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* initial environemnt definitions * high level defintion * formlating the reward simply * improved implementation * tailored docker compose image for secondary tenaordboard * preliminary desriptions and babble * details on formulation and defintion of agent and its loop * typos one * more grammar issues * fluidity improvements and refactors * more decluttering and dnoising * finalizing introduction review * some methodology * somehow this disappeared * bit more of this and that * methodology of how we do architectuer and online DP * fix: compilation * expanding on the taxonomy and economic references * authoer notes * acks + google GCP * making space w new format nada lit review * stronger lit review and more sources * forgot about tables and graphs * dedupe citations * adding cloudflare * fixing env vars * updating docs with url * upating embed * fixing the url * paper badge * formaliztaion of rewards and adding definitions * noisy formulations * connecting some more dots here * adding significant weight in prices * fixing error * fixing typos and consistency * extra math formulations and refferenceot DRO * fixing diagram of loops * github mindmap * fixing erro and thiknig about big picture * enhancing the website * goals methodology and gitignore * some more references and theory links * talking about some wtp * feature: added wordcounter * forcing latex builds and fixining the bib # * refactor: update Cost of Information equations and notation for clarity * some more math and refactors * refactor: unify notation and improve clarity in COI equations * refactor: generalize master function for demand estimation and pricing strategies * we dont like math but we have to do it :( * refactor: enhance Cost of Information framework with additional context and illustration * refactor: enhance literature review and methodology sections with economic theory insights and system architecture details * alining format to fit the rubric * refactoring bibliography * fix: align * mdp additionally * trying different title * adding balance figure * agentic givergence, finally * fix: figure fonts adjusted to match
3.9 KiB
3.9 KiB
PHANTOM
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