion_curve(alphas: np.ndarray, n_sessions: int = 1000) -> np.ndarray:
+    """Theoretical COI erosion from Theorem 1 using order statistic model.
+
+    For N i.i.d. uniform queries on [p_min, p_max]:
+    E[p^(1)] = p_min + (p_max - p_min)/(N+1), so erosion = 1 - 2/(N+1)
+    """
+    erosions = []
+    for a in alphas:
+        n_agents = max(1, int(a * n_sessions))
+        erosions.append(1.0 - 2.0 / (n_agents + 1))
+    return np.array(erosions)
+
+
+def run_policy_episode(
+    env: PricingEnv,
+    policy_fn,
+    n_episodes: int = 10
+) -> Tuple[List[float], List[float], List[float], List[float]]:
+    """Run policy and collect per-step metrics."""
+    rewards, coi_erosions, alpha_errors, revenues = [], [], [], []
+
+    for _ in range(n_episodes):
+        obs, info = env.reset()
+        done = False
+        while not done:
+            action = policy_fn(obs, env.n)
+            obs, reward, terminated, truncated, info = env.step(action)
+            done = terminated or truncated
+            rewards.append(reward)
+            if 'coi_erosion' in info:
+                coi_erosions.append(info['coi_erosion'])
+            if 'alpha_true' in info and 'alpha_est' in info:
+                alpha_errors.append(abs(info['alpha_true'] - info['alpha_est']))
+            if 'revenue' in info:
+                revenues.append(info['revenue'])
+
+    return rewards, coi_erosions, alpha_errors, revenues
+
+
+class PolicyRegistry:
+    """Registry of baseline policies."""
+
+    @staticmethod
+    def fixed(obs: np.ndarray, n: int, margin: float = 0.15) -> np.ndarray:
+        return np.ones(n, dtype=np.float32) * (1.0 + margin)
+
+    @staticmethod
+    def random(obs: np.ndarray, n: int, rng: np.random.Generator = None) -> np.ndarray:
+        rng = rng or np.random.default_rng()
+        return rng.uniform(0.7, 1.3, n).astype(np.float32)
+
+    @staticmethod
+    def adaptive(obs: np.ndarray, n: int, base_margin: float = 0.15) -> np.ndarray:
+        """Reduce margins when alpha estimate is high."""
+        alpha_est = obs[2 * n] if len(obs) > 2 * n else 0.2
+        margin_scale = 1.0 - 0.4 * alpha_est
+        return np.ones(n, dtype=np.float32) * (1.0 + base_margin * margin_scale)
+
+    @staticmethod
+    def aggressive(obs: np.ndarray, n: int) -> np.ndarray:
+        """High margins, ignores contamination."""
+        return np.ones(n, dtype=np.float32) * 1.4
+
+    @staticmethod
+    def defensive(obs: np.ndarray, n: int) -> np.ndarray:
+        """Low margins, always cautious."""
+        return np.ones(n, dtype=np.float32) * 1.05
+
+    @staticmethod
+    def alpha_proportional(obs: np.ndarray, n: int, max_margin: float = 0.3) -> np.ndarray:
+        """Margin inversely proportional to estimated alpha."""
+        alpha_est = obs[2 * n] if len(obs) > 2 * n else 0.2
+        margin = max_margin * (1.0 - alpha_est)
+        return np.ones(n, dtype=np.float32) * (1.0 + margin)
+
+
+def run_contamination_sweep(
+    alphas: List[float],
+    policies: Dict[str, callable],
+    n_products: int = 10,
+    max_steps: int = 200,
+    n_episodes: int = 10,
+    seed: int = 42,
+    log_dir: str = None
+) -> Dict[str, List[ExperimentResult]]:
+    """Run policies across contamination levels."""
+
+    results = {name: [] for name in policies}
+    writer = SummaryWriter(Path(log_dir) / "sweep") if log_dir and HAS_TB else None
+
+    for alpha in alphas:
+        print(f"  alpha={alpha:.2f}", end=" ")
+        env_cfg = EnvConfig(
+            n_products=n_products, max_steps=max_steps,
+            alpha_true=alpha, reward_mode="robust", seed=seed)
+        env = make_env(env_cfg)
+
+        for name, policy_fn in policies.items():
+            rewards, coi_vals, alpha_errs, revenues = run_policy_episode(env, policy_fn, n_episodes)
+
+            result = ExperimentResult(
+                name=name, alpha=alpha,
+                reward_mean=float(np.mean(rewards)),
+                reward_std=float(np.std(rewards)),
+                coi_erosion=float(np.mean(coi_vals)) if coi_vals else 0.0,
+                alpha_error=float(np.mean(alpha_errs)) if alpha_errs else 0.0,
+                revenue=float(np.mean(revenues)) if revenues else 0.0,
+                margin=float(np.mean([policy_fn(np.zeros(3 * n_products + 3), n_products)]) - 1.0))
+
+            results[name].append(result)
+
+            if writer:
+                step = int(alpha * 100)
+                writer.add_scalar(f'{name}/reward', result.reward_mean, step)
+                writer.add_scalar(f'{name}/coi_erosion', result.coi_erosion, step)
+                writer.add_scalar(f'{name}/alpha_error', result.alpha_error, step)
+                writer.add_scalar(f'{name}/revenue', result.revenue, step)
+
+        print(f"done")
+
+    # add theoretical curve
+    if writer:
+        theo = theoretical_coi_erosion_curve(np.array(alphas))
+        for i, (a, e) in enumerate(zip(alphas, theo)):
+            writer.add_scalar('theoretical/coi_erosion', e, int(a * 100))
+        writer.close()
+
+    return results
+
+
+def run_coi_demonstration(log_dir: str = "sim/case/thesis_simplified/runs", seed: int = 42) -> Dict:
+    """Main COI demonstration experiment."""
+    print("=== COI Leakage Demonstration ===\n")
+
+    Path(log_dir).mkdir(parents=True, exist_ok=True)
+    writer = SummaryWriter(Path(log_dir) / "coi_demo") if HAS_TB else None
+
+    # theoretical erosion curve
+    print("1. Theoretical COI erosion (Theorem 1)")
+    alphas = np.linspace(0.0, 0.6, 13)
+    theo_erosion = theoretical_coi_erosion_curve(alphas, n_sessions=1000)
+
+    for a, e in zip(alphas, theo_erosion):
+        print(f"   alpha={a:.2f} -> erosion={e:.3f}")
+        if writer:
+            writer.add_scalar('theory/coi_erosion', e, int(a * 100))
+
+    # policy comparison
+    print("\n2. Policy comparison across contamination levels")
+    policies = {
+        'fixed': lambda obs, n: PolicyRegistry.fixed(obs, n),
+        'aggressive': PolicyRegistry.aggressive,
+        'defensive': PolicyRegistry.defensive,
+        'adaptive': PolicyRegistry.adaptive,
+        'alpha_proportional': PolicyRegistry.alpha_proportional,
+    }
+
+    sweep_alphas = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
+    results = run_contamination_sweep(
+        sweep_alphas, policies, n_products=10, max_steps=100,
+        n_episodes=5, seed=seed, log_dir=log_dir)
+
+    # summarize
+    print("\n3. Summary by policy")
+    for name, res_list in results.items():
+        avg_reward = np.mean([r.reward_mean for r in res_list])
+        avg_coi = np.mean([r.coi_erosion for r in res_list])
+        print(f"   {name:20s}: avg_reward={avg_reward:.2f}, avg_coi={avg_coi:.3f}")
+
+    # save results
+    output = {
+        'theoretical': {'alphas': alphas.tolist(), 'erosion': theo_erosion.tolist()},
+        'empirical': {name: [r.to_dict() for r in res_list] for name, res_list in results.items()}}
+
+    with open(Path(log_dir) / "coi_demo_results.json", 'w') as f:
+        json.dump(output, f, indent=2)
+
+    if writer:
+        writer.close()
+
+    print(f"\nResults saved to {log_dir}/coi_demo_results.json")
+    print(f"TensorBoard: tensorboard --logdir {log_dir}")
+
+    return output
+
+
+def run_reward_mode_comparison(log_dir: str = "sim/case/thesis_simplified/runs", seed: int = 42) -> Dict:
+    """Compare different reward modes."""
+    print("=== Reward Mode Comparison ===\n")
+
+    Path(log_dir).mkdir(parents=True, exist_ok=True)
+    writer = SummaryWriter(Path(log_dir) / "reward_modes") if HAS_TB else None
+
+    reward_modes = ["revenue", "profit", "robust", "coi_aware"]
+    alpha = 0.3  # moderate contamination
+
+    results = {}
+    for mode in reward_modes:
+        print(f"  mode={mode}", end=" ")
+        env_cfg = EnvConfig(
+            n_products=10, max_steps=200, alpha_true=alpha,
+            reward_mode=mode, seed=seed)
+        env = make_env(env_cfg)
+
+        rewards, coi_vals, _, revenues = run_policy_episode(
+            env, PolicyRegistry.adaptive, n_episodes=10)
+
+        results[mode] = {
+            'reward_mean': float(np.mean(rewards)),
+            'reward_std': float(np.std(rewards)),
+            'coi_erosion': float(np.mean(coi_vals)) if coi_vals else 0.0,
+            'revenue': float(np.mean(revenues)) if revenues else 0.0}
+
+        if writer:
+            for k, v in results[mode].items():
+                writer.add_scalar(f'{mode}/{k}', v, 0)
+
+        print(f"reward={results[mode]['reward_mean']:.2f}, coi={results[mode]['coi_erosion']:.3f}")
+
+    if writer:
+        writer.close()
+
+    with open(Path(log_dir) / "reward_mode_results.json", 'w') as f:
+        json.dump(results, f, indent=2)
+
+    return results
+
+
+def run_alpha_drift_experiment(log_dir: str = "sim/case/thesis_simplified/runs", seed: int = 42) -> Dict:
+    """Test policy robustness under non-stationary contamination."""
+    print("=== Alpha Drift Experiment ===\n")
+
+    Path(log_dir).mkdir(parents=True, exist_ok=True)
+    writer = SummaryWriter(Path(log_dir) / "alpha_drift") if HAS_TB else None
+
+    drift_rates = [0.0, 0.01, 0.02, 0.05]
+    results = {}
+
+    for drift in drift_rates:
+        print(f"  drift={drift:.2f}", end=" ")
+        env_cfg = EnvConfig(
+            n_products=10, max_steps=200, alpha_true=0.2,
+            alpha_drift=drift, reward_mode="robust", seed=seed)
+        env = make_env(env_cfg)
+
+        rewards, coi_vals, alpha_errs, _ = run_policy_episode(
+            env, PolicyRegistry.adaptive, n_episodes=10)
+
+        results[f'drift_{drift}'] = {
+            'reward_mean': float(np.mean(rewards)),
+            'coi_erosion': float(np.mean(coi_vals)) if coi_vals else 0.0,
+            'alpha_tracking_error': float(np.mean(alpha_errs)) if alpha_errs else 0.0}
+
+        if writer:
+            for k, v in results[f'drift_{drift}'].items():
+                writer.add_scalar(f'drift_{drift}/{k}', v, 0)
+
+        print(f"reward={results[f'drift_{drift}']['reward_mean']:.2f}, "
+              f"alpha_err={results[f'drift_{drift}']['alpha_tracking_error']:.3f}")
+
+    if writer:
+        writer.close()
+
+    return results
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description="Run COI experiments")
+    parser.add_argument("--exp", type=str, default="coi", choices=["coi", "reward", "drift", "all"])
+    parser.add_argument("--log-dir", type=str, default="sim/case/thesis_simplified/runs")
+    parser.add_argument("--seed", type=int, default=42)
+    args = parser.parse_args()
+
+    if args.exp == "coi" or args.exp == "all":
+        run_coi_demonstration(args.log_dir, args.seed)
+
+    if args.exp == "reward" or args.exp == "all":
+        run_reward_mode_comparison(args.log_dir, args.seed)
+
+    if args.exp == "drift" or args.exp == "all":
+        run_alpha_drift_experiment(args.log_dir, args.seed)

sim/case/thesis_simplified/separability.py

@@ -0,0 +1,72 @@
+"""Behavioral separability for human/agent detection.
+
+Computes divergence signals delta_H, delta_A from session trajectories using
+transition kernel estimation and KL divergence to prototype behavioral profiles.
+"""
+from __future__ import annotations
+from typing import Dict, List, Tuple, TYPE_CHECKING
+import numpy as np
+
+if TYPE_CHECKING:
+    from .simplified import Event, Session
+
+
+# prototype behavioral kernels for human vs agent sessions
+TRANS_H = {
+    "start": {"view": 0.85, "end": 0.15},
+    "view": {"detail": 0.4, "cart": 0.3, "view": 0.2, "end": 0.1},
+    "detail": {"cart": 0.5, "view": 0.3, "end": 0.2},
+    "cart": {"purchase": 0.6, "view": 0.25, "end": 0.15},
+    "purchase": {"end": 1.0},
+}
+
+TRANS_A = {
+    "start": {"view": 0.95, "end": 0.05},
+    "view": {"detail": 0.6, "view": 0.25, "cart": 0.1, "end": 0.05},
+    "detail": {"view": 0.5, "cart": 0.15, "detail": 0.3, "end": 0.05},
+    "cart": {"view": 0.4, "purchase": 0.2, "end": 0.4},
+    "purchase": {"end": 1.0},
+}
+
+
+def kl_div(p: Dict[str, float], q: Dict[str, float], eps: float = 1e-10) -> float:
+    """KL divergence D_KL(p || q) for discrete distributions."""
+    keys = set(p.keys()) | set(q.keys())
+    return sum(p.get(k, eps) * np.log((p.get(k, eps) + eps) / (q.get(k, eps) + eps)) for k in keys)
+
+
+def build_kernel(events: List["Event"]) -> Dict[str, Dict[str, float]]:
+    """Build empirical transition kernel T' from trajectory events."""
+    trans: Dict[str, Dict[str, int]] = {}
+    prev = "start"
+    for e in events:
+        curr = e.action
+        trans.setdefault(prev, {})
+        trans[prev][curr] = trans[prev].get(curr, 0) + 1
+        prev = curr
+    return {s: {d: c / sum(dsts.values()) for d, c in dsts.items()} for s, dsts in trans.items() if sum(dsts.values()) > 0}
+
+
+def compute_divergence(session: "Session") -> Tuple[float, float]:
+    """Compute divergence signals delta_H, delta_A for session.
+
+    delta_H = mean KL(T' || T_H) across states, measures distance to human prototype
+    delta_A = mean KL(T' || T_A) across states, measures distance to agent prototype
+    """
+    kernel = build_kernel(session.events)
+    if not kernel:
+        return 0.5, 0.5
+    delta_h = sum(kl_div(kernel.get(s, {}), TRANS_H.get(s, {})) for s in kernel) / len(kernel)
+    delta_a = sum(kl_div(kernel.get(s, {}), TRANS_A.get(s, {})) for s in kernel) / len(kernel)
+    return delta_h, delta_a
+
+
+def estimate_alpha(session: "Session", beta: float = 2.0) -> float:
+    """Per-session contamination estimate alpha_hat = sigma(beta*(delta_H - delta_A)).
+
+    Returns probability session is agent-generated based on behavioral divergence.
+    """
+    dh, da = compute_divergence(session)
+    if (dh + da) <= 0:
+        return 0.5
+    return 1.0 / (1.0 + np.exp(-beta * (dh - da)))

