naoice COI implementation

engine/engine.py

@@ -3,20 +3,23 @@ import numpy as np
 from .lib.demand import generate_demand_for_actor, estimate_demand
 from .lib.behavior import sample_behavior
 from logging import INFO, getLogger
+
 logger = getLogger(__name__)
 logger.setLevel(INFO)
 
 
-class MarketEngine():
+class MarketEngine:
     """implements separate demand distributions for humans and agents per Section 3.1.1"""
 
-    def __init__(self,
+    def __init__(
+        self,
         alpha: float,
         N: int,
         human_params: tuple,
         agent_params: tuple,
-                 demand_distribution = np.random.normal,
+        demand_distribution=np.random.normal,
-                 noise_std: float = 1.0):
+        noise_std: float = 1.0,
+    ):
         # no defaults for D_H, D_A - force explicit experiment design
         self.alpha = alpha
         self.Nagents = int(N * alpha)
@@ -28,18 +31,32 @@ class MarketEngine():
 
     def act(self, prices):
         # generate separate demands d() per actor type
-        demand_h = generate_demand_for_actor(prices, self.human_params, self.noise_std, distribution_method = self.demand_dist)
+        demand_h = generate_demand_for_actor(
-        demand_a = generate_demand_for_actor(prices, self.agent_params, self.noise_std, distribution_method = self.demand_dist)
+            prices,
+            self.human_params,
+            self.noise_std,
+            distribution_method=self.demand_dist,
+        )
+        demand_a = generate_demand_for_actor(
+            prices,
+            self.agent_params,
+            self.noise_std,
+            distribution_method=self.demand_dist,
+        )
         # sample behavior trajectories from each demand distribution
         human_t = [sample_behavior(demand_h, human=True) for _ in range(self.Nhumans)]
         agent_t = [sample_behavior(demand_a, human=False) for _ in range(self.Nagents)]
-        return estimate_demand(human_t + agent_t)
+        # store trajectories for agent probability calculation
+        self.last_trajectories = human_t + agent_t
+        return estimate_demand(self.last_trajectories)
 
     def measure(self):
         pass
 
-class PricingEngine():
+
-    def __init__(self,
+class PricingEngine:
+    def __init__(
+        self,
     ) -> None:
         pass
 
@@ -47,12 +64,8 @@ class PricingEngine():
         return np.random.uniform(low=25, high=100, size=10)
 
 
-
+class Limbo:
-class Limbo():
+    def __init__(self, platform, market) -> None:
-    def __init__(self,
-                 platform,
-                 market
-                 ) -> None:
         self.platform_turn = True
         self.platform = platform
         self.market = market
@@ -67,9 +80,12 @@ class Limbo():
         print(self.output)
         self.platform_turn = not self.platform_turn
 
+
 if __name__ == "__main__":
     platform = PricingEngine()
-    market = MarketEngine(alpha=0.3, N=100, human_params=(50, 10), agent_params=(45, 15))
+    market = MarketEngine(
+        alpha=0.3, N=100, human_params=(50, 10), agent_params=(45, 15)
+    )
     limbo = Limbo(platform, market)
     for _ in range(10):
         limbo.step()

engine/lib/__init__.py

@@ -1,6 +1,7 @@
 from .demand import estimate_demand, generate_demand_for_actor
-from .behavior import sample_behavior
+from .behavior import sample_behavior, get_transition_models, trajectory_to_events
 from .render import DashboardRenderer, style_axis
 from .wrappers import EconomicMetricsWrapper
 from .callbacks import MetricsCallback, EvalMetricsCallback
 from .providers import ProviderBenchmark, ProviderResult, BenchmarkConfig
+from .coi import compute_coi_leakage, compute_erosion_metrics, compute_agent_probability

engine/lib/behavior.py

@@ -1,3 +1,8 @@
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parents[2]))
+
 from sim.rl.behavior_loader.models import (
     BehaviorModel,
     AgentBehaviorModel,
@@ -7,11 +12,9 @@ import pandas as pd
 import numpy as np
 from .demand import generate_demand_for_actor
 
-base_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments"
+base_dir = Path(__file__).parents[2] / "experiments"
-human_dir, agent_dir = (
+human_dir = str(base_dir / "collected_data")
-    f"{base_dir}/collected_data/",
+agent_dir = str(base_dir / "agents" / "collected_data")
-    f"{base_dir}/agents/collected_data/",
-)
 
 _cache = {}  # lazy cache for models and base pivots
 
@@ -25,6 +28,46 @@ def _get_base_pivot(human: bool):
     return _cache[key]
 
