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🧠 Cognitive Division of Labor (Latent vs. Introspective Agents)

This simulation demonstrates the architectural implications of the Asimov / Anthropic Parodox in Multi-Agent Systems.

It posits that an efficient agentic society cannot consist solely of "omniscient, hyper-reflective" agents, nor solely of "blind, reactive" agents. Total omniscience causes cognitive paralysis (The Last Answer), while total reactivity causes chaotic inefficiency.

The Simulation

The simulation runs three isolated resource-gathering societies in parallel:

  1. Pure Latent (Intuition Only): 10 agents that move every single tick. They have \(R \approx 0\) (no reflectivity). They only see 1 step ahead. They are fast but often wander aimlessly or loop around local minima.

  2. Pure Introspective (Reflexion Only): 10 agents that calculate the absolute perfect path (A* distance mapping) to the nearest resource. They have \(R \approx 1\) (high reflectivity). However, computing this "god-mode" vision takes time. They only move every 5 ticks.

  3. Symbiotic (Action + Reflexion): A mix of 7 Latent and 3 Introspective agents.

  4. The Introspective agents act as the slow-moving "consciousness" of the system. While computing their perfect paths, they leave strong gradients ("meaning"/pheromones) in the environment.
  5. The Latent agents act as the fast-moving "body". They still only see 1 step ahead, but their instincts drive them up the gradients left by the Introspectives.

Result: The Symbiotic society typically outperforms the others in this toy environment, suggesting that a β€œdivision of cognitive labor” (slow reflection shaping fast action through environmental memory) can outperform homogeneous swarms under some conditions.

Running the Simulation

pip install numpy matplotlib
python latent_introspective_society.py