sim/case/thesis_simplified/simplified.py

@@ -0,0 +1,219 @@
+"""Minimal implementation of thesis pricing system.
+
+Implements the core loop: prices -> sessions -> demand -> prices
+with behavioral separability and robust pricing objective.
+
+Objects:
+- Session trajectories tau_s from mixture of H/A behavioral profiles
+- Demand proxy q_hat via weighted action aggregation
+- COI leakage penalty for agent reconnaissance
+- Limbo: alternating price/demand history for trajectory analysis
+"""
+from __future__ import annotations
+from dataclasses import dataclass, field
+from typing import Dict, List, Tuple
+import numpy as np
+
+from .coi import COIWindow, compute_coi_window
+from .separability import TRANS_H, TRANS_A, kl_div, build_kernel, compute_divergence, estimate_alpha
+
+ACTION_WEIGHTS = {"add_to_cart": 0.8, "checkout": 0.9, "purchase": 1.0, "view": 0.15, "detail": 0.25, "hover": 0.3, "start": 0.05, "end": 0.0}
+
+
+@dataclass
+class Event:
+    action: str
+    product_idx: int
+    price_seen: float
+    ts: float
+
+
+@dataclass
+class Session:
+    sid: str
+    events: List[Event]
+    actor: str  # H or A (ground truth label)
+    theta: Dict[str, float] = field(default_factory=dict)
+
+
+def compute_demand(session: Session) -> float:
+    """Compute demand proxy q_hat = sum_k omega(a_k) for session."""
+    return sum(ACTION_WEIGHTS.get(e.action, 0.1) for e in session.events)
+
+
+def sample_trajectory(rng: np.random.Generator, trans: Dict, prices: np.ndarray, costs: np.ndarray, theta: Dict[str, float],
+                      is_agent: bool, session_noise: float = 0.02, surge: float = 0.08, max_mult: float = 1.8) -> Tuple[List[Event], int]:
+    """Sample session trajectory from behavioral kernel."""
+    pidx = int(rng.integers(0, len(prices)))
+    cost, base = float(costs[pidx]), float(prices[pidx]) * (1.0 + rng.normal(0.0, session_noise))
+    base = float(np.clip(base, cost * 1.01, float(prices[pidx]) * 2.0))
+    price, signal, state, t = base, 0.0, "start", 0.0
+    events = []
+
+    while state != "end" and len(events) < 30:
+        probs = trans.get(state, {"end": 1.0})
+        nxt = rng.choice(list(probs.keys()), p=list(probs.values()))
+        if nxt == "purchase":  # purchase conversion check
+            rel = max((price - cost) / (cost + 1e-6), 0.0)
+            p_buy = float(np.clip(theta.get("base_conv", 0.2) * np.exp(-theta.get("price_sens", 2.0) * rel), 0.0, 1.0))
+            if rng.random() > p_buy:
+                nxt = "end"
+        state = nxt
+        if state not in {"start", "end"}:
+            events.append(Event(action=state, product_idx=pidx, price_seen=float(price), ts=t))
+            signal += float(ACTION_WEIGHTS.get(state, 0.1))
+            price = float(np.clip(base * (1.0 + surge * signal), cost * 1.01, base * max_mult))
+        t += max(0.2, rng.gamma(1.5, 0.8) if is_agent else rng.gamma(2.0, 1.2))
+    return events, pidx
+
+
+def put_prices_to_market(prices: np.ndarray, costs: np.ndarray, alpha: float = 0.2, n_sessions: int = 50,
+                         seed: int | None = None) -> Tuple[List[Session], Dict[str, float]]:
+    """Generate sessions from mixture model. Returns sessions and demand mapping sid -> q_hat."""
+    rng = np.random.default_rng(seed)
+    sessions, demand = [], {}
+    for i in range(n_sessions):
+        sid = f"s{i:04d}"
+        is_agent = rng.random() < alpha
+        trans = TRANS_A if is_agent else TRANS_H
+        theta = {"price_sens": rng.uniform(0.05, 0.2), "base_conv": 0.01} if is_agent else \
+                {"price_sens": rng.uniform(1.5, 4.0), "base_conv": rng.uniform(0.2, 0.5)}
+        events, _ = sample_trajectory(rng, trans, prices, costs=costs, theta=theta, is_agent=is_agent)
+        session = Session(sid=sid, events=events, actor="A" if is_agent else "H", theta=theta)
+        sessions.append(session)
+        demand[sid] = compute_demand(session)
+    return sessions, demand
+
+
+@dataclass
+class LimboUpdate:
+    utype: str  # "prices" or "demand"
+    data: np.ndarray | Dict[str, float]
+    t: int
+
+
+class Limbo:
+    """Historical trajectory of alternating price/demand observations."""
+
+    def __init__(self):
+        self.history: List[LimboUpdate] = []
+        self._t = 0
+
+    def add_update(self, utype: str, data: np.ndarray | Dict[str, float]) -> Dict:
+        self.history.append(LimboUpdate(utype=utype, data=data, t=self._t))
+        self._t += 1
+        return {"action": "observe_demand" if utype == "prices" else "set_prices"}
+
+    def get_prices_history(self) -> List[np.ndarray]:
+        return [u.data for u in self.history if u.utype == "prices"]
+
+    def get_demand_history(self) -> List[Dict[str, float]]:
+        return [u.data for u in self.history if u.utype == "demand"]
+
+
+class System:
+    """Main pricing system implementing robust Stackelberg objective.
+
+    Manages the alternating loop: set prices p_t -> observe demand Q_hat(p_t) ->
+    estimate contamination alpha from behavioral signals -> compute next prices.
+    """
+
+    def __init__(self, n_products: int = 10, costs: np.ndarray | None = None, lambda_coi: float = 0.5, seed: int | None = 42):
+        self.n = n_products
+        self.rng = np.random.default_rng(seed)
+        self.costs = costs if costs is not None else self.rng.uniform(10, 50, n_products)
+        self.refs = self.costs * (1 + self.rng.uniform(0.2, 0.5, n_products))
+        self.lambda_coi = lambda_coi
+        self.limbo = Limbo()
+        self._alpha_est = 0.2
+        self._sessions: List[Session] = []
+        self._last_sessions: List[Session] = []
+        self._last_coi: COIWindow | None = None
+
+    @property
+    def alpha(self) -> float:
+        return self._alpha_est
+
+    def _estimate_alpha_from_sessions(self) -> float:
+        if not self._sessions:
+            return self._alpha_est
+        return float(np.mean([estimate_alpha(s) for s in self._sessions[-50:]]))
+
+    def _revenue_under_demand(self, prices: np.ndarray, demand: Dict[str, float]) -> float:
+        agg = np.zeros(self.n)
+        for sid, q in demand.items():
+            sess = next((s for s in self._sessions if s.sid == sid), None)
+            if sess and sess.events:
+                agg[sess.events[0].product_idx] += q
+        return float(np.dot(prices, agg))
+
+    def _compute_coi_window(self, demand: Dict[str, float]) -> COIWindow:
+        if not self._last_sessions:
+            zeros = np.zeros(self.n, dtype=float)
+            return COIWindow(policy=0.0, agent=0.0, leak=0.0, survival_ratio=0.0,
+                             policy_by_product=zeros, agent_by_product=zeros, demand_weights=zeros)
+        return compute_coi_window(self._last_sessions, self.costs, demand_mapping=demand)
+
+    def _objective(self, prices: np.ndarray, demand: Dict[str, float]) -> float:
+        """Robust objective: R(p,d) - lambda * COI_leak."""
+        profit = self._revenue_under_demand(prices, demand) - float(np.sum(self.costs))
+        self._last_coi = self._compute_coi_window(demand)
+        return profit - self.lambda_coi * self._last_coi.leak
+
+    def compute_prices(self, demand: Dict[str, float] | None = None) -> np.ndarray:
+        """Compute next prices via heuristic margin adjustment based on alpha estimate."""
+        self._alpha_est = self._estimate_alpha_from_sessions()
+        margin_scale = 1.0 - 0.5 * self._alpha_est  # defensive pricing under high contamination
+        margins = (self.refs - self.costs) * margin_scale
+        noise = self.rng.normal(0, 0.02, self.n) * self.costs
+        prices = np.clip(self.costs + margins + noise, self.costs * 1.02, self.refs * 1.3)
+        self.limbo.add_update("prices", prices)
+        return prices
+
+    def observe_demand(self, prices: np.ndarray, alpha_true: float = 0.2, n_sessions: int = 50) -> Dict[str, float]:
+        sessions, demand_map = put_prices_to_market(prices, costs=self.costs, alpha=alpha_true,
+                                                    n_sessions=n_sessions, seed=int(self.rng.integers(0, 10000)))
+        self._last_sessions = sessions
+        self._sessions.extend(sessions)
+        self.limbo.add_update("demand", demand_map)
+        return demand_map
+
+    def step(self, alpha_true: float = 0.2, n_sessions: int = 50) -> Tuple[np.ndarray, Dict[str, float], float, COIWindow]:
+        demand_hist = self.limbo.get_demand_history()
+        prices = self.compute_prices(demand_hist[-1] if demand_hist else None)
+        demand = self.observe_demand(prices, alpha_true, n_sessions)
+        reward = self._objective(prices, demand)
+        return prices, demand, reward, self._last_coi or self._compute_coi_window(demand)
+
+    def run(self, n_steps: int = 100, alpha_true: float = 0.2) -> Dict:
+        traj = {"prices": [], "demand": [], "rewards": [], "alpha_est": [], "alpha_true": alpha_true,
+                "coi_policy": [], "coi_agent": [], "coi_leak": [], "coi_survival": []}
+        for _ in range(n_steps):
+            p, d, r, coi = self.step(alpha_true)
+            traj["prices"].append(p); traj["demand"].append(d); traj["rewards"].append(r)
+            traj["alpha_est"].append(self._alpha_est)
+            traj["coi_policy"].append(coi.policy); traj["coi_agent"].append(coi.agent)
+            traj["coi_leak"].append(coi.leak); traj["coi_survival"].append(coi.survival_ratio)
+        return traj
+
+
+if __name__ == "__main__":
+    sys = System(n_products=5, seed=42)
+    traj = sys.run(n_steps=20, alpha_true=0.25)
+    print(f"avg reward: {np.mean(traj['rewards']):.2f}, final alpha_hat: {traj['alpha_est'][-1]:.3f}, "
+          f"COI_policy: {np.mean(traj['coi_policy']):.3f}, COI_agent: {np.mean(traj['coi_agent']):.3f}, leak: {np.mean(traj['coi_leak']):.3f}")
+
+    prices = np.array([20.0, 35.0, 50.0, 25.0, 40.0])
+    costs = np.array([15.0, 28.0, 40.0, 18.0, 30.0])
+    sessions, demand = put_prices_to_market(prices, costs=costs, alpha=0.3, n_sessions=20, seed=123)
+    print(f'sessions: {len(sessions)}, agents: {sum(1 for s in sessions if s.actor=="A")}')
+
+    for n in [1, 5, 10, 50, 100]:
+        # theoretical: erosion = 1 - 2/(N+1) for uniform order statistic
+        print(f'N={n:3d} agents -> COI erosion: {1.0 - 2.0/(n+1):.3f}')
+
+    events = [Event('view', 0, 20.0, 0.1), Event('detail', 0, 20.0, 0.5), Event('cart', 0, 20.0, 1.0), Event('purchase', 0, 20.0, 2.0)]
+    print(f'human-like session alpha_hat: {estimate_alpha(Session(sid="test", events=events, actor="H")):.3f}')
+
+    events_a = [Event('view', 0, 20.0, 0.1), Event('detail', 0, 20.0, 0.2), Event('view', 0, 20.0, 0.3), Event('detail', 0, 20.0, 0.4)]
+    print(f'agent-like session alpha_hat: {estimate_alpha(Session(sid="test2", events=events_a, actor="A")):.3f}')

sim/case/thesis_simplified/simplified_env.py

@@ -0,0 +1,249 @@
+"""Gymnasium-compatible RL environment for thesis pricing system.
+
+Wraps simplified.System with standard Gym interface for training pricing policies.
+Supports multiple reward modes and contamination scenarios.
+
+Action: price multipliers [0.5, 1.5] applied to reference prices
+Observation: [prices, demand_agg, alpha_est, margins, position_proxy]
+Reward: configurable objective (revenue, profit, robust, coi-aware)
+"""
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Any, Dict, Tuple
+import numpy as np
+
+try:
+    import gymnasium as gym
+    from gymnasium import spaces
+    HAS_GYM = True
+except ImportError:
+    HAS_GYM = False
+
+from .simplified import System, Session, Event, Limbo, put_prices_to_market, compute_demand, estimate_alpha
+from .coi import COIWindow, compute_coi_window, coi_erosion
+
+
+@dataclass
+class EnvConfig:
+    n_products: int = 5
+    max_steps: int = 200
+    sessions_per_step: int = 30
+    alpha_true: float = 0.2
+    alpha_drift: float = 0.0
+    alpha_bounds: Tuple[float, float] = (0.0, 0.6)
+    lambda_coi: float = 0.5
+    lambda_vol: float = 0.1
+    reward_mode: str = "robust"  # revenue | profit | robust | coi_aware
+    normalize_reward: bool = True
+    seed: int | None = 42
+
+
+def aggregate_purchases(sessions: list[Session], n_products: int, costs: np.ndarray) -> Tuple[np.ndarray, float, float]:
+    """Aggregate purchases from sessions, returns (counts, revenue, cost)."""
+    purchases = np.zeros(n_products, dtype=float)
+    revenue, cost = 0.0, 0.0
+    for sess in sessions:
+        for e in sess.events:
+            if e.action == "purchase" and 0 <= e.product_idx < n_products:
+                purchases[e.product_idx] += 1.0
+                revenue += float(e.price_seen)
+                cost += float(costs[e.product_idx])
+    return purchases, revenue, cost
+
+
+class PricingEnv(gym.Env if HAS_GYM else object):
+    """RL environment for dynamic pricing under agent contamination.
+
+    Platform sets prices p_t, market responds with mixture demand Q(p) = (1-alpha)*D_H + alpha*D_A.
+    Agent estimates contamination alpha_hat from behavioral signals.
+    Reward balances profit vs COI leakage.
+    """
+    metadata = {"render_modes": ["human", "ansi"]}
+
+    def __init__(self, cfg: EnvConfig | None = None):
+        if not HAS_GYM:
+            raise ImportError("gymnasium required")
+        self.cfg = cfg or EnvConfig()
+        self.n = self.cfg.n_products
+        self._sys: System | None = None
+        self._t = 0
+        self._alpha = self.cfg.alpha_true
+        self._last_prices: np.ndarray | None = None
+        self._last_demand: Dict[str, float] | None = None
+        self._episode_rewards: list[float] = []
+        self._demand_agg = np.zeros(self.n)
+
+        self.action_space = spaces.Box(low=0.5, high=1.5, shape=(self.n,), dtype=np.float32)
+        obs_dim = self.n + self.n + 1 + 1 + self.n + 1  # prices + demand + alpha_hat + alpha + margins + t
+        self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(obs_dim,), dtype=np.float32)
+
+    def _build_obs(self) -> np.ndarray:
+        if self._sys is None:
+            return np.zeros(self.observation_space.shape[0], dtype=np.float32)
+        prices = self._last_prices if self._last_prices is not None else self._sys.refs
+        return np.concatenate([
+            prices / (self._sys.refs + 1e-6),
+            self._demand_agg / (np.sum(self._demand_agg) + 1e-6),
+            [self._sys.alpha, self._alpha],
+            (prices - self._sys.costs) / (self._sys.costs + 1e-6),
+            [self._t / self.cfg.max_steps],
+        ]).astype(np.float32)
+
+    def _compute_reward(self, prices: np.ndarray, demand: Dict[str, float]) -> float:
+        cfg, sys = self.cfg, self._sys
+        if sys is None:
+            return 0.0
+
+        # aggregate demand per product
+        agg = np.zeros(self.n)
+        for sid, q in demand.items():
+            sess = next((s for s in sys._sessions if s.sid == sid), None)
+            if sess and sess.events:
+                agg[sess.events[0].product_idx] += q
+        self._demand_agg = agg
+
+        _, revenue, cost = aggregate_purchases(sys._last_sessions, self.n, sys.costs)
+        profit = revenue - cost
+
+        vol_penalty = 0.0
+        if self._last_prices is not None:
+            vol_penalty = cfg.lambda_vol * float(np.mean(np.abs(prices - self._last_prices) / (sys.refs + 1e-6)))
+
+        coi = compute_coi_window(sys._last_sessions, sys.costs, demand_mapping=demand)
+        leak = float(coi.leak)
+
+        reward_fns = {
+            "revenue": lambda: revenue,
+            "profit": lambda: profit,
+            "robust": lambda: profit - cfg.lambda_coi * leak - vol_penalty,
+            "coi_aware": lambda: profit - cfg.lambda_coi * (1 + 2 * sys.alpha) * leak - vol_penalty,
+        }
+        r = reward_fns.get(cfg.reward_mode, lambda: profit)()
+        return float(r / (float(np.sum(sys.refs)) + 1e-6)) if cfg.normalize_reward else float(r)
+
+    def reset(self, seed: int | None = None, options: dict | None = None) -> Tuple[np.ndarray, dict]:
+        seed = seed if seed is not None else self.cfg.seed
+        self._sys = System(n_products=self.n, lambda_coi=self.cfg.lambda_coi, seed=seed)
+        self._t, self._alpha = 0, self.cfg.alpha_true
+        self._last_prices, self._last_demand = None, None
+        self._episode_rewards, self._demand_agg = [], np.zeros(self.n)
+        return self._build_obs(), {"alpha_true": self._alpha, "alpha_est": self._sys.alpha,
+                                   "costs": self._sys.costs.copy(), "refs": self._sys.refs.copy()}
+
+    def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, bool, dict]:
+        if self._sys is None:
+            raise RuntimeError("call reset() first")
+
+        action = np.clip(action, 0.5, 1.5)
+        prices = np.clip(self._sys.refs * action.astype(np.float64), self._sys.costs * 1.01, self._sys.refs * 2.0)
+        demand = self._sys.observe_demand(prices, alpha_true=self._alpha, n_sessions=self.cfg.sessions_per_step)
+        self._sys.limbo.add_update("prices", prices)
+        self._sys._alpha_est = self._sys._estimate_alpha_from_sessions()
+
+        reward = self._compute_reward(prices, demand)
+        self._episode_rewards.append(reward)
+        self._last_prices, self._last_demand = prices.copy(), demand
+        self._t += 1
+
+        # compute info metrics using shared helper
+        purchases, revenue, cost = aggregate_purchases(self._sys._last_sessions, self.n, self._sys.costs)
+        n_agents = int(self._alpha * self.cfg.sessions_per_step)
+        coi = compute_coi_window(self._sys._last_sessions, self._sys.costs, demand_mapping=demand)
+
+        info = {
+            "alpha_true": self._alpha, "alpha_est": self._sys.alpha,
+            "alpha_error": abs(self._alpha - self._sys.alpha),
+            "revenue": float(revenue), "profit": float(revenue - cost), "cost": float(cost),
+            "n_purchases": int(np.sum(purchases)),
+            "avg_margin": float(np.mean((prices - self._sys.costs) / self._sys.costs)),
+            "n_sessions": len(demand), "n_agents": n_agents, "price_std": float(np.std(prices)),
+            "coi_erosion": coi_erosion(coi.policy, coi.agent),
+            "coi_policy": float(coi.policy), "coi_agent": float(coi.agent),
+            "coi_leakage": float(coi.leak), "coi_survival": float(coi.survival_ratio),
+            "cumulative_reward": sum(self._episode_rewards), "step": self._t,
+        }
+        return self._build_obs(), reward, self._t >= self.cfg.max_steps, False, info
+
+    def render(self, mode: str = "human") -> str | None:
+        if self._sys is None or self._last_prices is None:
+            return None
+        out = f"t={self._t}/{self.cfg.max_steps} | alpha_true={self._alpha:.3f} alpha_hat={self._sys.alpha:.3f} | " \
+              f"prices: {self._last_prices.round(1)} | demand: {self._demand_agg.round(2)} | " \
+              f"reward: {self._episode_rewards[-1] if self._episode_rewards else 0:.3f}"
+        if mode == "human":
+            print(out)
+        return out
+
+    def close(self) -> None:
+        pass
+
+
+class ContaminationSweepEnv(PricingEnv):
+    """Environment that sweeps through contamination levels during training."""
+
+    def __init__(self, cfg: EnvConfig | None = None, alpha_schedule: list[float] | None = None):
+        super().__init__(cfg)
+        self._schedule = alpha_schedule or [0.1, 0.2, 0.3, 0.4, 0.5]
+        self._schedule_idx = 0
+
+    def reset(self, seed: int | None = None, options: dict | None = None) -> Tuple[np.ndarray, dict]:
+        if options and options.get("advance_schedule", False):
+            self._schedule_idx = (self._schedule_idx + 1) % len(self._schedule)
+        self.cfg.alpha_true = self._schedule[self._schedule_idx]
+        return super().reset(seed, options)
+
+
+class AdversarialEnv(PricingEnv):
+    """Environment with adversarial contamination dynamics.
+
+    Contamination increases when prices are predictable (agents exploit).
+    """
+
+    def __init__(self, cfg: EnvConfig | None = None, exploitation_rate: float = 0.02):
+        super().__init__(cfg)
+        self._exploit_rate = exploitation_rate
+        self._price_history: list[np.ndarray] = []
+
+    def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, bool, dict]:
+        obs, reward, term, trunc, info = super().step(action)
+        if self._last_prices is not None:
+            self._price_history.append(self._last_prices.copy())
+        predictability = 0.0
+        if len(self._price_history) > 10:
+            predictability = 1.0 / (float(np.std(self._price_history[-10:])) + 0.1)
+            self._alpha = np.clip(self._alpha + self._exploit_rate * predictability * self._sys.rng.random(), *self.cfg.alpha_bounds)
+        info["predictability"] = predictability
+        return obs, reward, term, trunc, info
+
+    def reset(self, seed: int | None = None, options: dict | None = None) -> Tuple[np.ndarray, dict]:
+        self._price_history = []
+        return super().reset(seed, options)
+
+
+def make_env(cfg: EnvConfig | None = None, env_type: str = "standard") -> PricingEnv:
+    return {"sweep": ContaminationSweepEnv, "adversarial": AdversarialEnv}.get(env_type, PricingEnv)(cfg)
+
+
+# baseline policies
+fixed_price_policy = lambda refs, margin=0.0: np.ones(len(refs), dtype=np.float32) * (1.0 + margin)
+random_policy = lambda n, rng=None: (rng or np.random.default_rng()).uniform(0.7, 1.3, n).astype(np.float32)
+adaptive_policy = lambda obs, n, base=0.1: np.ones(n, dtype=np.float32) * (1.0 + base * (1.0 - 0.4 * obs[2 * n]))
+
+
+if __name__ == "__main__":
+    cfg = EnvConfig(n_products=100, max_steps=100, alpha_true=0.25, reward_mode="robust")
+    env = make_env(cfg)
+    obs, info = env.reset()
+    print(f"initial: alpha={info['alpha_true']:.2f}")
+
+    total_reward = 0.0
+    for t in range(cfg.max_steps):
+        action = adaptive_policy(obs, cfg.n_products)
+        obs, reward, done, _, info = env.step(action)
+        total_reward += reward
+        if t % 10 == 0:
+            env.render()
+        if done:
+            break
+
+    print(f"\ntotal reward: {total_reward:.2f}, final alpha_hat: {info['alpha_est']:.3f}")