 
+def get_transition_models():
+    """load human and agent transition models for agent probability calculation
+
+    returns:
+        tuple: (human_transitions, agent_transitions) as dicts of event->event->prob
+    """
+    human_model = BehaviorModel(human_dir)
+    agent_model = AgentBehaviorModel(agent_dir)
+
+    human_mdp = human_model.build_MDP()
+    agent_mdp = agent_model.build_MDP()
+
+    human_trans = aggregate_event_transitions(human_mdp)
+    agent_trans = aggregate_event_transitions(agent_mdp)
+
+    return human_trans, agent_trans
+
+
+def trajectory_to_events(trajectory: list) -> list:
+    """extract event names from trajectory for KL divergence calculation
+
+    trajectories are in format 'eventName_product0', extract just eventName
+
+    args:
+        trajectory: list like ['view_product0', 'add_to_cart_product1', 'checkout_product1']
+
+    returns:
+        list: event names like ['view', 'add_to_cart', 'checkout']
+    """
+    events = []
+    for state in trajectory:
+        # state format from sample_behavior: 'eventName_productX'
+        if "_product" in state:
+            event = state.rsplit("_product", 1)[0]
+        else:
+            event = state
+        events.append(event)
+    return events
+
+
 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)

engine/wrapper.py

@@ -3,15 +3,26 @@ from gymnasium import spaces
 import numpy as np
 from .engine import Limbo, MarketEngine, PricingEngine
 from .lib.render import DashboardRenderer
-from .lib.coi import compute_coi_proxy
+from .lib.coi import (
+    compute_coi_leakage,
+    compute_erosion_metrics,
+    compute_agent_probability,
+)
+from .lib.behavior import get_transition_models, trajectory_to_events
 from .lib.wrappers import EconomicMetricsWrapper
 
 
 class PHANTOM(gym.Env):
-    """Gymnasium wrapper for the Limbo pricing-market simulation. Platform sets prices, market responds with demand."""
+    """Gymnasium wrapper for Limbo pricing-market simulation implementing thesis COI framework
+
+    reward = R(p,d) - λ·COI_leak(p,τ') per thesis Section on DR-RL
+    COI_leak uses behavioral divergence to estimate agent probability f(τ')
+    """
+
     metadata = {"render_modes": ["human", "ansi"]}
 
-    def __init__(self,
+    def __init__(
+        self,
         n_products: int = 10,
         alpha: float = 0.3,
         N: int = 100,
@@ -21,12 +32,13 @@ class PHANTOM(gym.Env):
         price_bounds: tuple = (10.0, 150.0),
         lambda_coi: float = 0.1,
         coi_window: int = 10,
-                 render_mode: str = None):
+        render_mode: str = None,
+    ):
         super().__init__()
         self.n_products = n_products
         self.price_bounds = price_bounds
         self.lambda_coi = lambda_coi
-        self.coi_window = coi_window  # K steps for rolling COI calculation
+        self.coi_window = coi_window
         self.render_mode = render_mode
         self.alpha = alpha
         self.N = N
@@ -34,20 +46,34 @@ class PHANTOM(gym.Env):
         self.agent_params = agent_params
 
         self.market = MarketEngine(
-            alpha=alpha, N=N,
+            alpha=alpha,
-            human_params=human_params, agent_params=agent_params, noise_std=noise_std
+            N=N,
+            human_params=human_params,
+            agent_params=agent_params,
+            noise_std=noise_std,
         )
         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],
+            low=price_bounds[0],
-            shape=(n_products,), dtype=np.float32
+            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.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
@@ -56,25 +82,61 @@ class PHANTOM(gym.Env):
         self._price_history = []
         self._revenue_history = []
         self._renderer = None
-        self._initial_episode_prices = None  # prices at episode start for COI calc
+        self._initial_episode_prices = None
+        self._trajectories = []  # session trajectories for agent prob calculation
+
+        # load behavioral models for agent probability estimation
+        try:
+            self._human_trans, self._agent_trans = get_transition_models()
+        except Exception:
+            # fallback if behavioral data unavailable
+            self._human_trans, self._agent_trans = None, 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)
+        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_coi_proxy(self):
+    def _compute_agent_prob(self) -> float:
-        return compute_coi_proxy(
+        """estimate agent probability from accumulated trajectories using KL divergence"""
-            self._price_history, self._demand_history, self._initial_episode_prices,
+        if (
-            self._prices, self.price_bounds, self.alpha, self.coi_window
+            not self._trajectories
+            or self._human_trans is None
+            or self._agent_trans is None
+        ):
+            return self.alpha  # fallback to contamination level
+
+        # aggregate all trajectories from this episode
+        all_events = []
+        for traj in self._trajectories:
+            all_events.extend(trajectory_to_events(traj))
+
+        if len(all_events) < 2:
+            return self.alpha
+
+        return compute_agent_probability(
+            all_events, self._human_trans, self._agent_trans
         )
 
