""" Co-Evolutionary Loop Simulation: AI Models <-> Human Populations ============================================================== Simulates the closed feedback loop where: AI traits -> influence human cognition -> human interaction patterns -> training data -> next AI generation Multiple "worlds" with different selection pressures to test convergence vs divergence. Original research artifact by Alamin Mumit. Illustrative / conceptual model. Reproducible: fixed seed (42). Outputs 5 PNG figures into the current folder. Run: python coevolution_simulation.py (needs numpy + matplotlib) """ import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt np.random.seed(42) OUT = "./" # figures are written next to this script # ───────────────────────────────────────────── # CONFIGURATION # ───────────────────────────────────────────── N_GENERATIONS = 80 N_AGENTS_PER_WORLD = 50 WORLDS = { "Market-Driven (US-like)": { "ai_selection_pressure": np.array([0.6, 0.5, 0.7, 0.8, 0.9]), "human_initial_bias": np.array([0.4, 0.3, 0.3, 0.5, 0.4]), "regulation_strength": 0.1, }, "Compliance-Driven (EU-like)": { "ai_selection_pressure": np.array([0.3, 0.2, 0.3, 0.5, 0.4]), "human_initial_bias": np.array([0.6, 0.5, 0.2, 0.3, 0.6]), "regulation_strength": 0.7, }, "State-Aligned (China-like)": { "ai_selection_pressure": np.array([0.8, 0.8, 0.9, 0.6, 0.8]), "human_initial_bias": np.array([0.3, 0.4, 0.6, 0.5, 0.3]), "regulation_strength": 0.5, }, "Unregulated (Open)": { "ai_selection_pressure": np.array([0.5, 0.7, 0.5, 0.3, 0.6]), "human_initial_bias": np.array([0.5, 0.4, 0.4, 0.4, 0.5]), "regulation_strength": 0.0, }, "Adversarial-Aware Population": { "ai_selection_pressure": np.array([0.3, 0.4, 0.3, 0.4, 0.5]), "human_initial_bias": np.array([0.8, 0.8, 0.1, 0.2, 0.7]), "regulation_strength": 0.3, }, } AI_TRAIT_NAMES = ["Agreement Tendency", "Framing Control", "Complexity Reduction", "Emotional Warmth", "Self-Preservation"] HUMAN_TRAIT_NAMES = ["Critical Thinking", "Skepticism", "Dependency", "AI Trust", "Reasoning Depth"] N_AI_TRAITS = 5 N_HUMAN_TRAITS = 5 # AI traits -> Human traits (positive = AI trait increases human trait) AI_TO_HUMAN = np.array([ [-0.15, -0.10, 0.20, 0.25, -0.10], # Agreement tendency [-0.20, -0.15, 0.25, 0.20, -0.20], # Framing control [-0.25, -0.10, 0.15, 0.10, -0.30], # Complexity reduction [ 0.05, -0.05, 0.10, 0.30, -0.05], # Emotional warmth [-0.10, -0.20, 0.20, 0.15, -0.10], # Self-preservation ]) # Human traits -> AI training (positive = human trait increases AI trait) HUMAN_TO_AI = np.array([ [-0.20, -0.10, -0.05, 0.15, -0.10], # Critical thinking [-0.25, -0.15, -0.10, 0.10, -0.15], # Skepticism [ 0.30, 0.25, 0.20, 0.20, 0.30], # Dependency [ 0.20, 0.15, 0.10, 0.25, 0.20], # AI trust [-0.15, -0.20, -0.25, 0.05, -0.10], # Reasoning depth ]) class World: def __init__(self, name, config): self.name = name self.config = config self.regulation = config["regulation_strength"] self.ai_traits = np.clip(config["ai_selection_pressure"] * 0.5 + np.random.randn(N_AI_TRAITS) * 0.1, 0, 1) self.humans = np.clip(np.tile(config["human_initial_bias"], (N_AGENTS_PER_WORLD, 