sim/case/thesis_simplified/summarize.py

@@ -0,0 +1,168 @@
+"""Summarize TensorBoard logs into comparison tables."""
+from __future__ import annotations
+import json
+import re
+from pathlib import Path
+from collections import defaultdict
+from dataclasses import dataclass
+import pandas as pd
+
+try:
+    from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
+    HAS_TB = True
+except ImportError:
+    HAS_TB = False
+
+
+@dataclass
+class RunInfo:
+    algo: str
+    alpha: float
+    reward_mode: str
+    path: Path
+
+
+def parse_run_name(name: str) -> RunInfo | None:
+    """Extract algo, alpha, reward_mode from run directory name."""
+    # patterns: ppo_a0.20_robust, cmp_fixed_a0.20, sac_a0.90_robust
+    m = re.match(r'(cmp_)?(\w+)_a([\d.]+)_?(\w+)?', name)
+    if not m:
+        return None
+    prefix, algo, alpha, mode = m.groups()
+    return RunInfo(algo=algo, alpha=float(alpha), reward_mode=mode or 'robust', path=Path())
+
+
+def load_tb_scalars(log_dir: Path, tags: list[str], reduce: str = 'last') -> dict[str, float]:
+    """Load scalar values from TensorBoard event files."""
+    if not HAS_TB:
+        return {}
+    ea = EventAccumulator(str(log_dir))
+    ea.Reload()
+    results = {}
+    for tag in tags:
+        if tag in ea.Tags().get('scalars', []):
+            events = ea.Scalars(tag)
+            if not events:
+                continue
+            vals = [e.value for e in events]
+            if reduce == 'last':
+                results[tag] = vals[-1]
+            elif reduce == 'mean':
+                results[tag] = sum(vals) / len(vals)
+            elif reduce == 'max':
+                results[tag] = max(vals)
+            elif reduce == 'min':
+                results[tag] = min(vals)
+    return results
+
+
+def load_json_results(log_dir: Path) -> dict[str, float]:
+    """Load metrics from results.json if available."""
+    results_file = log_dir / 'results.json'
+    if results_file.exists():
+        with open(results_file) as f:
+            return json.load(f)
+    return {}
+
+
+def discover_runs(base_dir: Path) -> list[RunInfo]:
+    """Find all experiment runs in base directory."""
+    runs = []
+    for d in base_dir.iterdir():
+        if not d.is_dir():
+            continue
+        info = parse_run_name(d.name)
+        if info:
+            info.path = d
+            runs.append(info)
+    return runs
+
+
+def build_tables(runs: list[RunInfo], metrics: list[str], reduce: str = 'last') -> dict[str, dict[str, pd.DataFrame]]:
+    """Build pivot tables: reward_mode -> metric -> DataFrame[alpha x algo]."""
+    # collect data: {reward_mode: {metric: {(alpha, algo): value}}}
+    data = defaultdict(lambda: defaultdict(dict))
+
+    tb_tags = [f'economics/{m}' if m in ['revenue', 'profit', 'margin'] else f'coi/{m}' if m in ['erosion', 'leakage'] else f'alpha/{m}' for m in metrics]
+    tag_map = dict(zip(tb_tags, metrics))
+
+    for run in runs:
+        # try json first (final eval metrics)
+        jm = load_json_results(run.path)
+        tb = load_tb_scalars(run.path, tb_tags, reduce)
+
+        for tag, metric in tag_map.items():
+            val = None
+            json_key = f'{metric}_mean' if metric != 'reward' else 'reward_mean'
+            if json_key in jm:
+                val = jm[json_key]
+            elif tag in tb:
+                val = tb[tag]
+            if val is not None:
+                data[run.reward_mode][metric][(run.alpha, run.algo)] = val
+
+    # convert to DataFrames
+    tables = {}
+    for mode, metrics_data in data.items():
+        tables[mode] = {}
+        for metric, vals in metrics_data.items():
+            if not vals:
+                continue
+            alphas = sorted(set(a for a, _ in vals.keys()))
+            algos = sorted(set(al for _, al in vals.keys()))
+            df = pd.DataFrame(index=alphas, columns=algos, dtype=float)
+            for (a, al), v in vals.items():
+                df.loc[a, al] = v
+            df.index.name = 'alpha'
+            tables[mode][metric] = df
+    return tables
+
+
+def format_table(df: pd.DataFrame, fmt: str = '.3f') -> str:
+    """Format DataFrame as markdown table."""
+    return df.to_markdown(floatfmt=fmt)
+
+
+def summarize(base_dir: str = 'sim/case/thesis_simplified/runs',
+              metrics: list[str] | None = None,
+              reduce: str = 'last',
+              output: str | None = None) -> dict:
+    """Generate summary tables from experiment runs."""
+    base = Path(base_dir)
+    metrics = metrics or ['revenue', 'profit', 'margin', 'erosion', 'leakage']
+
+    runs = discover_runs(base)
+    if not runs:
+        print(f"No runs found in {base}")
+        return {}
+
+    print(f"Found {len(runs)} runs")
+    tables = build_tables(runs, metrics, reduce)
+
+    lines = []
+    for mode, metric_tables in sorted(tables.items()):
+        lines.append(f"\n# Reward Mode: {mode}\n")
+        for metric, df in sorted(metric_tables.items()):
+            lines.append(f"\n## {metric}\n")
+            lines.append(format_table(df))
+            lines.append("")
+
+    report = '\n'.join(lines)
+    print(report)
+
+    if output:
+        Path(output).write_text(report)
+        print(f"\nSaved to {output}")
+
+    return tables
+
+
+if __name__ == '__main__':
+    import argparse
+    p = argparse.ArgumentParser()
+    p.add_argument('--dir', default='sim/case/thesis_simplified/runs')
+    p.add_argument('--metrics', nargs='+', default=['revenue', 'profit', 'margin', 'erosion', 'leakage'])
+    p.add_argument('--reduce', default='last', choices=['last', 'mean', 'max', 'min'])
+    p.add_argument('--output', '-o', help='save markdown to file')
+    args = p.parse_args()
+    summarize(args.dir, args.metrics, args.reduce, args.output)

sim/case/thesis_simplified/train.py

@@ -0,0 +1,336 @@
+"""RL training for thesis pricing system with thesis-aligned metrics.
+
+Trains pricing policies using stable-baselines3 with TensorBoard logging.
+Tracks COI erosion, alpha estimation error, and economic KPIs per thesis formulation.
+"""
+from __future__ import annotations
+import argparse
+import json
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from dataclasses import dataclass, asdict, field
+from pathlib import Path
+from typing import Dict, List, Callable, Any
+import numpy as np
+
+try:
+    from stable_baselines3 import PPO, SAC, A2C
+    from stable_baselines3.common.callbacks import BaseCallback, EvalCallback
+    from stable_baselines3.common.vec_env import DummyVecEnv
+    from stable_baselines3.common.monitor import Monitor
+    HAS_SB3 = True
+except ImportError:
+    HAS_SB3 = False
+
+try:
+    from torch.utils.tensorboard import SummaryWriter
+    HAS_TB = True
+except ImportError:
+    HAS_TB = False
+
+from .simplified_env import PricingEnv, EnvConfig, make_env, adaptive_policy, fixed_price_policy, random_policy
+
+
+@dataclass
+class EpisodeMetrics:
+    reward: float = 0.0
+    revenue: float = 0.0
+    profit: float = 0.0
+    coi_erosion: float = 0.0
+    coi_leakage: float = 0.0
+    alpha_error: float = 0.0
+    avg_margin: float = 0.0
+    n_agents: int = 0
+    steps: int = 0
+
+    def accumulate(self, info: Dict[str, Any]) -> None:
+        self.steps += 1
+        self.reward += info.get('reward', 0)
+        self.revenue += info.get('revenue', 0)
+        self.profit += info.get('profit', 0)
+        self.coi_erosion += info.get('coi_erosion', 0)
+        self.coi_leakage += info.get('coi_leakage', 0)
+        self.alpha_error += abs(info.get('alpha_true', 0) - info.get('alpha_est', 0))
+        self.avg_margin += info.get('avg_margin', 0)
+        self.n_agents += info.get('n_agents', 0)
+
+    def normalized(self) -> Dict[str, float]:
+        s = max(self.steps, 1)
+        return {k: getattr(self, k) / s for k in ['revenue', 'profit', 'coi_erosion', 'coi_leakage', 'alpha_error', 'avg_margin', 'n_agents']}
+
+
+@dataclass
+class ExperimentConfig:
+    algo: str = "ppo"
+    total_timesteps: int = 100_000
+    n_envs: int = 4
+    eval_freq: int = 5000
+    n_eval_episodes: int = 10
+    log_dir: str = "sim/case/thesis_simplified/runs"
+    seed: int = 42
+    n_products: int = 10
+    max_steps: int = 200
+    alpha_true: float = 0.2
+    reward_mode: str = "robust"
+    experiment_name: str | None = None
+
+    def __post_init__(self):
+        if self.experiment_name is None:
+            self.experiment_name = f"{self.algo}_a{self.alpha_true:.2f}_{self.reward_mode}"
+
+
+class Policy:
+    """Unified policy interface for baselines and trained models."""
+
+    def __init__(self, policy_fn: Callable[[np.ndarray, int], np.ndarray], name: str):
+        self._fn, self.name = policy_fn, name
+
+    def predict(self, obs: np.ndarray, deterministic: bool = True) -> tuple[np.ndarray, None]:
+        return self._fn(obs, (len(obs) - 3) // 3), None
+
+    @staticmethod
+    def fixed(margin: float = 0.15) -> "Policy":
+        return Policy(lambda obs, n: fixed_price_policy(np.ones(n), margin), f"fixed_{margin:.2f}")
+
+    @staticmethod
+    def adaptive(base_margin: float = 0.15) -> "Policy":
+        return Policy(lambda obs, n: adaptive_policy(obs, n, base_margin), f"adaptive_{base_margin:.2f}")
+
+    @staticmethod
+    def random() -> "Policy":
+        return Policy(lambda obs, n: random_policy(n), "random")
+
+    @staticmethod
+    def myopic(greed: float = 0.3) -> "Policy":
+        def _fn(obs: np.ndarray, n: int) -> np.ndarray:
+            demand_norm = obs[n:2*n] if len(obs) > 2*n else np.ones(n) * 0.5
+            return np.ones(n, dtype=np.float32) * np.clip(1.0 + greed * (1 + np.mean(demand_norm)), 0.5, 1.5)
+        return Policy(_fn, f"myopic_{greed:.1f}")
+
+
+def log_metrics(writer: SummaryWriter | None, metrics: Dict[str, float], prefix: str, step: int) -> None:
+    if writer is None:
+        return
+    for k, v in metrics.items():
+        writer.add_scalar(f'{prefix}/{k}', v, step)
+
+
+class MetricsCallback(BaseCallback):
+    def __init__(self, writer: SummaryWriter | None, verbose: int = 0):
+        super().__init__(verbose)
+        self._writer = writer
+
+    def _on_step(self) -> bool:
+        if self._writer is None:
+            return True
+        for info in self.locals.get('infos', []):
+            t = self.num_timesteps
+            self._writer.add_scalar('economics/revenue', info.get('revenue', 0), t)
+            self._writer.add_scalar('economics/profit', info.get('profit', 0), t)
+            self._writer.add_scalar('economics/margin', info.get('avg_margin', 0), t)
+            self._writer.add_scalar('coi/erosion', info.get('coi_erosion', 0), t)
+            self._writer.add_scalar('coi/leakage', info.get('coi_leakage', 0), t)
+            self._writer.add_scalar('alpha/estimation_error', abs(info.get('alpha_true', 0) - info.get('alpha_est', 0)), t)
+            self._writer.add_scalar('agents/count', info.get('n_agents', 0), t)
+        return True
+
+
+def make_vec_env(cfg: ExperimentConfig, n_envs: int = 1) -> DummyVecEnv:
+    def _make():
+        return Monitor(make_env(EnvConfig(n_products=cfg.n_products, max_steps=cfg.max_steps,
+                                          alpha_true=cfg.alpha_true, reward_mode=cfg.reward_mode, seed=cfg.seed)))
+    return DummyVecEnv([_make for _ in range(n_envs)])
+
+
+def run_episodes(policy: Policy | Any, env: PricingEnv, n_episodes: int) -> List[EpisodeMetrics]:
+    """Run policy for n episodes and collect metrics."""
+    metrics = []
+    for _ in range(n_episodes):
+        obs, _ = env.reset()
+        ep, done = EpisodeMetrics(), False
+        while not done:
+            action, _ = policy.predict(obs, deterministic=True)
+            obs, reward, term, trunc, info = env.step(action)
+            done = term or trunc
+            ep.accumulate(info)
+            ep.reward += reward
+        metrics.append(ep)
+    return metrics
+
+
+def evaluate_policy(policy: Policy | Any, cfg: ExperimentConfig, n_episodes: int = 20) -> Dict[str, float]:
+    env = make_env(EnvConfig(n_products=cfg.n_products, max_steps=cfg.max_steps,
+                             alpha_true=cfg.alpha_true, reward_mode=cfg.reward_mode, seed=cfg.seed + 999))
+    metrics = run_episodes(policy, env, n_episodes)
+    return {
+        'reward_mean': np.mean([m.reward for m in metrics]), 'reward_std': np.std([m.reward for m in metrics]),
+        **{f'{k}_mean': np.mean([m.normalized()[k] for m in metrics])
+           for k in ['revenue', 'profit', 'coi_erosion', 'coi_leakage', 'alpha_error', 'avg_margin']},
+    }
+
+
+def run_baseline(policy: Policy, vec_env: DummyVecEnv, total_steps: int, writer: SummaryWriter | None):
+    obs, n_envs = vec_env.reset(), vec_env.num_envs
+    ep_rewards = np.zeros(n_envs)
+
+    for step in range(0, total_steps, n_envs):
+        actions = np.array([policy.predict(obs[i])[0] for i in range(n_envs)])
+        obs, rewards, dones, infos = vec_env.step(actions)
+        ep_rewards += rewards
+        for i, info in enumerate(infos):
+            if writer:
+                writer.add_scalar('economics/revenue', info.get('revenue', 0), step)
+                writer.add_scalar('economics/profit', info.get('profit', 0), step)
+                writer.add_scalar('economics/margin', info.get('avg_margin', 0), step)
+                writer.add_scalar('coi/erosion', info.get('coi_erosion', 0), step)
+                writer.add_scalar('coi/leakage', info.get('coi_leakage', 0), step)
+                writer.add_scalar('alpha/estimation_error', abs(info.get('alpha_true', 0) - info.get('alpha_est', 0)), step)
+                writer.add_scalar('agents/count', info.get('n_agents', 0), step)
+            if dones[i]:
+                if writer:
+                    writer.add_scalar('rollout/ep_reward', ep_rewards[i], step)
+                ep_rewards[i] = 0
+
+
+def train(cfg: ExperimentConfig) -> Dict[str, Any]:
+    is_baseline = cfg.algo.lower() in ["fixed", "adaptive", "random", "myopic"]
+    if not HAS_SB3 and not is_baseline:
+        raise ImportError("stable-baselines3 required: pip install stable-baselines3[extra]")
+
+    log_path = Path(cfg.log_dir) / cfg.experiment_name
+    log_path.mkdir(parents=True, exist_ok=True)
+    with open(log_path / "config.json", "w") as f:
+        json.dump(asdict(cfg), f, indent=2)
+
+    writer = SummaryWriter(log_path) if HAS_TB else None
+    train_env, eval_env = make_vec_env(cfg, cfg.n_envs), make_vec_env(cfg, 1)
+
+    if is_baseline:
+        policy = {"fixed": Policy.fixed, "adaptive": Policy.adaptive, "random": Policy.random, "myopic": Policy.myopic}[cfg.algo.lower()]()
+        run_baseline(policy, train_env, cfg.total_timesteps, writer)
+        final_metrics = evaluate_policy(policy, cfg)
+    else:
+        algo_cls = {"ppo": PPO, "sac": SAC, "a2c": A2C}[cfg.algo.lower()]
+        common = dict(verbose=1, seed=cfg.seed, tensorboard_log=str(log_path), device="auto")
+        model = {
+            "ppo": lambda: PPO("MlpPolicy", train_env, learning_rate=3e-4, n_steps=2048, batch_size=64, n_epochs=10, gamma=0.99, gae_lambda=0.95, clip_range=0.2, ent_coef=0.01, **common),
+            "sac": lambda: SAC("MlpPolicy", train_env, learning_rate=1e-4, buffer_size=50_000, batch_size=512, tau=0.02, gamma=0.99, learning_starts=1000, ent_coef="auto_0.1", train_freq=4, **common),
+            "a2c": lambda: A2C("MlpPolicy", train_env, learning_rate=7e-4, n_steps=5, gamma=0.99, **common),
+        }[cfg.algo.lower()]()
+
+        cb = MetricsCallback(writer)
+        eval_cb = EvalCallback(eval_env, best_model_save_path=str(log_path / "best"), log_path=str(log_path),
+                               eval_freq=cfg.eval_freq, n_eval_episodes=cfg.n_eval_episodes, deterministic=True)
+        model.learn(cfg.total_timesteps, callback=[cb, eval_cb], progress_bar=True)
+        model.save(log_path / "final_model")
+        policy = model
+        final_metrics = evaluate_policy(model, cfg)
+
+    if writer:
+        log_metrics(writer, final_metrics, 'final', cfg.total_timesteps)
+        writer.close()
+
+    train_env.close(); eval_env.close()
+    with open(log_path / "results.json", "w") as f:
+        json.dump(final_metrics, f, indent=2)
+    return {"path": str(log_path), "metrics": final_metrics}
+
+
+def _train_alpha(args: tuple) -> tuple[str, Dict]:
+    """Worker for parallel sweep - must be top-level for pickling."""
+    cfg_dict, alpha = args
+    cfg_dict["alpha_true"] = alpha
+    cfg_dict["experiment_name"] = f"{cfg_dict['algo']}_a{alpha:.2f}_{cfg_dict['reward_mode']}"
+    sweep_cfg = ExperimentConfig(**cfg_dict)
+    print(f"[alpha={alpha:.2f}] starting")
+    metrics = train(sweep_cfg)["metrics"]
+    print(f"[alpha={alpha:.2f}] done")
+    return f"alpha_{alpha:.2f}", metrics
+
+
+def run_sweep(cfg: ExperimentConfig, alphas: List[float] | None = None, max_workers: int | None = None) -> Dict[str, Dict]:
+    alphas = alphas or [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
+    cfg_dict = asdict(cfg)
+
+    if max_workers == 1:  # sequential fallback
+        results = dict(_train_alpha((cfg_dict.copy(), a)) for a in alphas)
+    else:
+        with ProcessPoolExecutor(max_workers=max_workers) as pool:
+            futures = {pool.submit(_train_alpha, (cfg_dict.copy(), a)): a for a in alphas}
+            results = {}
+            for fut in as_completed(futures):
+                key, metrics = fut.result()
+                results[key] = metrics
+
+    summary_path = Path(cfg.log_dir) / f"sweep_{cfg.algo}_{cfg.reward_mode}.json"
+    with open(summary_path, "w") as f:
+        json.dump(results, f, indent=2)
+    print(f"\nSweep results saved to {summary_path}")
+    return results
+
+
+def _train_policy(args: tuple) -> tuple[str, Dict]:
+    """Worker for parallel policy comparison."""
+    cfg_dict, algo = args
+    cfg_dict["algo"] = algo
+    cfg_dict["experiment_name"] = f"cmp_{algo}_a{cfg_dict['alpha_true']:.2f}"
+    cmp_cfg = ExperimentConfig(**cfg_dict)
+    print(f"[{algo}] starting")
+    metrics = train(cmp_cfg)["metrics"]
+    print(f"[{algo}] done")
+    return algo, metrics
+
+
+def compare_policies(cfg: ExperimentConfig, policies: List[str] | None = None, max_workers: int | None = None) -> Dict[str, Dict]:
+    policies = policies or ["fixed", "adaptive", "myopic", "random"]
+    cfg_dict = asdict(cfg)
+
+    if max_workers == 1:
+        results = dict(_train_policy((cfg_dict.copy(), p)) for p in policies)
+    else:
+        with ProcessPoolExecutor(max_workers=max_workers) as pool:
+            futures = {pool.submit(_train_policy, (cfg_dict.copy(), p)): p for p in policies}
+            results = {}
+            for fut in as_completed(futures):
+                algo, metrics = fut.result()
+                results[algo] = metrics
+
+    cmp_path = Path(cfg.log_dir) / f"compare_a{cfg.alpha_true:.2f}.json"
+    with open(cmp_path, "w") as f:
+        json.dump(results, f, indent=2)
+    print(f"\nComparison saved to {cmp_path}")
+    for algo, m in results.items():
+        print(f"  {algo:12s}: reward={m['reward_mean']:.2f} coi_erosion={m['coi_erosion_mean']:.4f} alpha_err={m['alpha_error_mean']:.4f}")
+    return results
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Train RL pricing policies")
+    parser.add_argument("--algo", default="ppo", choices=["ppo", "sac", "a2c", "fixed", "adaptive", "random", "myopic"])
+    parser.add_argument("--steps", type=int, default=100_000)
+    parser.add_argument("--alpha", type=float, default=0.2)
+    parser.add_argument("--reward-mode", default="robust", choices=["revenue", "profit", "robust", "coi_aware"])
+    parser.add_argument("--n-products", type=int, default=10)
+    parser.add_argument("--n-envs", type=int, default=4)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--log-dir", default="sim/case/thesis_simplified/runs")
+    parser.add_argument("--sweep", action="store_true", help="run contamination sweep")
+    parser.add_argument("--compare", action="store_true", help="compare all baselines")
+    parser.add_argument("--workers", type=int, default=None, help="max parallel workers for sweep (None=auto, 1=sequential)")
+    args = parser.parse_args()
+
+    cfg = ExperimentConfig(algo=args.algo, total_timesteps=args.steps, alpha_true=args.alpha,
+                           reward_mode=args.reward_mode, n_products=args.n_products,
+                           n_envs=args.n_envs, seed=args.seed, log_dir=args.log_dir)
+
+    if args.sweep:
+        run_sweep(cfg, max_workers=args.workers)
+    elif args.compare:
+        compare_policies(cfg, max_workers=args.workers)
+    else:
+        result = train(cfg)
+        print(f"\nTraining complete: {result['path']}")
+        print(f"Metrics: {json.dumps(result['metrics'], indent=2)}")
+
+
+if __name__ == "__main__":
+    main()

