Strategic Algorithmic Monoculture: Experimental Evidence from Coordination Games

1. 1
   Distinguish Monoculture Types

   Understand the difference between Primary Algorithmic Monoculture (inherent, baseline action similarity) and Strategic Algorithmic Monoculture (active, adaptive behavioral alignment driven by incentives to optimize outcomes).
2. 2
   Design Agent Incentives

   Carefully craft reward functions within your multi-agent system. These incentives are the primary drivers that will encourage or discourage behavioral similarity among agents, shaping the emergence of strategic monoculture.
3. 3
   Set Up Coordination Experiments

   Create simulated multi-agent environments or 'coordination games' where agents must achieve a shared goal. Vary the incentive structures in these environments to observe how strategic monoculture emerges and impacts collective outcomes.
4. 4
   Develop Adaptive Agents

   Implement AI agents with mechanisms for learning and adaptation. These agents should be capable of observing peer behavior, understanding the incentive landscape, and strategically adjusting their actions to optimize individual or collective rewards.

import random

# Define a coordination game's reward structure (incentives)
# This matrix dictates how agents are rewarded based on their joint actions.
# Example: A 2-player, 2-action game (e.g., choosing 'A' or 'B')
# Rewards are (Agent1_Reward, Agent2_Reward)

COORDINATION_GAME_REWARDS = {
    ('A', 'A'): (10, 10), # High reward for coordinating on 'A'
    ('A', 'B'): (0, 5),   # Agent 1 chose A, Agent 2 chose B
    ('B', 'A'): (5, 0),   # Agent 1 chose B, Agent 2 chose A
    ('B', 'B'): (7, 7)    # Moderate reward for coordinating on 'B'
}

class StrategicAgent:
    def __init__(self, agent_id):
        self.id = agent_id

    def decide_action(self, observed_peer_action=None):
        # This method is where 'Strategic Algorithmic Monoculture' emerges.
        # A real agent would use a learned policy (e.g., RL) or game-theoretic
        # reasoning to adapt its action based on incentives and peer behavior.
        if observed_peer_action:
            # Simple heuristic: high chance to align if coordination is generally good
            if random.random() < 0.8:
                return observed_peer_action
        return random.choice(['A', 'B']) # Default or exploratory action