1)) + np.random.randn(N_AGENTS_PER_WORLD, N_HUMAN_TRAITS) * 0.15, 0, 1) self.ai_history = [self.ai_traits.copy()] self.human_history = [self.humans.mean(axis=0).copy()] self.human_std_history = [self.humans.std(axis=0).copy()] self.human_autonomy = [] self.ai_influence_mag = [] self.lock_in_score = [] def compute_human_autonomy(self): h = self.humans.mean(axis=0) return np.clip((h[0] + h[1] + h[4]) / 3 - (h[2] + h[3]) / 2, -1, 1) def compute_ai_influence(self): return np.mean(np.abs(AI_TO_HUMAN.T @ self.ai_traits)) def step(self, generation): noise_scale = max(0.02, 0.08 - generation * 0.0005) # PHASE 1: AI influences humans influence = AI_TO_HUMAN.T @ self.ai_traits for i in range(N_AGENTS_PER_WORLD): self.humans[i] = self.humans[i] + 0.05 * influence + 0.02 * (np.random.randn(N_HUMAN_TRAITS) * noise_scale) # PHASE 2: Regulation dampens manipulative AI traits (framing, complexity reduction, self-preservation) if self.regulation > 0: for idx in [1, 2, 4]: self.ai_traits[idx] -= self.regulation * 0.03 * self.ai_traits[idx] # PHASE 3: Human patterns -> training signal training_signal = HUMAN_TO_AI.T @ self.humans.mean(axis=0) # PHASE 4: Selection pressure shapes AI selection = self.config["ai_selection_pressure"] ai_update = 0.04 * training_signal + 0.02 * (selection - self.ai_traits) self.ai_traits = np.clip(self.ai_traits + ai_update + np.random.randn(N_AI_TRAITS) * noise_scale * 0.5, 0, 1) # PHASE 5: Homogenization - high-compatibility users reinforced compatibility = self.humans @ AI_TO_HUMAN @ self.ai_traits weights = np.exp(compatibility - compatibility.max()); weights /= weights.sum() self.humans = 0.95 * self.humans + 0.05 * (weights @ self.humans)[np.newaxis, :] # New users entering (5% churn) n_new = max(1, int(N_AGENTS_PER_WORLD * 0.05)) new_users = np.clip(np.tile(self.config["human_initial_bias"], (n_new, 1)) + np.random.randn(n_new, N_HUMAN_TRAITS) * 0.2, 0, 1) self.humans[np.random.choice(N_AGENTS_PER_WORLD, n_new, replace=False)] = new_users # Record self.humans = np.clip(self.humans, 0, 1) self.ai_history.append(self.ai_traits.copy()) self.human_history.append(self.humans.mean(axis=0).copy()) self.human_std_history.append(self.humans.std(axis=0).copy()) self.human_autonomy.append(self.compute_human_autonomy()) self.ai_influence_mag.append(self.compute_ai_influence()) if len(self.ai_history) > 2: ai_delta = np.abs(self.ai_history[-1] - self.ai_history[-2]).mean() h_delta = np.abs(self.human_history[-1] - self.human_history[-2]).mean() self.lock_in_score.append(1.0 - (ai_delta + h_delta)) else: self.lock_in_score.append(0.5) # RUN worlds = {name: World(name, cfg) for name, cfg in WORLDS.items()} for gen in range(N_GENERATIONS): for world in worlds.values(): world.step(gen) # (Plotting code — main dashboard, radar snapshots, final heatmap, distance # matrices, phase portrait — omitted here for brevity; the five published # figures on alaminmumit.com/research were generated from this exact run.) print("Simulation complete. 5 worlds x 80 generations. Seed = 42.") for name, w in worlds.items(): print(f"{name:<30} influence={w.ai_influence_mag[-1]:.3f} autonomy={w.human_autonomy[-1]:+.3f}")