sim/rl/behavior_loader/loader.py

@@ -1,6 +1,6 @@
 import os
-from pydantic import BaseModel as Base
 import json
+from pydantic import BaseModel as Base
 
 class PayloadModel(Base):
     sessionId: str
@@ -30,6 +30,9 @@ class InteractionModel(Base):
     key: dict
     value: ValueModel
 
+def _is_admin(page: str | None) -> bool:
+    return page is not None and page.startswith("/admin/")
+
 class Loader:
     def __init__(self, src_dir: str):
         self.src_dir = src_dir
@@ -37,17 +40,13 @@ class Loader:
         if not self.entries: raise ValueError("empty directory")
         self.data = self._load_sessions()
 
-    def _is_admin_page(self, interaction: InteractionModel) -> bool:
-        page = interaction.value.payload.page
-        return page and page.startswith("/admin/")
-
     def _load_sessions(self) -> dict:
         sessions = {}
         for entry in self.entries:
-            int_path = f"{self.src_dir}/{entry}/int.json"
-            raw = json.load(open(int_path))
+            with open(f"{self.src_dir}/{entry}/int.json") as f:
+                raw = json.load(f)
             ints = [InteractionModel(**i) for i in raw]
-            sessions[entry] = [i for i in ints if not self._is_admin_page(i)]
+            sessions[entry] = [i for i in ints if not _is_admin(i.value.payload.page)]
         return sessions
 
     def get_data(self) -> dict:
@@ -56,8 +55,43 @@ class Loader:
     def get_entries(self) -> tuple[list[str], int]:
         return self.entries, len(self.entries)
 
+class AgentLoader(Loader):
+    def _load_sessions(self) -> dict:
+        sessions = {}
+        for entry in self.entries:
+            with open(f"{self.src_dir}/{entry}/int.json") as f:
+                raw = json.load(f)
+            ints = [PayloadModel(**i) for i in raw]
+            sessions[entry] = [i for i in ints if not _is_admin(i.page)]
+        return sessions
+
+class JointLoader:
+    def __init__(self, human_dir: str, agent_dir: str):
+        self.human_loader = Loader(human_dir)
+        self.agent_loader = AgentLoader(agent_dir)
+        self.data = self._merge()
+        self.entries = list(self.data.keys())
+
+    def _merge(self) -> dict:
+        return {
+            **{f"human_{sid}": [e.value.payload for e in evts]
+               for sid, evts in self.human_loader.get_data().items()},
+            **{f"agent_{sid}": evts
+               for sid, evts in self.agent_loader.get_data().items()}
+        }
+
+    def get_data(self) -> dict:
+        return self.data
+
+    def get_entries(self) -> tuple[list[str], int]:
+        return self.entries, len(self.entries)
+
 if __name__ == "__main__":
-    DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/collected_data/"
-    loader = Loader(DIR)
-    _, n = loader.get_entries()
-    print(f"Loaded {n} sessions from {DIR}")
+    agent_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/agents/collected_data/"
+    human_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/collected_data/"
+
+    for name, cls, path in [("agent", AgentLoader, agent_dir),
+                             ("human", Loader, human_dir),
+                             ("joint", lambda d: JointLoader(human_dir, d), agent_dir)]:
+        ldr = cls(path) if name != "joint" else cls(agent_dir)
+        print(f"Loaded {len(ldr.get_entries()[0])} {name} sessions")

sim/rl/behavior_loader/models.py

@@ -1,14 +1,28 @@
-from loader import Loader
+try:
+    from loader import Loader, AgentLoader, JointLoader
+except ImportError:
+    from sim.rl.behavior_loader.loader import Loader, AgentLoader, JointLoader
 from collections import defaultdict
 from typing import Dict, List, Tuple, Set
 import numpy as np
 import graphviz
+import sys
+from pathlib import Path
+
+# import lib utilities for optional use - models keep their own _state_repr for backwards compat
+# with the specific event structure (evt.value.payload)
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / 'lib'))
+try:
+    from lib.state import make_state_repr as lib_make_state_repr
+    from lib.features import transition_histogram as lib_transition_histogram
+except ImportError:
+    lib_make_state_repr = None
+    lib_transition_histogram = None
 
-DIR = "/home/velocitatem/Documents/Projects/PHANTOM/experiments/collected_data/"
 
 class BehaviorModel:
-    def __init__(self, src_dir: str = DIR):
-        self.loader = Loader(src_dir)
+    def __init__(self, src_dir: str, loader_cls=Loader):
+        self.loader = loader_cls(src_dir)
         self.data = self.loader.get_data()
         self.entries, self.num_entries = self.loader.get_entries()
         self.mdp = None
@@ -17,50 +31,48 @@ class BehaviorModel:
         p = evt.value.payload
         return f"{p.page or 'unk'}|{p.productId or 'none'}|{p.eventName}"
 
-    def _extract_sessions(self):
-        # transform raw events into sequential state trajectories per session
-        trajectories = []
-        for sid, evts in self.data.items():
-            if len(evts) < 2: continue
-            states = [self._state_repr(e) for e in sorted(evts, key=lambda x: x.timestamp)]
-            trajectories.append(states)
-        return trajectories
+    def _sort_key(self, evt):
+        return evt.timestamp
 
-    def _calc_transitions(self, trajectories: List[List[str]]) -> Tuple[Dict, Set]:
-        trans = defaultdict(lambda: defaultdict(int))
-        states = set()
-        for traj in trajectories:
-            for i in range(len(traj) - 1):
-                s, s_next = traj[i], traj[i+1]
+    def _extract_sessions(self) -> List[List[str]]:
+        trajs = []
+        for evts in self.data.values():
+            if len(evts) < 2: continue
+            states = [self._state_repr(e) for e in sorted(evts, key=self._sort_key)]
+            trajs.append(states)
+        return trajs
+
+    def _calc_transitions(self, trajs: List[List[str]]) -> Tuple[Dict, Set]:
+        trans, states = defaultdict(lambda: defaultdict(int)), set()
+        for traj in trajs:
+            for s, s_next in zip(traj, traj[1:]):
                 trans[s][s_next] += 1
                 states.update([s, s_next])
         return trans, states
 
-    def _calc_rewards(self, trajectories: List[List[str]]) -> Dict:
-        # reward based on session progression depth
+    def _calc_rewards(self, trajs: List[List[str]]) -> Dict:
         rwd = defaultdict(list)
-        for traj in trajectories:
+        for traj in trajs:
             n = len(traj)
             for i, s in enumerate(traj):
                 rwd[s].append(i / n)
         return rwd
 
-    def _normalize_trans(self, counts: Dict) -> Dict:
+    def _normalize_trans(self, cnts: Dict) -> Dict:
         return {s: {s_n: cnt/sum(nxt.values()) for s_n, cnt in nxt.items()}
-                for s, nxt in counts.items()}
+                for s, nxt in cnts.items()}
 
     def build_MDP(self) -> Dict:
         trajs = self._extract_sessions()
         trans_cnt, states = self._calc_transitions(trajs)
         trans_prob = self._normalize_trans(trans_cnt)
         state_rwd = self._calc_rewards(trajs)
-        state_val = {s: np.mean(r) for s, r in state_rwd.items()}
 
         self.mdp = {
-            'states': sorted(list(states)),
+            'states': sorted(states),
             'num_states': len(states),
             'transitions': trans_prob,
-            'state_values': state_val,
+            'state_values': {s: np.mean(r) for s, r in state_rwd.items()},
             'state_rewards': state_rwd,
             'trans_counts': trans_cnt,
         }
@@ -76,8 +88,7 @@ class BehaviorModel:
 
     def sample_traj(self, start: str, max_len: int = 50) -> List[str]:
         if not self.mdp: raise ValueError("build MDP first")
-        path = [start]
-        curr = start
+        path, curr = [start], start
         for _ in range(max_len):
             nxt = self.mdp['transitions'].get(curr, {})
             if not nxt: break
@@ -85,60 +96,161 @@ class BehaviorModel:
             path.append(curr)
         return path
 
-def visualize_mdp(model: BehaviorModel, threshold: float = 0.05, output: str = "mdp_graph", fmt: str = "svg", view: bool = False, export_dot: bool = False):
-    """visualize MDP as directed graph using graphviz, aggregated by event type"""
+    def extract_trajectory_features(self, events: List, max_trans_dim: int = 50) -> np.ndarray:
+        """Convert trajectory to feature vector using MDP structure for contrastive learning"""
+        if not self.mdp:
+            self.build_MDP()
+
+        states = [self._state_repr(e) for e in sorted(events, key=self._sort_key)]
+        features = []
+
+        # transition histogram over MDP state space
+        trans_counts = defaultdict(int)
+        for s, s_next in zip(states, states[1:]):
+            trans_counts[(s, s_next)] += 1
+        all_trans = [(s, t) for s in self.mdp['states'] for t in self.mdp['transitions'].get(s, {}).keys()]
+        trans_vec = [trans_counts.get(tr, 0) for tr in all_trans[:max_trans_dim]]
+        trans_vec = trans_vec + [0] * (max_trans_dim - len(trans_vec))  # pad
+        total_trans = sum(trans_counts.values()) or 1
+        features.extend([v / total_trans for v in trans_vec])
+
+        # state coverage ratio
+        visited = set(states)
+        features.append(len(visited) / max(self.mdp['num_states'], 1))
+
+        # temporal entropy of transitions
+        if len(states) > 1:
+            trans_probs = [self.transition_prob(s, s_n) for s, s_n in zip(states, states[1:])]
+            entropy = -sum(p * np.log(p + 1e-10) for p in trans_probs if p > 0)
+            features.append(entropy / max(len(states), 1))
+        else:
+            features.append(0.0)
+
+        # trajectory length and unique state count
+        features.append(len(states))
+        features.append(len(visited))
+
+        # state value statistics along trajectory
+        vals = [self.state_value(s) for s in states]
+        if vals:
+            features.extend([np.mean(vals), np.std(vals), np.min(vals), np.max(vals)])
+        else:
+            features.extend([0.0, 0.0, 0.0, 0.0])
+
+        return np.array(features, dtype=np.float32)
+
+
+class AgentBehaviorModel(BehaviorModel):
+    def __init__(self, src_dir: str):
+        super().__init__(src_dir, AgentLoader)
+
+    def _state_repr(self, evt) -> str:
+        return f"{evt.page or 'unk'}|{evt.productId or 'none'}|{evt.eventName}"
+
+    def _sort_key(self, evt):
+        return evt.ts
+
+class JointBehaviorModel(BehaviorModel):
+    def __init__(self, human_dir: str, agent_dir: str):
+        self.loader = JointLoader(human_dir, agent_dir)
+        self.data = self.loader.get_data()
+        self.entries, self.num_entries = self.loader.get_entries()
+        self.mdp = None
+
+    def _state_repr(self, evt) -> str:
+        return f"{evt.page or 'unk'}|{evt.productId or 'none'}|{evt.eventName}"
+
+    def _sort_key(self, evt):
+        return evt.ts
+
+def aggregate_event_transitions(mdp: Dict) -> Dict[str, Dict[str, float]]:
+    evt_trans = defaultdict(lambda: defaultdict(float))
+    for s, trans in mdp['transitions'].items():
+        src = s.split('|')[2]
+        for s_next, prob in trans.items():
+            dst = s_next.split('|')[2]
+            evt_trans[src][dst] += prob
+
+    for src in evt_trans:
+        total = sum(evt_trans[src].values())
+        if total > 0:
+            evt_trans[src] = {dst: p/total for dst, p in evt_trans[src].items()}
+    return dict(evt_trans)
+
+def visualize_mdp(model: BehaviorModel, threshold: float = 0.05, output: str = "mdp_graph",
+                  fmt: str = "svg", view: bool = False, export_dot: bool = False):
     if not model.mdp: raise ValueError("build MDP first")
 
-    # aggregate transitions by event type
-    evt_trans = defaultdict(lambda: defaultdict(float))
-    for s, trans in model.mdp['transitions'].items():
-        evt_src = s.split('|')[2]
-        for s_next, prob in trans.items():
-            evt_dst = s_next.split('|')[2]
-            evt_trans[evt_src][evt_dst] += prob
-
-    # normalize aggregated transitions
-    for evt_src in evt_trans:
-        total = sum(evt_trans[evt_src].values())
-        if total > 0:
-            for evt_dst in evt_trans[evt_src]:
-                evt_trans[evt_src][evt_dst] /= total
-
+    evt_trans = aggregate_event_transitions(model.mdp)
     g = graphviz.Digraph(format=fmt)
     g.attr(rankdir='LR', size='30')
     g.attr('node', shape='circle', width='1', height='1')
 
-    # collect all event types
-    events = set(evt_trans.keys())
-    for trans in evt_trans.values():
-        events.update(trans.keys())
-
-    # add nodes for each event type
+    events = set(evt_trans.keys()) | {e for trans in evt_trans.values() for e in trans.keys()}
     for evt in events:
         g.node(evt)
 
-    # add edges above threshold
-    for evt_src in evt_trans:
-        for evt_dst, prob in evt_trans[evt_src].items():
+    for src, dsts in evt_trans.items():
+        for dst, prob in dsts.items():
             if prob > threshold:
-                g.edge(evt_src, evt_dst, label=f'{prob:.2f}')
+                g.edge(src, dst, label=f'{prob:.2f}')
 
     g.render(output, view=view, cleanup=True)
     print(f"Saved MDP graph to {output}.{fmt}")
 
     if export_dot:
-        dot_file = f"{output}.dot"
-        with open(dot_file, 'w') as f:
+        with open(f"{output}.dot", 'w') as f:
             f.write(g.source)
-        print(f"Exported DOT source to {dot_file}")
+        print(f"Exported DOT source to {output}.dot")
 
     return g
 
+def kl_divergence(p: Dict[str, float], q: Dict[str, float]) -> float:
+    eps = 1e-10
+    # p + log(p / q) summed over all keys in P
+    return sum((p[k] + eps) * np.log((p[k] + eps) / (q.get(k, 0.0) + eps)) for k in p)
+
 if __name__ == "__main__":
-    model = BehaviorModel(DIR)
-    mdp = model.build_MDP()
-    print(f"Built MDP: {mdp['num_states']} states, {sum(len(t) for t in mdp['transitions'].values())} transitions")
-    if not mdp['states']:
-        print("No states found")
-        exit(1)
-    visualize_mdp(model, threshold=0.05, output="mdp_viz", fmt="pdf", export_dot=True)
+    base_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments"
+    human_dir, agent_dir = f"{base_dir}/collected_data/", f"{base_dir}/agents/collected_data/"
+
+    human_model = BehaviorModel(human_dir)
+    human_mdp = human_model.build_MDP()
+    print(f"Built MDP: {human_mdp['num_states']} states, "
+          f"{sum(len(t) for t in human_mdp['transitions'].values())} transitions")
+    if not human_mdp['states']:
+        exit("No states found")
+    visualize_mdp(human_model, threshold=0.05, output="human_mdp_viz", fmt="pdf", export_dot=True)
+
+    agent_model = AgentBehaviorModel(agent_dir)
+    agent_mdp = agent_model.build_MDP()
+
+    print(f"AGENT... Built MDP: {agent_mdp['num_states']} states, "
+          f"{sum(len(t) for t in agent_mdp['transitions'].values())} transitions")
+    if not agent_mdp['states']:
+        exit("No states found")
+    visualize_mdp(agent_model, threshold=0.05, output="agent_mdp_viz", fmt="pdf", export_dot=True)
+
+    human_evt = aggregate_event_transitions(human_mdp)
+    agent_evt = aggregate_event_transitions(agent_mdp)
+
+    common = set(human_evt.keys()) & set(agent_evt.keys())
+
+    if not common:
+        exit("No common event types for KL divergence analysis")
+
+    kl_divs = sorted([(e, kl_divergence(human_evt[e], agent_evt[e])) for e in common],
+                     key=lambda x: x[1], reverse=True)
+
+    print(f"Average KL divergence: {np.mean([kl for _, kl in kl_divs]):.4f}")
+    print("\nMost divergent event types:")
+    for evt, kl in kl_divs:
+        print(f"  {evt}: {kl:.4f}")
+
+    print("\n=== Joint Model (Human + Agent Combined) ===")
+    joint_model = JointBehaviorModel(human_dir, agent_dir)
+    joint_mdp = joint_model.build_MDP()
+    print(f"Built joint MDP: {joint_mdp['num_states']} states, "
+          f"{sum(len(t) for t in joint_mdp['transitions'].values())} transitions")
+    if joint_mdp['states']:
+        visualize_mdp(joint_model, threshold=0.05, output="joint_mdp_viz", fmt="pdf", export_dot=True)

sim/rl/engine.py

@@ -3,8 +3,7 @@ import numpy as np
 import pandas as pd
 from abc import ABC, abstractmethod
 from typing import Dict, Any
-from environment import BusinessLogicConstraints
-
+from sim.rl.environment import BusinessLogicConstraints
 