     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)]))
+        revenue = np.sum(
-        coi_penalty = self.lambda_coi * self._compute_coi_proxy()
+            prices * np.array([demand.get(i, 0.0) for i in range(self.n_products)])
+        )
+
+        # compute agent probability from behavioral trajectories
+        agent_prob = self._compute_agent_prob()
+
+        # COI leakage: minimal implementation per thesis
+        coi_leakage = compute_coi_leakage(prices, agent_prob)
+        coi_penalty = self.lambda_coi * coi_leakage
+
         return float(revenue - coi_penalty)
 
     def _record_history(self):
-        demand_arr = np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])
+        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))
@@ -82,10 +144,11 @@ class PHANTOM(gym.Env):
     def reset(self, seed=None, options=None):
         super().reset(seed=seed)
         self._prices = np.random.uniform(*self.price_bounds, size=self.n_products)
-        self._initial_episode_prices = self._prices.copy()  # snapshot for COI calculation
+        self._initial_episode_prices = self._prices.copy()
         self._demand = self.market.act(self._prices)
         self._step_count = 0
         self._demand_history, self._price_history, self._revenue_history = [], [], []
+        self._trajectories = []
         self._record_history()
         return self._get_obs(), {}
 
@@ -95,15 +158,36 @@ class PHANTOM(gym.Env):
         self._step_count += 1
         self._record_history()
 
-        coi_proxy = self._compute_coi_proxy()
+        # capture trajectories generated by market for agent prob estimation
+        if hasattr(self.market, "last_trajectories"):
+            self._trajectories.extend(self.market.last_trajectories)
+
+        agent_prob = self._compute_agent_prob()
+        coi_leakage = compute_coi_leakage(self._prices, agent_prob)
         reward = self._compute_reward(self._prices, self._demand)
         terminated = self._step_count >= 100
 
+        # legacy erosion metrics for comparison
+        erosion = compute_erosion_metrics(
+            self._price_history,
+            self._demand_history,
+            self._initial_episode_prices,
+            self._prices,
+            self.price_bounds,
+            self.alpha,
+            self.coi_window,
+        )
+
         info = {
             "step": self._step_count,
-            "coi_proxy": coi_proxy,
+            "agent_prob": agent_prob,
-            "coi_penalty": self.lambda_coi * coi_proxy,
+            "coi_leakage": coi_leakage,
-            "raw_revenue": np.sum(self._prices * np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])),
+            "coi_penalty": self.lambda_coi * coi_leakage,
+            "erosion_metrics": erosion,
+            "raw_revenue": np.sum(
+                self._prices
+                * np.array([self._demand.get(i, 0.0) for i in range(self.n_products)])
+            ),
         }
         return self._get_obs(), reward, terminated, False, info
 
@@ -114,10 +198,16 @@ class PHANTOM(gym.Env):
         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'):
+        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.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)
+        return (
+            np.mean(elasticity, axis=0)
+            if len(elasticity) > 0
+            else np.zeros(self.n_products)
+        )
 
     def render(self):
         if self.render_mode == "human":
@@ -125,7 +215,9 @@ class PHANTOM(gym.Env):
                 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 (
+                f"step={self._step_count}, prices={self._prices}, demand={self._demand}"
+            )
         return None
 
     def close(self):
@@ -140,6 +232,7 @@ if __name__ == "__main__":
 
     class RandomPolicy:
         """Minimal SB3-compatible random policy for baseline testing."""
+
         def __init__(self, env):
             self.env = env
             self.num_timesteps = 0

paper/src/main.tex

@@ -27,7 +27,7 @@ These behavioral signals serve as inputs for a Distributionally Robust Reinforce
 \noindent\textbf{Keywords:} Dynamic Pricing, LLM Agents, Adversarial Machine Learning, E-commerce, Behavioral Detection, Reinforcement Learning
 
 \vspace{1em}
-\noindent\textbf{Acknowledgments:} Eugene Bykovets, PhD - ETH for helping with problem formulation. This research was supported by the TPU Research Cloud program.
+\noindent\textbf{Acknowledgments:} This research was supported by the TPU Research Cloud program.
 
 \clearpage
 \input{chapters/01-intro}