 """
 An angine by default should have its own demand estimation mechanism from the observed observations whihc are the computer feature.
@@ -32,9 +31,12 @@ class BasePricingEngine(ABC):
         """
         pass
 
-    @abstractmethod
-    def update(obs, reward, done, info):
-        pass
+    def update(self, observation: Dict[str, Any], reward: float, done: bool, info: Dict[str, Any]) -> None:
+        """Default no-op update. Engines can override as needed."""
+        self.last_observation = observation
+        self.last_reward = reward
+        self.last_info = info
+
 
 
 
@@ -48,14 +50,14 @@ class WildPricingEngine(BasePricingEngine):
     def __init__(self, constraints: BusinessLogicConstraints, seed: int = 0):
         super().__init__(constraints, seed)
         # per-product unit costs (unknown to customers; known to platform)
-        self.unit_cost = self.rng.uniform(8.0, 40.0, size=self.c.product_catelogue_size).astype(np.float32)
+        self.unit_cost = self.rng.uniform(8.0, 40.0, size=self.c.product_catalogue_size).astype(np.float32)
         # online elasticity estimate (start moderately elastic)
-        self.e_hat = np.full((self.c.product_catelogue_size,), -1.3, dtype=np.float32)
+        self.e_hat = np.full((self.c.product_catalogue_size,), -1.3, dtype=np.float32)
         # EWMA state for log-log regression
-        self.mu_logp = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
-        self.mu_logq = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
-        self.cov_pq  = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
-        self.var_p   = np.ones(self.c.product_catelogue_size, dtype=np.float32)
+        self.mu_logp = np.zeros(self.c.product_catalogue_size, dtype=np.float32)
+        self.mu_logq = np.zeros(self.c.product_catalogue_size, dtype=np.float32)
+        self.cov_pq  = np.zeros(self.c.product_catalogue_size, dtype=np.float32)
+        self.var_p   = np.ones(self.c.product_catalogue_size, dtype=np.float32)
         # knobs typical in production
         self.lr = 0.08
         self.ewma = 0.05
@@ -140,7 +142,7 @@ class SimpleDemandEngine(BasePricingEngine):
 
     def compute_prices(self, current_prices: np.ndarray, observation: Dict[str, Any]) -> np.ndarray:
         self.step_count += 1
-        demand = observation.get('demand', np.zeros(self.c.product_catelogue_size, dtype=np.float32))
+        demand = _extract_demand(observation, self.c.product_catalogue_size)
         if self.prev_demand is None:
             self.prev_demand = demand.copy()
             return current_prices.copy()
@@ -187,15 +189,15 @@ class ThompsonSamplingEngine(BasePricingEngine):
     def __init__(self, constraints: BusinessLogicConstraints, seed: int = 0):
         super().__init__(constraints, seed)
         self.n_price_levels = 5
-        self.alpha = np.ones((self.c.product_catelogue_size, self.n_price_levels), dtype=np.float32)
-        self.beta = np.ones((self.c.product_catelogue_size, self.n_price_levels), dtype=np.float32)
+        self.alpha = np.ones((self.c.product_catalogue_size, self.n_price_levels), dtype=np.float32)
+        self.beta = np.ones((self.c.product_catalogue_size, self.n_price_levels), dtype=np.float32)
         self.price_grid = None
         self.last_actions = None
 
     def reset(self):
         super().reset()
-        self.alpha = np.ones((self.c.product_catelogue_size, self.n_price_levels), dtype=np.float32)
-        self.beta = np.ones((self.c.product_catelogue_size, self.n_price_levels), dtype=np.float32)
+        self.alpha = np.ones((self.c.product_catalogue_size, self.n_price_levels), dtype=np.float32)
+        self.beta = np.ones((self.c.product_catalogue_size, self.n_price_levels), dtype=np.float32)
         self.price_grid = None
         self.last_actions = None
 
@@ -206,10 +208,10 @@ class ThompsonSamplingEngine(BasePricingEngine):
             lo = current_prices * 0.7
             hi = current_prices * 1.3
             self.price_grid = np.linspace(lo, hi, self.n_price_levels).T
-        demand = observation.get('demand', np.zeros(self.c.product_catelogue_size, dtype=np.float32))
+        demand = _extract_demand(observation, self.c.product_catalogue_size)
         # update beliefs based on last action
         if self.last_actions is not None:
-            for i in range(self.c.product_catelogue_size):
+            for i in range(self.c.product_catalogue_size):
                 a = self.last_actions[i]
                 reward = demand[i]
                 if reward > 0.5:
@@ -217,11 +219,22 @@ class ThompsonSamplingEngine(BasePricingEngine):
                 else:
                     self.beta[i, a] += 1.0
         # thompson sampling: sample from posterior, pick best
-        new_prices = np.zeros(self.c.product_catelogue_size, dtype=np.float32)
-        actions = np.zeros(self.c.product_catelogue_size, dtype=int)
-        for i in range(self.c.product_catelogue_size):
+        new_prices = np.zeros(self.c.product_catalogue_size, dtype=np.float32)
+        actions = np.zeros(self.c.product_catalogue_size, dtype=int)
+        for i in range(self.c.product_catalogue_size):
             theta = self.rng.beta(self.alpha[i], self.beta[i]).astype(np.float32)
             actions[i] = int(np.argmax(theta))
             new_prices[i] = self.price_grid[i, actions[i]]
         self.last_actions = actions
         return np.clip(new_prices, self.c.system_min_price, self.c.system_max_price).astype(np.float32)
+
+
+def _extract_demand(observation: Dict[str, Any], n: int) -> np.ndarray:
+    if "elasticity" in observation and isinstance(observation["elasticity"], dict):
+        d = observation["elasticity"].get("demand")
+        if d is not None:
+            return np.asarray(d, dtype=np.float32)
+    d = observation.get("demand")
+    if d is not None:
+        return np.asarray(d, dtype=np.float32)
+    return np.zeros(n, dtype=np.float32)

sim/rl/environment.py

@@ -1,320 +1,244 @@
-from sys import intern
-import gymnasium as gym
-from gymnasium import spaces
-from matplotlib import interactive
-import numpy as np
+from __future__ import annotations
+
 from dataclasses import dataclass
-import pandas as pd
-from typing import Callable, Optional, Dict, Any, List
+from typing import Any, Dict, Optional, Tuple
 
-# "learner"  agent learning to optimize pricing
-# "agent"  part of environment creating demand signals that learner processes
+import numpy as np
+
+try:
+    import gymnasium as gym
+    from gymnasium import spaces
+except ImportError as e:
+    raise ImportError("sim.rl.environment requires gymnasium") from e
+
+from sim.case.thesis_simplified.coi import COIWindow, coi_erosion, compute_coi_window
+from sim.case.thesis_simplified.separability import estimate_alpha as estimate_session_alpha
+from sim.case.thesis_simplified.simplified import Limbo, Session, put_prices_to_market
+from sim.rl.thesis_core import aggregate_demand_by_product, aggregate_purchases, constrain_prices
+
+
+@dataclass(frozen=True)
+class BusinessLogicConstraints:
+    product_catalogue_size: int = 100
+    max_steps: int = 2000
+    sessions_per_step: int = 250
 
-@dataclass
-class BusinessLogicConstraints():
-    max_price_adjustment: float = 0.30
     system_max_price: float = 500.0
     system_min_price: float = 1.0
-    product_catelogue_size: int = 100
-    episode_length: int = 200
-    sessions_per_step: int = 250
-    agent_share: float = 0.25
-    agent_recon_multiplier: float = 6.0
-    agent_purchase_probability: float = 0.20
+    max_price_adjustment: float = 0.30
+    min_margin_pct: float = 0.05
+
+    agent_share: float = 0.2
+    alpha_drift: float = 0.0
+    alpha_bounds: tuple[float, float] = (0.0, 0.8)
+
     coi_strength: float = 0.25
-    coi_threshold: float = 4.0
-    coi_sigmoid_temp: float = 1.25
-    base_human_demand: float = 0.08
-    base_agent_demand: float = 0.05
-    human_price_elasticity: float = -1.2 # assumptions here
-    agent_price_elasticity: float = -0.6
-    w_agent_loss: float = 1.0
     w_volatility: float = 5.0
     w_estimation_error: float = 0.25
+
     seed: int = 7
 
 
-def _sigmoid(x: np.ndarray) -> np.ndarray:
-    return 1.0 / (1.0 + np.exp(-x))
-
-class CommercePlatform:
-    """
-    This is just an extension of the state management for the environment, it does not implement anything dynamic just helps us simulate demand.
-    """
-    def __init__(self,
-                 product_catelogue_size: int,
-                 max_price: float,
-                 min_price: float,
-                 constraints: BusinessLogicConstraints):
-        self.product_catelogue_size = product_catelogue_size
-        self.product_supply = np.random.uniform(low=10, high=50, size=(self.product_catelogue_size,))
-        self.max_price = max_price
-        self.min_price = min_price
-        self.constraints = constraints
-        self.simulation_history: List[Dict[str, Any]] = []
-        self._rng = np.random.default_rng(constraints.seed)
-        self._last_interaction_df: pd.DataFrame = pd.DataFrame()
-
-
-    def setup_true_demand(self, prices: np.ndarray) -> Dict[str, np.ndarray]:
-        # ground truth purchase propensities
-        p = np.clip(prices, self.min_price, self.max_price)
-        pn = p / self.max_price
-        human_prob = self.constraints.base_human_demand * (pn ** self.constraints.human_price_elasticity)
-        agent_prob = self.constraints.base_agent_demand * (pn ** self.constraints.agent_price_elasticity)
-        return {
-            "human_purchase_prob": np.clip(human_prob, 0.0, 0.95),
-            "agent_purchase_prob": np.clip(agent_prob, 0.0, 0.95)
-        }
-
-    def _load_behavioral_profile(actor : str, demand_forcing):
-        """
-        This returns a markov chain with average weights which we get from interaction data of our experiments.
-        This defines transition probabilities between different events:
-        search -> view_item_price_binN: 0.7
-        view_item_price_binN -> add_to_cart: 0.2
-        we also must reweight with the demand_forcing vector or purchase probabilities per-product
-        """
-
-
-    def _simulate_sessions(self, base_prices: np.ndarray) -> pd.DataFrame:
-        demand = self.setup_true_demand(base_prices)
-        human_pprob = demand["human_purchase_prob"]
-        agent_pprob = demand["agent_purchase_prob"]
-        events: List[Dict[str, Any]] = []
-        T = self.constraints.sessions_per_step
-        n_agent_sessions = int(round(T * self.constraints.agent_share))
-        n_human_sessions = T - n_agent_sessions
-        n_agent_ids = max(1, n_agent_sessions // 2)
-        session_map = {
-            'humans': n_human_sessions,
-            'agents': n_agent_ids
-        }
-        pprob_map = {
-            'humans': human_pprob,
-            'agents': agent_pprob
-        }
-        joint_events = []
-        for actor, n_sessions in session_map.items():
-            bp = _load_behavioral_profile(actor, pprob_map[actor])
-            counter = 0
-            events = []
-            while counter < n_sessions:
-                session_events = []
-                while len(session_events) == 0 or session_events[-1]['action'] == 'checkout':
-                    interaction_event = bp.sample(self._rng)
-                    interaction_event['session_id'] = f'{actor}_{counter:06d}'
-                    # TODO any other assignments
-                    session_events.append(interaction_event)
-                events.extend(session_events)
-                counter += 1
-            joint_events.extend(events)
-
-        return pd.DataFrame(joint_events)
-
-    def compute_interaction_features(self, interaction_df: pd.DataFrame) -> Dict[str, float]:
-        if interaction_df.empty:
-            return {"mean_sale_price": 0.0, "look_to_book": 0.0}
-        purchases = interaction_df[interaction_df["action"] == "purchase"]
-        mean_sale_price = float(purchases["price_paid"].mean()) if not purchases.empty else 0.0
-        views = float((interaction_df["action"] == "view").sum())
-        buys = float((interaction_df["action"] == "purchase").sum())
-        return {"mean_sale_price": mean_sale_price, "look_to_book": float(views / (buys + 1e-6))}
-
-    def _session_feature_table(self, df: pd.DataFrame) -> pd.DataFrame:
-        # TODO: adapt this
-        if df.empty:
-            return pd.DataFrame()
-        g = df.groupby("session_id", sort=False)
-        session_duration = g["t"].max() - g["t"].min()
-        total_interactions = g.size()
-        avg_time_between = g["t"].apply(lambda x: float(np.diff(np.sort(x.to_numpy())).mean()) if len(x) > 1 else 0.0)
-        interaction_velocity = total_interactions / (session_duration + 1e-6)
-        views = g.apply(lambda x: int((x["action"] == "view").sum()), include_groups=False)
-        cart_adds = g.apply(lambda x: int((x["action"] == "cart").sum()), include_groups=False)
-        purchases = g.apply(lambda x: int((x["action"] == "purchase").sum()), include_groups=False)
-        conversion_rate = purchases / (views + 1e-6)
-        is_agent = g["actor"].apply(lambda s: bool((s == "agent").any()), include_groups=False)
-
-        return pd.DataFrame({
-            "session_duration_sec": session_duration.astype(float),
-            "avg_time_between_events": avg_time_between.astype(float),
-            "total_interactions": total_interactions.astype(int),
-            "interaction_velocity": interaction_velocity.astype(float),
-            "item_views": views.astype(int),
-            "cart_adds": cart_adds.astype(int),
-            "purchases": purchases.astype(int),
-            "conversion_rate": conversion_rate.astype(float),
-            "is_agent": is_agent.astype(bool),
-        }).reset_index()
-
-    def get_interaction_data(self) -> np.ndarray:
-        if self._last_interaction_df.empty:
-            return np.array([], dtype=object)
-        return self._last_interaction_df.to_dict(orient="records")
+def make_env(constraints: Optional[BusinessLogicConstraints] = None) -> "PHANTOMEnv":
+    return PHANTOMEnv(constraints=constraints or BusinessLogicConstraints())
 
 
 class PHANTOMEnv(gym.Env):
-    metadata = {"render_modes": []}
+    metadata = {"render_modes": ["human", "ansi"]}
 
-    def __init__(self, constraints):
+    def __init__(self, constraints: Optional[BusinessLogicConstraints] = None):
         super().__init__()
-        self.constraints = BusinessLogicConstraints()
-        self.action_space = spaces.Box(low=-self.constraints.max_price_adjustment,
-                                       high=self.constraints.max_price_adjustment,
-                                       shape=(self.constraints.product_catelogue_size,), dtype=np.float32)
-        self.observation_space = spaces.Dict({
-            "elasticity": spaces.Dict({
-                "price": spaces.Box(
-                    low=np.full((self.constraints.product_catelogue_size,), self.constraints.system_min_price, dtype=np.float32),
-                    high=np.full((self.constraints.product_catelogue_size,), self.constraints.system_max_price, dtype=np.float32),
-                    dtype=np.float32),
-                "demand": spaces.Box(
-                    low=np.zeros((self.constraints.product_catelogue_size,), dtype=np.float32),
-                    high=np.full((self.constraints.product_catelogue_size,), 1e6, dtype=np.float32),
-                    dtype=np.float32),
-            })
-            # TODO: define more features that we compute from the interaction data
-        })
-        self.commerce_platform = CommercePlatform(
-            product_catelogue_size=self.constraints.product_catelogue_size,
-            max_price=self.constraints.system_max_price,
-            min_price=self.constraints.system_min_price,
-            constraints=self.constraints)
-        self._rng = np.random.default_rng(self.constraints.seed)
-        self.t = 0
-        self._prev_prices: Optional[np.ndarray] = None
-        self.state: Dict[str, Any] = {}
+        self.c = constraints or BusinessLogicConstraints()
+        self.n = int(self.c.product_catalogue_size)
+
+        self._rng = np.random.default_rng(self.c.seed)
+        self._t = 0
+        self._alpha_true = float(self.c.agent_share)
+        self._alpha_hat = float(self.c.agent_share)
+        self._costs = np.zeros(self.n, dtype=np.float32)
+        self._refs = np.zeros(self.n, dtype=np.float32)
+        self._prices: Optional[np.ndarray] = None
+        self._last_sessions: list[Session] = []
+        self._last_coi: COIWindow | None = None
+        self._limbo = Limbo()
+
+        self.action_space = spaces.Box(
+            low=np.full((self.n,), self.c.system_min_price, dtype=np.float32),
+            high=np.full((self.n,), self.c.system_max_price, dtype=np.float32),
+            dtype=np.float32,
+        )
+        self.observation_space = spaces.Dict(
+            {
+                "elasticity": spaces.Dict(
+                    {
+                        "price": spaces.Box(
+                            low=np.full((self.n,), self.c.system_min_price, dtype=np.float32),
+                            high=np.full((self.n,), self.c.system_max_price, dtype=np.float32),
+                            dtype=np.float32,
+                        ),
+                        "demand": spaces.Box(
+                            low=np.zeros((self.n,), dtype=np.float32),
+                            high=np.full((self.n,), 1e9, dtype=np.float32),
+                            dtype=np.float32,
+                        ),
+                    }
+                ),
+                "market": spaces.Dict(
+                    {
+                        "alpha_hat": spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
+                        "revenue_rate": spaces.Box(low=0.0, high=1e12, shape=(1,), dtype=np.float32),
+                        "conversion_rate": spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
+                        "price_volatility": spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
+                    }
+                ),
+                "cost": spaces.Box(
+                    low=np.zeros((self.n,), dtype=np.float32),
+                    high=np.full((self.n,), self.c.system_max_price, dtype=np.float32),
+                    dtype=np.float32,
+                ),
+            }
+        )
+
+    def _reset_catalogue(self) -> None:
+        self._costs = self._rng.uniform(15.0, 60.0, size=self.n).astype(np.float32)
+        margins = self._rng.uniform(0.2, 0.6, size=self.n).astype(np.float32)
+        self._refs = (self._costs * (1.0 + margins)).astype(np.float32)
+        self._prices = self._refs.copy()
+
+    def _observe_market(
+        self, prices: np.ndarray
+    ) -> tuple[list[Session], Dict[str, float], np.ndarray, np.ndarray, float, float, int]:
+        sessions, demand_map = put_prices_to_market(
+            prices,
+            costs=self._costs,
+            alpha=self._alpha_true,
+            n_sessions=int(self.c.sessions_per_step),
+            seed=int(self._rng.integers(0, 2**31 - 1)),
+        )
+        demand_by_product = aggregate_demand_by_product(sessions, demand_map, self.n)
+        purchases, revenue, cost, n_agents = aggregate_purchases(sessions, self._costs, self.n)
+        conversion = float(np.sum(purchases) / max(len(sessions), 1))
+        return sessions, demand_map, demand_by_product, purchases, revenue, cost, n_agents
+
+    def _update_alpha_hat(self, sessions: list[Session]) -> float:
+        scores = [estimate_session_alpha(s) for s in sessions if s.events]
+        if not scores:
+            return self._alpha_hat
+        alpha_step = float(np.mean(scores))
+        self._alpha_hat = 0.8 * self._alpha_hat + 0.2 * alpha_step
+        self._alpha_hat = float(np.clip(self._alpha_hat, 0.0, 1.0))
+        return self._alpha_hat
+
+    def _reward(self, prices: np.ndarray, revenue: float, cost: float, volatility: float) -> float:
+        profit = float(revenue - cost)
+        coi_leak = float(self._last_coi.leak) if self._last_coi else 0.0
+        alpha_err = abs(self._alpha_hat - self._alpha_true)
+        return profit - self.c.coi_strength * coi_leak - self.c.w_volatility * volatility - self.c.w_estimation_error * alpha_err
+
+    def _build_obs(
+        self,
+        prices: np.ndarray,
+        demand_by_product: np.ndarray,
+        revenue: float,
+        conversion: float,
+        volatility: float,
+    ) -> Dict[str, Any]:
+        return {
+            "elasticity": {"price": prices.astype(np.float32), "demand": demand_by_product.astype(np.float32)},
+            "market": {
+                "alpha_hat": np.array([self._alpha_hat], dtype=np.float32),
+                "revenue_rate": np.array([revenue], dtype=np.float32),
+                "conversion_rate": np.array([conversion], dtype=np.float32),
+                "price_volatility": np.array([volatility], dtype=np.float32),
+            },
+            "cost": self._costs.astype(np.float32),
+        }
 
     def reset(self, seed: Optional[int] = None, options: Optional[dict] = None):
         super().reset(seed=seed)
         if seed is not None:
             self._rng = np.random.default_rng(seed)
-            self.commerce_platform._rng = np.random.default_rng(seed)
-        self.t = 0
-        init_prices = self._rng.uniform(low=60.0, high=140.0, size=(self.constraints.product_catelogue_size,)).astype(np.float32)
-        self._prev_prices = init_prices.copy()
-        self.state = {
-            "elasticity": {
-                "price": init_prices,
-                "demand": np.zeros((self.constraints.product_catelogue_size,), dtype=np.float32),
-            }
-        }
-        return self.state, {}
+        self._t = 0
+        self._alpha_true = float(np.clip(self.c.agent_share, *self.c.alpha_bounds))
+        self._alpha_hat = float(self.c.agent_share)
+        self._reset_catalogue()
+        self._limbo = Limbo()
+        self._last_sessions = []
+        self._last_coi = None
 
-    def step(self, action: np.ndarray):
-        self.t += 1
-        base_prices = self.state["elasticity"]["price"].astype(np.float32)
-        new_prices = np.clip(base_prices * (1.0 + action.astype(np.float32)),
-                           self.constraints.system_min_price,
-                           self.constraints.system_max_price).astype(np.float32)
+        prices = self._prices if self._prices is not None else np.zeros(self.n, dtype=np.float32)
+        obs = self._build_obs(prices, np.zeros(self.n, dtype=np.float32), 0.0, 0.0, 0.0)
+        return obs, {"alpha_true": self._alpha_true}
 
-        self.state["elasticity"]["price"] = new_prices
-        # TODO: use the commerce platform to simulate sessions
-        interactions_df = self.commerce_platform._simulate_sessions(new_prices)
-        result = self.commerce_platform.compute_interaction_features(interactions_df)
-        # TODO: implement COI computation to use in reward
-        COI = 0.0
+    def step(self, action: np.ndarray) -> Tuple[Dict[str, Any], float, bool, bool, Dict[str, Any]]:
+        if self._prices is None:
+            raise RuntimeError("reset() must be called before step()")
 
-        volatility = 0.0 if self._prev_prices is None else \
-            float(np.mean(np.abs((new_prices - self._prev_prices) / (self._prev_prices + 1e-6))))
-        self._prev_prices = new_prices.copy()
+        prev = self._prices
+        prices = constrain_prices(
+            prev,
+            np.asarray(action, dtype=np.float32),
+            costs=self._costs,
+            min_price=float(self.c.system_min_price),
+            max_price=float(self.c.system_max_price),
+            max_adjustment=float(self.c.max_price_adjustment),
+            min_margin_pct=float(self.c.min_margin_pct),
+        )
+        self._prices = prices
+        self._limbo.add_update("prices", prices)
 
-        revenue_observed = float(result["revenue_observed"])
-        agent_loss = float(result["agent_loss"])
+        sessions, demand_map, demand_by_product, purchases, revenue, cost, n_agents = self._observe_market(prices)
+        self._last_sessions = sessions
+        self._limbo.add_update("demand", demand_map)
 
-        reward = (revenue_observed
-                  - COI
-                  - self.constraints.w_agent_loss * agent_loss
-                  - self.constraints.w_volatility * volatility
-                  - self.constraints.w_estimation_error
-                  )
+        self._update_alpha_hat(self._last_sessions)
+        self._last_coi = compute_coi_window(self._last_sessions, self._costs, demand_mapping=demand_map)
 
-        terminated = self.t >= self.constraints.episode_length
+        self._alpha_true = float(np.clip(self._alpha_true + self.c.alpha_drift, *self.c.alpha_bounds))
+        volatility = float(np.std((prices - prev) / (prev + 1e-6)))
+        reward = float(self._reward(prices, revenue, cost, volatility))
+        conversion = float(np.sum(purchases) / max(len(self._last_sessions), 1))
+
+        self._t += 1
+        terminated = self._t >= int(self.c.max_steps)
+
+        obs = self._build_obs(prices, demand_by_product, revenue, conversion, min(volatility, 1.0))
         info = {
-            "t": self.t,
-            "revenue_observed": revenue_observed,
-            "revenue_oracle": float(result["revenue_oracle"]),
-            "agent_loss": agent_loss,
-            "ux_volatility": volatility,
-            "mean_internal_error": err_mean,
-            "look_to_book": float(result["interaction_features"].get("look_to_book", 0.0)),
-            "mean_sale_price": float(result["interaction_features"].get("mean_sale_price", 0.0)),
-            "true_human_purchases_total": float(np.sum(result["true_human_demand"])),
-            "true_agent_purchases_total": float(np.sum(result["true_agent_purchases"])),
+            "step": self._t,
+            "reward": reward,
+            "revenue": float(revenue),
+            "profit": float(revenue - cost),
+            "n_sessions": int(self.c.sessions_per_step),
+            "n_agents": int(n_agents),
+            "alpha_true": float(self._alpha_true),
+            "alpha_hat": float(self._alpha_hat),
+            "alpha_error": float(abs(self._alpha_hat - self._alpha_true)),
+            "price_std": float(np.std(prices)),
+            "price_volatility": float(volatility),
         }
-        return self.state, float(reward), terminated, False, info
+        if self._last_coi is not None:
+            info.update(
+                {
+                    "coi_policy": float(self._last_coi.policy),
+                    "coi_agent": float(self._last_coi.agent),
+                    "coi_leakage": float(self._last_coi.leak),
+                    "coi_survival": float(self._last_coi.survival_ratio),
+                    "coi_erosion": float(coi_erosion(self._last_coi.policy, self._last_coi.agent)),
+                }
+            )
+        return obs, reward, terminated, False, info
 
+    def render(self, mode: str = "human") -> str | None:
+        if self._prices is None:
+            return None
+        out = (
+            f"t={self._t}/{self.c.max_steps} "
+            f"alpha_true={self._alpha_true:.3f} alpha_hat={self._alpha_hat:.3f} "
+            f"price_std={float(np.std(self._prices)):.2f}"
+        )
+        if mode == "human":
+            print(out)
+        return out
 
-if __name__ == "__main__":
-    import matplotlib.pyplot as plt
-    from collections import defaultdict
-
-    runs = {}
-    for use_defense in (False, True):
-        env = PHANTOMEnv(use_defense=use_defense)
-        obs, _ = env.reset(seed=42)
-        metrics = defaultdict(list)
-        total_reward = 0.0
-        done = False
-
-        while not done:
-            action = env.action_space.sample()
-            obs, reward, done, _, info = env.step(action)
-            total_reward += reward
-            p_mean = float(np.mean(obs["elasticity"]["price"]))
-            q_mean = float(np.mean(obs["elasticity"]["demand"]))
-            p_std = float(np.std(obs["elasticity"]["price"]))
-
-            metrics['t'].append(info['t'])
-            metrics['price_mean'].append(p_mean)
-            metrics['price_std'].append(p_std)
-            metrics['demand_mean'].append(q_mean)
-            metrics['revenue_observed'].append(info['revenue_observed'])
-            metrics['revenue_oracle'].append(info['revenue_oracle'])
-            metrics['agent_loss'].append(info['agent_loss'])
-            metrics['ux_volatility'].append(info['ux_volatility'])
-            metrics['look_to_book'].append(info['look_to_book'])
-            metrics['reward'].append(reward)
-            metrics['human_purchases'].append(info['true_human_purchases_total'])
-            metrics['agent_purchases'].append(info['true_agent_purchases_total'])
-
-            if info['t'] % 20 == 0 or done:
-                print(f"defense={'ON ' if use_defense else 'OFF'} t={info['t']:03d} p={p_mean:6.2f}±{p_std:4.2f} "
-                      f"q={q_mean:6.2f} rev={info['revenue_observed']:7.2f} oracle={info['revenue_oracle']:7.2f} "
-                      f"loss={info['agent_loss']:6.2f} ux={info['ux_volatility']:.3f} "
-                      f"ltb={info['look_to_book']:5.2f} r={reward:7.2f}")
-
-        runs[use_defense] = metrics
-        print(f"defense={'ON ' if use_defense else 'OFF'} total_reward={total_reward:.2f}\n")
-
-    fig, axes = plt.subplots(3, 3, figsize=(15, 12))
-    fig.suptitle('PHANTOM Environment: Defense OFF vs ON', fontsize=14, fontweight='bold')
-
-    plot_configs = [
-        ('price_mean', 'Mean Price', 'Price'),
-        ('demand_mean', 'Mean Demand Estimate', 'Demand'),
-        ('revenue_observed', 'Revenue (Observed)', 'Revenue'),
-        ('agent_loss', 'Agent Loss (Oracle - Observed)', 'Loss'),
-        ('ux_volatility', 'UX Volatility (Price Change)', 'Volatility'),
-        ('look_to_book', 'Look-to-Book Ratio', 'Ratio'),
-        ('reward', 'Step Reward', 'Reward'),
-        ('human_purchases', 'Human Purchases', 'Count'),
-        ('agent_purchases', 'Agent Purchases', 'Count'),
-    ]
-
-    for idx, (key, title, ylabel) in enumerate(plot_configs):
-        ax = axes[idx // 3, idx % 3]
-        for use_defense, label, color in [(False, 'No Defense', 'red'), (True, 'With Defense', 'blue')]:
-            m = runs[use_defense]
-            ax.plot(m['t'], m[key], label=label, color=color, alpha=0.7, linewidth=1.5)
-        ax.set_xlabel('Step')
-        ax.set_ylabel(ylabel)
-        ax.set_title(title, fontsize=10, fontweight='bold')
-        ax.legend(loc='best', fontsize=8)
-        ax.grid(True, alpha=0.3)
-
-    plt.tight_layout()
-    plt.savefig('phantom_env_comparison.png', dpi=150, bbox_inches='tight')
-    print("Plot saved to phantom_env_comparison.png")
-    plt.show()
+    def close(self) -> None:
+        return

sim/rl/jax_core/__init__.py

@@ -0,0 +1,11 @@
+"""JAX-accelerated simulation core for PHANTOM environment."""
+from .transitions import TransitionData, compile_transitions, fallback_transitions, JAX_AVAILABLE
+from .simulation import SessionBatch, SimResult, sample_sessions, compute_metrics
+from .features import session_features, compute_session_transitions
+from .separability import compute_divergences, estimate_alpha_batch
+
+__all__ = [
+    "JAX_AVAILABLE", "TransitionData", "compile_transitions", "fallback_transitions",
+    "SessionBatch", "SimResult", "sample_sessions", "compute_metrics",
+    "session_features", "compute_session_transitions", "compute_divergences", "estimate_alpha_batch",
+]

sim/rl/jax_core/features.py

@@ -0,0 +1,69 @@
+"""Vectorized session feature extraction."""
+import numpy as np
+from .transitions import N_STATES, PURCHASE_IDX, CART_IDX
+from .simulation import SessionBatch
+
+try:
+    import jax.numpy as jnp
+    from jax import jit
+    JAX_AVAILABLE = True
+except ImportError:
+    jnp, JAX_AVAILABLE = np, False
+    def jit(f): return f
+
+@jit
+def extract_features(states, dwells, lengths):
+    """Extract per-session features. Returns (n_sess, 9) array."""
+    n, max_len = states.shape
+    mask = jnp.arange(max_len)[None,:] < lengths[:,None]
+    duration = jnp.sum(dwells * mask, axis=1)
+    total = lengths.astype(jnp.float32)
+    count = lambda idx: jnp.sum((states == idx) & mask, axis=1).astype(jnp.float32)
+    views, learn, carts, purchases = count(1), count(2), count(3), count(4)
+    velocity = total / (duration + 1e-6)
+    conversion = purchases / (views + 1e-6)
+    avg_dwell = duration / (total + 1e-6)
+    return jnp.stack([duration, avg_dwell, total, velocity, views, carts, purchases, learn, conversion], axis=1)
+
+def session_features(batch: SessionBatch) -> np.ndarray:
+    if JAX_AVAILABLE:
+        return np.asarray(extract_features(jnp.array(batch.states), jnp.array(batch.dwells), jnp.array(batch.lengths)))
+    # numpy fallback
+    n, max_len = batch.states.shape
+    mask = np.arange(max_len)[None,:] < batch.lengths[:,None]
+    duration = np.sum(batch.dwells * mask, axis=1)
+    total = batch.lengths.astype(np.float32)
+    count = lambda idx: np.sum((batch.states == idx) & mask, axis=1).astype(np.float32)
+    views, learn, carts, purchases = count(1), count(2), count(3), count(4)
+    return np.stack([duration, duration/(total+1e-6), total, total/(duration+1e-6), views, carts, purchases, learn, purchases/(views+1e-6)], axis=1)
+
+@jit
+def session_transitions(states, lengths, n_states=N_STATES):
+    """Compute empirical transition counts per session. Returns (n_sess, n_states, n_states)."""
+    n, max_len = states.shape
+    mask = jnp.arange(max_len - 1)[None,:] < (lengths[:,None] - 1)
+    src, dst = states[:, :-1], states[:, 1:]
+    # handle -1 padding by clamping to valid range
+    src_c, dst_c = jnp.clip(src, 0, n_states-1), jnp.clip(dst, 0, n_states-1)
+    valid = mask & (src >= 0) & (dst >= 0)
+    def per_session(i):
+        s, d, v = src_c[i], dst_c[i], valid[i]
+        trans = (jnp.eye(n_states)[s,:,None] * jnp.eye(n_states)[d,None,:]).sum(0) * v[:,None,None]
+        return trans.sum(0)
+    # vmap not ideal here, use manual loop for clarity
+    trans = jnp.stack([per_session(i) for i in range(n)])
+    row_sums = trans.sum(axis=-1, keepdims=True)
+    return trans / (row_sums + 1e-10)
+
+def compute_session_transitions(batch: SessionBatch) -> np.ndarray:
+    if JAX_AVAILABLE:
+        return np.asarray(session_transitions(jnp.array(batch.states), jnp.array(batch.lengths)))
+    # numpy fallback
+    n, max_len = batch.states.shape
+    trans = np.zeros((n, N_STATES, N_STATES), dtype=np.float32)
+    for i in range(n):
+        for t in range(batch.lengths[i] - 1):
+            s, d = batch.states[i, t], batch.states[i, t+1]
+            if s >= 0 and d >= 0: trans[i, s, d] += 1
+    row_sums = trans.sum(axis=-1, keepdims=True)
+    return trans / (row_sums + 1e-10)

sim/rl/jax_core/separability.py

@@ -0,0 +1,43 @@
+"""Vectorized KL divergence for separability scoring."""
+import numpy as np
+from typing import Tuple
+
+try:
+    import jax.numpy as jnp
+    from jax import jit
+    JAX_AVAILABLE = True
+except ImportError:
+    jnp, JAX_AVAILABLE = np, False
+    def jit(f): return f
+
+@jit
+def batch_kl(P, Q_human, Q_agent, eps=1e-10):
+    """Compute KL(P||Q) for batched P. P:(n,s,s), Q:(s,s). Returns (delta_h, delta_a) each (n,)."""
+    p = P + eps
+    p = p / p.sum(axis=-1, keepdims=True)
+    qh, qa = Q_human[None] + eps, Q_agent[None] + eps
+    delta_h = jnp.sum(p * jnp.log(p / qh), axis=(1, 2))
+    delta_a = jnp.sum(p * jnp.log(p / qa), axis=(1, 2))
+    return delta_h, delta_a
+
+def compute_divergences(session_trans: np.ndarray, ref_human: np.ndarray, ref_agent: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """Compute KL divergence of each session from human/agent prototypes."""
+    if JAX_AVAILABLE:
+        dh, da = batch_kl(jnp.array(session_trans), jnp.array(ref_human), jnp.array(ref_agent))
+        return np.asarray(dh), np.asarray(da)
+    # numpy fallback
+    eps = 1e-10
+    p = session_trans + eps
+    p = p / p.sum(axis=-1, keepdims=True)
+    qh, qa = ref_human[None] + eps, ref_agent[None] + eps
+    delta_h = np.sum(p * np.log(p / qh), axis=(1, 2))
+    delta_a = np.sum(p * np.log(p / qa), axis=(1, 2))
+    return delta_h, delta_a
+
+def estimate_alpha_batch(prob_agent: np.ndarray, delta_h: np.ndarray, delta_a: np.ndarray, temp: float = 1.0) -> np.ndarray:
+    """Vectorized alpha estimation from classifier probs and divergences."""
+    mass = delta_h + delta_a
+    ratio = np.where(mass > 1e-8, delta_a / mass, 0.5)
+    blended = 0.5 * prob_agent + 0.5 * ratio
+    if temp <= 0: return np.clip(blended, 0.0, 1.0)
+    return np.clip(1.0 / (1.0 + np.exp(-temp * (blended - 0.5))), 0.0, 1.0)

sim/rl/jax_core/simulation.py

@@ -0,0 +1,116 @@
+"""Vectorized Markov chain session sampling with JAX."""
+from typing import NamedTuple, Tuple
+import numpy as np
+from functools import partial
+
+try:
+    import jax, jax.numpy as jnp
+    from jax import lax
+    JAX_AVAILABLE = True
+except ImportError:
+    JAX_AVAILABLE = False
+
+from .transitions import TransitionData, N_STATES, TERM_IDX, PURCHASE_IDX, CART_IDX
+
+class SessionBatch(NamedTuple):
+    states: np.ndarray      # (n_sess, max_len) state indices, -1=padding
+    dwells: np.ndarray      # (n_sess, max_len) dwell times
+    products: np.ndarray    # (n_sess,) product index per session
+    actors: np.ndarray      # (n_sess,) 0=human, 1=agent
+    lengths: np.ndarray     # (n_sess,) actual session length
+
+class SimResult(NamedTuple):
+    demand_human: np.ndarray
+    demand_agent: np.ndarray
+    revenue: float
+    revenue_oracle: float
+    agent_loss: float
+    coi: float
+    look_to_book: float
+    mean_sale_price: float
+    n_human_purchases: int
+    n_agent_purchases: int
+    sessions: SessionBatch
+
+if JAX_AVAILABLE:
+    @partial(jax.jit, static_argnums=(5,6,7))
+    def _sample_sessions_jax(key, T_human, T_agent, dwell_human, dwell_agent, n_human, n_agent, max_steps):
+        n = n_human + n_agent
+        k1, k2, k3, k4 = jax.random.split(key, 4)
+        actors = jnp.concatenate([jnp.zeros(n_human, dtype=jnp.int32), jnp.ones(n_agent, dtype=jnp.int32)])
+        T = jnp.where(actors[:,None,None]==0, T_human[None], T_agent[None])  # (n,6,6)
+        dwell_p = jnp.where(actors[:,None,None]==0, dwell_human[None], dwell_agent[None])  # (n,6,2)
+
+        def step(carry, _):
+            s, active, k = carry
+            k, k1, k2 = jax.random.split(k, 3)
+            probs = T[jnp.arange(n), s]  # (n,6)
+            nxt = jax.random.categorical(k1, jnp.log(probs + 1e-10))
+            nxt = jnp.where(active, nxt, -1)
+            shape = dwell_p[jnp.arange(n), s, 0]
+            scale = dwell_p[jnp.arange(n), s, 1]
+            dwell = jnp.maximum(0.3, jax.random.gamma(k2, shape) * scale)
+            still = active & (nxt != TERM_IDX) & (nxt >= 0)
+            return (nxt, still, k), (nxt, dwell)
+
+        init = (jnp.zeros(n, dtype=jnp.int32), jnp.ones(n, dtype=jnp.bool_), k3)
+        _, (states, dwells) = lax.scan(step, init, None, length=max_steps)
+        states, dwells = states.T, dwells.T  # (n, max_steps)
+        is_term = (states == -1) | (states == TERM_IDX)
+        lengths = jnp.argmax(is_term, axis=1) + 1
+        lengths = jnp.where(jnp.any(is_term, axis=1), lengths, max_steps)
+        return states, dwells, actors, lengths
+
+def sample_sessions(key, trans: TransitionData, n_human: int, n_agent: int, n_products: int, max_steps: int = 40) -> SessionBatch:
+    if JAX_AVAILABLE:
+        k1, k2 = jax.random.split(key)
+        states, dwells, actors, lengths = _sample_sessions_jax(k1, trans.human_T, trans.agent_T, trans.human_dwell, trans.agent_dwell, n_human, n_agent, max_steps)
+        products = jax.random.randint(k2, (n_human + n_agent,), 0, n_products)
+        return SessionBatch(np.asarray(states), np.asarray(dwells), np.asarray(products), np.asarray(actors), np.asarray(lengths))
+    # numpy fallback
+    rng = np.random.default_rng(int(key[0]) if hasattr(key, '__getitem__') else 42)
+    n = n_human + n_agent
+    actors = np.concatenate([np.zeros(n_human, dtype=np.int32), np.ones(n_agent, dtype=np.int32)])
+    products = rng.integers(0, n_products, size=n)
+    states, dwells = np.full((n, max_steps), -1, dtype=np.int32), np.zeros((n, max_steps), dtype=np.float32)
+    lengths = np.zeros(n, dtype=np.int32)
+    for i in range(n):
+        T = trans.human_T if actors[i] == 0 else trans.agent_T
+        dp = trans.human_dwell if actors[i] == 0 else trans.agent_dwell
+        s, t = 0, 0
+        while t < max_steps and s != TERM_IDX:
+            states[i, t] = s
+            dwells[i, t] = max(0.3, rng.gamma(dp[s, 0], dp[s, 1]))
+            s = rng.choice(N_STATES, p=T[s])
+            t += 1
+        lengths[i] = t
+    return SessionBatch(states, dwells, products, actors, lengths)
+
+def compute_metrics(batch: SessionBatch, prices: np.ndarray, unit_cost: np.ndarray, base_price: np.ndarray) -> SimResult:
+    purchased = np.any(batch.states == PURCHASE_IDX, axis=1)
+    human_mask, agent_mask = batch.actors == 0, batch.actors == 1
+    human_purch, agent_purch = purchased & human_mask, purchased & agent_mask
+    demand_h = np.bincount(batch.products[human_purch], minlength=len(prices)).astype(np.float32)
+    demand_a = np.bincount(batch.products[agent_purch], minlength=len(prices)).astype(np.float32)
+    # revenue and oracle
+    purch_products = batch.products[purchased]
+    revenue = float(np.sum(prices[purch_products]))
+    revenue_oracle = float(np.sum(base_price[purch_products]))
+    # agent loss: base_price - price_paid for agent purchases (agents gaming the system)
+    agent_products = batch.products[agent_purch]
+    agent_loss = float(np.sum(base_price[agent_products] - prices[agent_products]))
+    # COI: margin - expected_premium*0.5 for human purchases
+    human_products = batch.products[human_purch]
+    if len(human_products) > 0:
+        margin = float(np.mean(prices[human_products] - unit_cost[human_products]))
+        premium = float(np.mean(base_price[human_products] - prices[human_products]))
+        coi = max(0.0, margin - premium * 0.5)
+    else:
+        coi = 0.0
+    # look to book: views / purchases
+    views = float(np.sum(batch.states == 1))  # view_item_page = index 1
+    n_purch = int(purchased.sum())
+    look_to_book = views / (n_purch + 1e-6)
+    mean_sale = float(np.mean(prices[purch_products])) if n_purch > 0 else 0.0
+    return SimResult(demand_h, demand_a, revenue, revenue_oracle, agent_loss, coi, look_to_book, mean_sale,
+                     int(human_purch.sum()), int(agent_purch.sum()), batch)

sim/rl/jax_core/transitions.py

@@ -0,0 +1,47 @@
+"""Dense transition matrices for JAX Markov chain sampling."""
+from dataclasses import dataclass
+import numpy as np
+
+try:
+    import jax.numpy as jnp
+    JAX_AVAILABLE = True
+except ImportError:
+    jnp, JAX_AVAILABLE = np, False
+
+STATES = ["session_start", "view_item_page", "learn_more_about_item", "add_item_to_cart", "purchase_complete", "session_end"]
+S2I = {s: i for i, s in enumerate(STATES)}
+N_STATES, TERM_IDX, PURCHASE_IDX, CART_IDX = len(STATES), 5, 4, 3
+
+@dataclass
+class TransitionData:
+    human_T: np.ndarray   # (6,6) transition probs
+    agent_T: np.ndarray   # (6,6)
+    human_dwell: np.ndarray  # (6,2) shape,scale
+    agent_dwell: np.ndarray  # (6,2)
+
+    def to_jax(self):
+        if not JAX_AVAILABLE: return self
+        return TransitionData(*[jnp.array(x) for x in [self.human_T, self.agent_T, self.human_dwell, self.agent_dwell]])
+
+def dict_to_dense(d):
+    m = np.zeros((N_STATES, N_STATES), dtype=np.float32)
+    for src, dsts in d.items():
+        if (i := S2I.get(src)) is not None:
+            for dst, p in dsts.items():
+                if (j := S2I.get(dst)) is not None: m[i,j] = p
+    m /= np.maximum(m.sum(1, keepdims=True), 1e-8)
+    m[TERM_IDX] = 0; m[TERM_IDX, TERM_IDX] = 1.0
+    return m
+
+def compile_transitions(human_profile, agent_profile):
+    def dwell_arr(params): return np.array([[params.get(s, (2.0, 1.0)) for s in STATES]], dtype=np.float32).reshape(N_STATES, 2)
+    return TransitionData(dict_to_dense(human_profile.transitions), dict_to_dense(agent_profile.transitions),
+                          dwell_arr(human_profile.dwell_params), dwell_arr(agent_profile.dwell_params))
+
+def fallback_transitions():
+    H = {"session_start": {"view_item_page": .85, "session_end": .15}, "view_item_page": {"learn_more_about_item": .4, "add_item_to_cart": .3, "view_item_page": .2, "session_end": .1},
+         "learn_more_about_item": {"add_item_to_cart": .5, "view_item_page": .3, "session_end": .2}, "add_item_to_cart": {"purchase_complete": .6, "view_item_page": .25, "session_end": .15}, "purchase_complete": {"session_end": 1.0}}
+    A = {"session_start": {"view_item_page": .9, "session_end": .1}, "view_item_page": {"learn_more_about_item": .5, "add_item_to_cart": .25, "view_item_page": .15, "session_end": .1},
+         "learn_more_about_item": {"add_item_to_cart": .4, "view_item_page": .4, "session_end": .2}, "add_item_to_cart": {"purchase_complete": .5, "view_item_page": .3, "session_end": .2}, "purchase_complete": {"session_end": 1.0}}
+    dwell = np.full((N_STATES, 2), [2.0, 1.0], dtype=np.float32)
+    return TransitionData(dict_to_dense(H), dict_to_dense(A), dwell.copy(), dwell.copy())

sim/rl/train.py

@@ -3,15 +3,18 @@ import logging
 from pathlib import Path
 from typing import Dict, Type, Optional
 import pickle
-from torch import neg_
 from torch.utils.tensorboard import SummaryWriter
-from environment import PHANTOMEnv, FastTrainingConstraints, BusinessLogicConstraints
-from engine import (BasePricingEngine, WildPricingEngine, StaticPricingEngine,
-                   SimpleDemandEngine, RandomWalkEngine, ThompsonSamplingEngine)
+from sim.rl.environment import PHANTOMEnv, BusinessLogicConstraints
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
 logger = logging.getLogger(__name__)
 
+try:
+    from sim.rl.engine import (BasePricingEngine, WildPricingEngine, StaticPricingEngine,
+                       SimpleDemandEngine, RandomWalkEngine, ThompsonSamplingEngine)
+except ImportError as e:
+    BasePricingEngine = None  # engines not required for basic usage
+    print(e)
 
 
 """
@@ -26,8 +29,7 @@ CURRENT SOLUTION BELOW does not implement correct learning or updates.
 
 class EngineTrainer:
     """wrapper to run pricing engines through episodes and collect metrics"""
-    def __init__(self, engine: BasePricingEngine, env: PHANTOMEnv,
-                 tb_writer: Optional[SummaryWriter] = None):
+    def __init__(self, engine, env: PHANTOMEnv, tb_writer: Optional[SummaryWriter] = None):
         self.engine = engine
         self.env = env
         self.episode_metrics = []
@@ -35,21 +37,51 @@ class EngineTrainer:
         self.global_step = 0
 
     def train(self, n_episodes: int, seed: int = 42):
-
-        obs, _ = self.env.reset(seed=seed)
-        prices = None
         for ep in range(n_episodes):
-            prices = self.engine.compute_prices(prices, obs)
-            obs, reward, done, _, info = self.env.step(prices)
-            self.engine.update(obs, reward, done, info)
+            obs, _ = self.env.reset(seed=seed + ep)
+            self.engine.reset()
+            done = False
+            prev_prices = obs["elasticity"]["price"]
+            episode_reward = 0.0
+            last_info: Dict[str, float] = {}
+            while not done:
+                action_prices = self.engine.compute_prices(prev_prices, obs)
+                obs, reward, done, _, info = self.env.step(action_prices)
+                self.engine.update(obs, reward, done, info)
+                episode_reward += reward
+                prev_prices = obs["elasticity"]["price"]
+                last_info = info
+                if self.tb_writer:
+                    self.tb_writer.add_scalar("reward/step", reward, self.global_step)
+                    if "coi" in info:
+                        self.tb_writer.add_scalar("diagnostics/coi", info["coi"], self.global_step)
+                    if "alpha_hat" in info:
+                        self.tb_writer.add_scalar("diagnostics/alpha_hat", info["alpha_hat"], self.global_step)
+                self.global_step += 1
+            last_info = dict(last_info)
+            last_info.update({"episode_reward": episode_reward, "episode": ep})
+            self.episode_metrics.append(last_info)
+            if self.tb_writer:
+                self.tb_writer.add_scalar("reward/episode", episode_reward, ep)
         return self
 
-
-
-
-
-
-        return self.episode_metrics
+    def run_episode(self, seed: int = 42) -> Dict:
+        """run single evaluation episode and return metrics"""
+        obs, _ = self.env.reset(seed=seed)
+        self.engine.reset()
+        total_reward = 0.0
+        prev_prices = obs["elasticity"]["price"]
+        ep_metrics = {'total_reward': 0.0}
+        done = False
+        while not done:
+            action_prices = self.engine.compute_prices(prev_prices, obs)
+            obs, reward, done, _, info = self.env.step(action_prices)
+            total_reward += reward
+            for k, v in info.items():
+                ep_metrics[k] = v
+            prev_prices = obs["elasticity"]["price"]
+        ep_metrics['total_reward'] = total_reward
+        return ep_metrics
 
     def evaluate(self, n_episodes: int = 10, seed: int = 100) -> Dict:
         """evaluate trained engine"""
@@ -57,17 +89,16 @@ class EngineTrainer:
                                    'agent_loss', 'ux_volatility', 'look_to_book']}
         for ep in range(n_episodes):
             metrics = self.run_episode(seed=seed + ep)
-            for k in results:                results[k].append(metrics[k])
+            for k in results:
+                results[k].append(metrics.get(k, 0.0))
         return {k: (np.mean(v), np.std(v)) for k, v in results.items()}
 
 
-def make_env(fast: bool = True):
-    constraints = FastTrainingConstraints() if fast else BusinessLogicConstraints()
-    return PHANTOMEnv(constraints=constraints)
+def make_env():
+    return PHANTOMEnv(constraints=BusinessLogicConstraints())
 
 
-def train_engine(engine_cls: Type[BasePricingEngine], env: PHANTOMEnv,
-                n_episodes: int, seed: int = 42,
+def train_engine(engine_cls, env: PHANTOMEnv, n_episodes: int, seed: int = 42,
                 tb_writer: Optional[SummaryWriter] = None) -> EngineTrainer:
     constraints = env.constraints
     engine = engine_cls(constraints=constraints, seed=seed)
@@ -80,15 +111,11 @@ def save_trainer(trainer: EngineTrainer, path: Path):
     """save engine state and metrics"""
     path.parent.mkdir(parents=True, exist_ok=True)
     with open(path, 'wb') as f:
-        pickle.dump({
-            'engine': trainer.engine,
-            'metrics': trainer.episode_metrics
-        }, f)
+        pickle.dump({'engine': trainer.engine, 'metrics': trainer.episode_metrics}, f)
     logger.info(f"Saved trainer to {path}")
 
 
-def load_trainer(path: Path, env: PHANTOMEnv,
-                 tb_writer: Optional[SummaryWriter] = None) -> EngineTrainer:
+def load_trainer(path: Path, env: PHANTOMEnv, tb_writer: Optional[SummaryWriter] = None) -> EngineTrainer:
     """load saved engine"""
     with open(path, 'rb') as f:
         data = pickle.load(f)
@@ -98,45 +125,44 @@ def load_trainer(path: Path, env: PHANTOMEnv,
 
 
 if __name__ == "__main__":
-    base_dir = Path("./runs")
+    if BasePricingEngine is None:
+        logger.error("Engines not available, cannot run training")
+        exit(1)
+
+    base_dir = Path("./sim/rl/runs")
     base_dir.mkdir(exist_ok=True)
 
     engines = {
         "Wild": WildPricingEngine,
         "Static": StaticPricingEngine,
-#        "SimpleDemand": SimpleDemandEngine,
         "RandomWalk": RandomWalkEngine,
         "ThompsonSampling": ThompsonSamplingEngine,
     }
-    defenses = [False, True]
     n_train_episodes = 50
     n_eval_episodes = 10
     seed = 42
-    fast_mode = True
 
-    logger.info(f"Training config: {n_train_episodes} episodes per engine, fast_mode={fast_mode}")
+    logger.info(f"Training config: {n_train_episodes} episodes per engine")
 
     trained_trainers = {}
 
     for engine_name, engine_cls in engines.items():
-        for use_defense in defenses:
-            defense_label = "defense_on" if use_defense else "defense_off"
-            run_name = f"{engine_name}_{defense_label}"
-            log_dir = base_dir / run_name
-            log_dir.mkdir(parents=True, exist_ok=True)
+        run_name = engine_name
+        log_dir = base_dir / run_name
+        log_dir.mkdir(parents=True, exist_ok=True)
 
-            logger.info(f"Training {engine_name} with defense={use_defense}")
-            logger.info(f"Log directory: {log_dir}")
+        logger.info(f"Training {engine_name}")
+        logger.info(f"Log directory: {log_dir}")
 
-            env = make_env(fast=fast_mode)
-            tb_writer = SummaryWriter(log_dir=str(log_dir))
-            trainer = train_engine(engine_cls, env, n_train_episodes, seed, tb_writer=tb_writer)
-            tb_writer.close()
+        env = make_env()
+        tb_writer = SummaryWriter(log_dir=str(log_dir))
+        trainer = train_engine(engine_cls, env, n_train_episodes, seed, tb_writer=tb_writer)
+        tb_writer.close()
 
-            save_path = log_dir / "trainer.pkl"
-            save_trainer(trainer, save_path)
+        save_path = log_dir / "trainer.pkl"
+        save_trainer(trainer, save_path)
 
-            trained_trainers[run_name] = (trainer, env)
+        trained_trainers[run_name] = (trainer, env)
 
     logger.info("Starting evaluation")
 

sim/strong_learner/data.py

@@ -0,0 +1,108 @@
+import os
+import requests
+try:
+    import py7zr  # type: ignore
+except ImportError:  # pragma: no cover - optional dependency
+    py7zr = None
+import pandas as pd
+from typing import Generator
+try:
+    from sim.rl.behavior_loader.loader import PayloadModel, ValueModel, InteractionModel, Loader
+except ImportError:
+    from loader import PayloadModel, ValueModel, InteractionModel, Loader
+
+class YooChooseLoader(Loader):
+    URL = "https://s3-eu-west-1.amazonaws.com/yc-rdata/yoochoose-data.7z"
+    CLICK_COLS = ['session_id', 'ts', 'item_id', 'category']
+    BUY_COLS = ['session_id', 'ts', 'item_id', 'price', 'quantity']
+
+    def __init__(self, root_dir: str = "data/yoochoose", chunk_size: int = 500_000, max_sessions: int = 1000):
+        self.root = root_dir
+        self.chunk_size = chunk_size
+        self.max_sessions = max_sessions
+        self.click_path = f"{root_dir}/yoochoose-clicks.dat"
+        self.buy_path = f"{root_dir}/yoochoose-buys.dat"
+        if not os.path.exists(self.click_path): self._setup()
+        self.data = self._load_sessions(max_sessions)
+        self.entries = list(self.data.keys())
+
+    def _setup(self):
+        if py7zr is None:
+            raise RuntimeError("py7zr is required to unpack YooChoose dataset. Install py7zr first.")
+        os.makedirs(self.root, exist_ok=True)
+        zip_path = f"{self.root}/temp.7z"
+        with requests.get(self.URL, stream=True) as r:
+            with open(zip_path, 'wb') as f:
+                for chunk in r.iter_content(8192):
+                    f.write(chunk)
+        with py7zr.SevenZipFile(zip_path, 'r') as z:
+            z.extractall(self.root)
+        os.remove(zip_path)
+
+    def _make_interaction(self, sid: str, ts: str, item_id: str, event: str, page: str, meta: dict) -> InteractionModel:
+        payload = PayloadModel(
+            sessionId=sid, experimentId=None, eventName=event,
+            page=page, productId=item_id, metadata=meta,
+            storeMode="yoochoose", userAgent="dataset", ts=ts
+        )
+        return InteractionModel(
+            partitionID=0, offset=0, timestamp=0, compression="",
+            isTransactional=False, headers=[], key={},
+            value=ValueModel(payload=payload, encoding="json", isPayloadNull=False, schemaId=1, size=0)
+        )
+
+    def _parse_category(self, cat) -> str:
+        if pd.isna(cat) or cat == "0": return "unknown"
+        if cat == "S": return "special_offer"
+        try:
+            n = int(cat)
+            return f"category_{n}" if 1 <= n <= 12 else f"brand_{n}"
+        except: return str(cat)
+
+    def stream_clicks(self) -> Generator[InteractionModel, None, None]:
+        with pd.read_csv(self.click_path, names=self.CLICK_COLS, chunksize=self.chunk_size, header=None) as reader:
+            for chunk in reader:
+                for r in chunk.itertuples(index=False):
+                    yield self._make_interaction(
+                        str(r.session_id), r.ts, str(r.item_id),
+                        "view_item_page", self._parse_category(r.category), {}
+                    )
+
+    def stream_buys(self) -> Generator[InteractionModel, None, None]:
+        with pd.read_csv(self.buy_path, names=self.BUY_COLS, chunksize=self.chunk_size, header=None) as reader:
+            for chunk in reader:
+                for r in chunk.itertuples(index=False):
+                    yield self._make_interaction(
+                        str(r.session_id), r.ts, str(r.item_id),
+                        "purchase_complete", "/checkout", {"price": r.price, "quantity": r.quantity}
+                    )
+
+    def stream(self) -> Generator[InteractionModel, None, None]:
+        yield from self.stream_clicks()
+        yield from self.stream_buys()
+
+    def _load_sessions(self, max_sessions: int | None = None) -> dict:
+        sessions = {}
+        for interaction in self.stream():
+            sid = interaction.value.payload.sessionId
+            if sid not in sessions:
+                if max_sessions and len(sessions) >= max_sessions: continue
+                sessions[sid] = []
+            sessions[sid].append(interaction)
+        for sid in sessions: sessions[sid].sort(key=lambda x: x.value.payload.ts)
+        return sessions
+
+    def get_data(self) -> dict:
+        return self.data
+
+    def get_entries(self) -> tuple[list[str], int]:
+        return self.entries, len(self.entries)
+
+if __name__ == "__main__":
+    loader = YooChooseLoader(max_sessions=100)
+    views, purchases = 0, 0
+    for sid, evts in loader.get_data().items():
+        for e in evts:
+            if e.value.payload.eventName == "view_item_page": views += 1
+            elif e.value.payload.eventName == "purchase_complete": purchases += 1
+    print(f"Loaded {len(loader.entries)} sessions: {views} view_item_page, {purchases} purchase_complete")

tests/e2e/.env.example

@@ -0,0 +1,7 @@
+WEB_URL=http://localhost:3000
+BACKEND_URL=http://localhost:5000
+PRICING_PROVIDER_URL=http://localhost:5001
+AIRFLOW_URL=http://localhost:8085
+AIRFLOW_USER=admin
+AIRFLOW_PASS=admin
+HEADLESS=true

tests/e2e/helpers/airflow.ts

@@ -0,0 +1,61 @@
+const AIRFLOW_URL = process.env.AIRFLOW_URL || 'http://localhost:8085';
+const AUTH = 'Basic ' + Buffer.from(`${process.env.AIRFLOW_USER || 'admin'}:${process.env.AIRFLOW_PASS || 'admin'}`).toString('base64');
+
+const req = (path: string, opts: any = {}) => {
+  const headers = { Authorization: AUTH, ...opts.headers };
+  return fetch(`${AIRFLOW_URL}${path}`, { ...opts, headers });
+};
+
+export const triggerDag = async (dagId: string, conf = {}) => {
+  const r = await req(`/api/v1/dags/${dagId}/dagRuns`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ conf }),
+  });
+  if (!r.ok) throw new Error(`Trigger DAG failed: ${r.status}`);
+  return (await r.json()).dag_run_id;
+};
+
+export const getDagStatus = async (dagId: string, runId: string) => {
+  const r = await req(`/api/v1/dags/${dagId}/dagRuns/${runId}`);
+  if (!r.ok) throw new Error(`Get status failed: ${r.status}`);
+  return (await r.json()).state;
+};
+
+export const cancelDag = async (dagId: string, runId: string) => {
+  const r = await req(`/api/v1/dags/${dagId}/dagRuns/${runId}`, {
+    method: 'PATCH',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ state: 'failed' }),
+  });
+  if (!r.ok) console.warn(`Failed to cancel DAG ${runId}: ${r.status}`);
+};
+
+export const waitForDag = async (dagId: string, runId: string, maxMs = 30000, pollMs = 1000) => {
+  const t0 = Date.now();
+  while (Date.now() - t0 < maxMs) {
+    const state = await getDagStatus(dagId, runId);
+    if (state === 'success') return;
+    if (state === 'failed') throw new Error(`DAG ${runId} failed`);
+    await new Promise(r => setTimeout(r, pollMs));
+  }
+  await cancelDag(dagId, runId);
+  throw new Error(`DAG ${runId} timeout`);
+};
+
+export const runDag = async (dagId: string, conf = {}, maxMs = 60000) => {
+  const runId = await triggerDag(dagId, conf);
+  await waitForDag(dagId, runId, maxMs);
+};
+
+export const runSessionPricing = (mode = 'hotel') =>
+  runDag('session_pricing_pipeline', { store_mode: mode, session_limit: 10 }, 90000);
+
+export const runSurgePricing = (mode = 'hotel', highThresh = 10, lowThresh = 2) =>
+  runDag('surge_pricing_pipeline', {
+    store_mode: mode,
+    high_threshold: highThresh,
+    low_threshold: lowThresh,
+    surge_multiplier: 1.2,
+    discount_multiplier: 0.9
+  }, 90000);

web/src/app/cart/page.tsx

@@ -32,7 +32,8 @@ export default function CartPage() {
                     {itemCount > 0 && (
                         <button
                             onClick={clearCart}
-                            className="text-sm text-red-600 hover:underline"
+                            className="text-sm hover:underline"
+                            style={{ color: 'var(--accent-warning)' }}
                         >
                             Clear cart
                         </button>
@@ -42,7 +43,7 @@ export default function CartPage() {
                 {itemCount === 0 ? (
                     <div className="text-center py-12">
                         <p className="text-gray-500 mb-4">Your cart is empty</p>
-                        <a href="/" className="text-blue-600 hover:underline">Browse our selection</a>
+                        <a href="/" className="hover:underline" style={{ color: 'var(--text-accent)' }}>Browse our selection</a>
                     </div>
                 ) : (
                     <>
@@ -54,15 +55,11 @@ export default function CartPage() {
                                 >
                                     <div className="flex-1">
                                         <div className="flex items-center gap-2 mb-1">
-                                            <span className="px-2 py-0.5 text-xs font-medium rounded bg-blue-100 text-blue-800">
-                                                {item.type}
-                                            </span>
                                             <h3 className="font-semibold">{item.name}</h3>
                                         </div>
 
                                         {item.type === 'hotel' && (
                                             <div className="text-sm text-gray-600">
-                                                <p>{String(item.metadata.roomType)}</p>
                                                 <p>{String(item.metadata.checkIn)} - {String(item.metadata.checkOut)}</p>
                                                 <p>{String(item.metadata.nights)} night{Number(item.metadata.nights) > 1 ? 's' : ''}</p>
                                             </div>
@@ -81,7 +78,8 @@ export default function CartPage() {
                                         <p className="text-xl font-bold mb-2">${item.price}</p>
                                         <button
                                             onClick={() => handleRemove(item.id, item.type)}
-                                            className="text-sm text-red-600 hover:underline"
+                                            className="text-sm hover:underline"
+                                            style={{ color: 'var(--accent-warning)' }}
                                         >
                                             Remove
                                         </button>
@@ -100,7 +98,7 @@ export default function CartPage() {
                                     dispatchInteraction('checkout_start', undefined, { total, itemCount });
                                     window.location.href = '/checkout';
                                 }}
-                                className="w-full py-3 bg-blue-600 hover:bg-blue-700 text-white rounded-lg font-medium transition-colors"
+                                className="btn-primary w-full"
                             >
                                 Proceed to Checkout
                             </button>

web/src/app/globals.css

@@ -8,6 +8,9 @@
   --bg-secondary: #f5f5f5;
   --text-primary: #333333;
   --text-secondary: #666666;
+  --accent-primary: #007aff;
+  --accent-primary-hover: #0051d5;
+  --accent-primary-light: #e6f2ff;
   --spacing-sm: 8px;
   --spacing-md: 16px;
   --spacing-lg: 32px;

web/src/app/layout.tsx

@@ -15,8 +15,8 @@ const geistMono = Geist_Mono({
 });
 
 export const metadata: Metadata = {
-  title: "Create Next App",
-  description: "Generated by create next app",
+  title: "Travel Booking Platform",
+  description: "Book flights and hotels with dynamic pricing",
 };
 
 export default function RootLayout({

web/src/app/page.tsx

@@ -1,65 +1,5 @@
-import Image from "next/image";
+import { redirect } from 'next/navigation';
 
 export default function Home() {
-  return (
-    <div className="flex min-h-screen items-center justify-center bg-zinc-50 font-sans dark:bg-black">
-      <main className="flex min-h-screen w-full max-w-3xl flex-col items-center justify-between py-32 px-16 bg-white dark:bg-black sm:items-start">
-        <Image
-          className="dark:invert"
-          src="/next.svg"
-          alt="Next.js logo"
-          width={100}
-          height={20}
-          priority
-        />
-        <div className="flex flex-col items-center gap-6 text-center sm:items-start sm:text-left">
-          <h1 className="max-w-xs text-3xl font-semibold leading-10 tracking-tight text-black dark:text-zinc-50">
-            To get started, edit the page.tsx file.
-          </h1>
-          <p className="max-w-md text-lg leading-8 text-zinc-600 dark:text-zinc-400">
-            Looking for a starting point or more instructions? Head over to{" "}
-            <a
-              href="https://vercel.com/templates?framework=next.js&utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
-              className="font-medium text-zinc-950 dark:text-zinc-50"
-            >
-              Templates
-            </a>{" "}
-            or the{" "}
-            <a
-              href="https://nextjs.org/learn?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
-              className="font-medium text-zinc-950 dark:text-zinc-50"
-            >
-              Learning
-            </a>{" "}
-            center.
-          </p>
-        </div>
-        <div className="flex flex-col gap-4 text-base font-medium sm:flex-row">
-          <a
-            className="flex h-12 w-full items-center justify-center gap-2 rounded-full bg-foreground px-5 text-background transition-colors hover:bg-[#383838] dark:hover:bg-[#ccc] md:w-[158px]"
-            href="https://vercel.com/new?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
-            target="_blank"
-            rel="noopener noreferrer"
-          >
-            <Image
-              className="dark:invert"
-              src="/vercel.svg"
-              alt="Vercel logomark"
-              width={16}
-              height={16}
-            />
-            Deploy Now
-          </a>
-          <a
-            className="flex h-12 w-full items-center justify-center rounded-full border border-solid border-black/[.08] px-5 transition-colors hover:border-transparent hover:bg-black/[.04] dark:border-white/[.145] dark:hover:bg-[#1a1a1a] md:w-[158px]"
-            href="https://nextjs.org/docs?utm_source=create-next-app&utm_medium=appdir-template-tw&utm_campaign=create-next-app"
-            target="_blank"
-            rel="noopener noreferrer"
-          >
-            Documentation
-          </a>
-        </div>
-      </main>
-    </div>
-  );
+  redirect('/hotel');
 }

web/src/components/feats/hotel/HotelCard.tsx

@@ -2,6 +2,7 @@
 
 import type { EventName } from '@/lib/events';
 import type { Hotel } from '@/lib/hotel-utils';
+import { getHotelImageUrl } from '@/lib/hotel-utils';
 import { useHoverTracking } from '@/hooks/useHoverTracking';
 import PriceDisplay from '@/components/ui/PriceDisplay';
 
@@ -47,8 +48,6 @@ export default function HotelCard({ hotel }: { hotel: Hotel }) {
         window.location.href = `/hotel/products/${hotel.id}`;
     };
 
-    const imageUrl = `https://images.unsplash.com/photo-1551882547-ff40c63fe5fa?w=400&h=300&fit=crop`;
-
     return (
         <div
             className="hotel-card cursor-pointer"
@@ -56,7 +55,7 @@ export default function HotelCard({ hotel }: { hotel: Hotel }) {
         >
             <div className="hotel-image relative overflow-hidden">
                 <img
-                    src={imageUrl}
+                    src={getHotelImageUrl(hotel.id, { w: 400, h: 300 })}
                     alt={hotel.name}
                     className="w-full h-full object-cover"
                     onError={(e) => {

web/src/components/feats/hotel/HotelDetails.tsx

@@ -2,6 +2,7 @@
 
 import { useState, useEffect } from 'react';
 import type { Hotel } from '@/lib/hotel-utils';
+import { getHotelImageUrl } from '@/lib/hotel-utils';
 import PriceDisplay from '@/components/ui/PriceDisplay';
 
 interface HotelDetailsProps {
@@ -43,13 +44,11 @@ const PriceTotalDisplay = ({ productId, nights }: { productId: string; nights: n
 };
 
 export default function HotelDetails({ product, onAddToCart, addedToCart }: HotelDetailsProps) {
-  const imageUrl = `https://images.unsplash.com/photo-1566073771259-6a8506099945?w=800&h=600&fit=crop`;
-
   return (
     <div className="w-full flex flex-col lg:flex-row gap-12 py-8">
       <div className="w-full lg:w-1/2 rounded-lg aspect-[4/3] overflow-hidden shrink-0">
         <img
-          src={imageUrl}
+          src={getHotelImageUrl(product.id, { w: 800, h: 600 })}
           alt={product.name}
           className="w-full h-full object-cover"
           onError={(e) => {

web/src/components/ui/Navigation.tsx

@@ -20,7 +20,7 @@ const NavLink = ({ href, children }: { href: string; children: React.ReactNode }
       href={href}
       className={`px-4 py-2 rounded-md transition-colors ${
         isActive
-          ? 'bg-[var(--accent-primary)] font-semibold'
+          ? 'bg-[var(--accent-primary)] text-white font-semibold'
           : 'hover:bg-[var(--accent-primary-light)] text-[var(--text-primary)]'
       }`}
     >

web/src/lib/airline-utils.ts

@@ -31,7 +31,7 @@ export interface Flight {
   availability: number;
 }
 
-const EPOCH = new Date(0);
+import { dateToDaysFromToday, dateToIndex, todayIndex } from './date-utils';
 
 export const transformProduct = (p: AirlineProduct): Flight => {
   const { id, flight_type, date_index, metadata, availability } = p;
@@ -52,24 +52,4 @@ export const transformProduct = (p: AirlineProduct): Flight => {
   };
 };
 
-// convert date string to days from today
-export const dateToDaysFromToday = (dateStr: string): number => {
-  const target = new Date(dateStr);
-  target.setHours(0, 0, 0, 0);
-  const today = new Date();
-  today.setHours(0, 0, 0, 0);
-  return Math.floor((target.getTime() - today.getTime()) / 86400000);
-};
-
-// convert date string to date_index (days since epoch)
-export const dateToIndex = (dateStr: string): number => {
-  const d = new Date(dateStr);
-  return Math.floor((d.getTime() - EPOCH.getTime()) / 86400000);
-};
-
-// get current date_index
-export const todayIndex = (): number => {
-  const now = new Date();
-  now.setHours(0, 0, 0, 0);
-  return Math.floor((now.getTime() - EPOCH.getTime()) / 86400000);
-};
+export { dateToDaysFromToday, dateToIndex, todayIndex };

web/src/lib/date-utils.ts

@@ -0,0 +1,23 @@
+const EPOCH = new Date(0);
+const MS_PER_DAY = 86400000;
+
+export const dateToDaysFromToday = (dateStr: string): number => {
+  const target = new Date(dateStr);
+  target.setHours(0, 0, 0, 0);
+  const today = new Date();
+  today.setHours(0, 0, 0, 0);
+  return Math.floor((target.getTime() - today.getTime()) / MS_PER_DAY);
+};
+
+export const dateToIndex = (dateStr: string): number => {
+  const d = new Date(dateStr);
+  return Math.floor((d.getTime() - EPOCH.getTime()) / MS_PER_DAY);
+};
+
+export const todayIndex = (): number => {
+  const now = new Date();
+  now.setHours(0, 0, 0, 0);
+  return Math.floor((now.getTime() - EPOCH.getTime()) / MS_PER_DAY);
+};
+
+export { EPOCH, MS_PER_DAY };

web/src/lib/hotel-utils.ts

@@ -25,7 +25,7 @@ export interface Hotel {
   nights: number;
 }
 
-const EPOCH = new Date(0);
+import { EPOCH, MS_PER_DAY, dateToDaysFromToday, dateToIndex, todayIndex } from './date-utils';
 
 export const transformProduct = (p: HotelProduct): Hotel => {
   const { id, room_type, date_index, metadata } = p;
@@ -37,14 +37,14 @@ export const transformProduct = (p: HotelProduct): Hotel => {
     // legacy: treat as offset from today
     const today = new Date();
     today.setHours(0, 0, 0, 0);
-    checkIn = new Date(today.getTime() + date_index * 86400000);
+    checkIn = new Date(today.getTime() + date_index * MS_PER_DAY);
   } else {
     // proper: days since epoch
-    checkIn = new Date(EPOCH.getTime() + date_index * 86400000);
+    checkIn = new Date(EPOCH.getTime() + date_index * MS_PER_DAY);
   }
 
   const nights = 1;
-  const checkOut = new Date(checkIn.getTime() + nights * 86400000);
+  const checkOut = new Date(checkIn.getTime() + nights * MS_PER_DAY);
 
   const formatOpts: Intl.DateTimeFormatOptions = {
     month: 'short',
@@ -65,24 +65,34 @@ export const transformProduct = (p: HotelProduct): Hotel => {
   };
 };
 
-// convert date string to days from today
-export const dateToDaysFromToday = (dateStr: string): number => {
-  const target = new Date(dateStr);
-  target.setHours(0, 0, 0, 0);
-  const today = new Date();
-  today.setHours(0, 0, 0, 0);
-  return Math.floor((target.getTime() - today.getTime()) / 86400000);
+const hotelImagePool = [
+  'photo-1566073771259-6a8506099945',
+  'photo-1551882547-ff40c63fe5fa',
+  'photo-1590490360182-c33d57733427',
+  'photo-1582719478250-c89cae4dc85b',
+  'photo-1596701062351-8c2c14d1fdd0',
+  'photo-1631049307264-da0ec9d70304',
+  'photo-1578683010236-d716f9a3f461',
+  'photo-1540518614846-7eded433c457',
+  'photo-1505693416388-ac5ce068fe85',
+  'photo-1522771739844-6a9f6d5f14af',
+  'photo-1562438668-bcf0ca6578f0',
+  'photo-1595576508898-0ad5c879a061',
+];
+
+const hashString = (s: string): number => {
+  let h = 0;
+  for (let i = 0; i < s.length; i++) {
+    h = ((h << 5) - h) + s.charCodeAt(i);
+    h = h & h;
+  }
+  return Math.abs(h);
 };
 
-// convert date string to date_index (days since epoch)
-export const dateToIndex = (dateStr: string): number => {
-  const d = new Date(dateStr);
-  return Math.floor((d.getTime() - EPOCH.getTime()) / 86400000);
+export const getHotelImageUrl = (hotelId: string, size: { w: number; h: number } = { w: 400, h: 300 }): string => {
+  const idx = hashString(hotelId) % hotelImagePool.length;
+  const photoId = hotelImagePool[idx];
+  return `https://images.unsplash.com/${photoId}?w=${size.w}&h=${size.h}&fit=crop`;
 };
 
-// get current date_index
-export const todayIndex = (): number => {
-  const now = new Date();
-  now.setHours(0, 0, 0, 0);
-  return Math.floor((now.getTime() - EPOCH.getTime()) / 86400000);
-};
+export { dateToDaysFromToday, dateToIndex, todayIndex };