Autonomous Agent Swarm Neurobiological Simulation

The FreshCredit Agent Society Simulator is a computational laboratory for testing financial coordination protocols and digital banking systems before expensive real-world deployment. It simulates economies of autonomous financial agents with neurobiologically-inspired cognition, enabling validation of lending mechanisms, credit scoring models, risk assessments, and DeFi coordination strategies. Each agent represents an economic actor—borrowers, lenders, liquidity providers, validators—with complete cognitive architectures. Agents implement DOCAS neuromodulation (Dopamine-Oxytocin-Cortisol-Adrenaline-Serotonin) for affective decision-making under uncertainty, Prospect Theory for risk assessment, hyperbolic discounting for temporal preferences, and Rescorla-Wagner learning for trust and reputation formation. This creates realistic behavioral responses to market conditions: panic selling during crashes, herding behaviors in bubbles, trust erosion after defaults, and patient capital allocation during stability. The simulation environment models complete economic systems: resource production and consumption, bilateral exchange with bargaining, wealth accumulation with inequality dynamics, credit markets with default risk, and network effects from trust relationships. Agents form coalitions, establish trade routes, develop reputations, and respond to crises—all without scripted behaviors emerging from cognitive architectures. Technical implementation enables 1000+ agents at 10+ FPS through Barnes-Hut O(n log n) social forces and spatial hashing O(n) collision detection. Stigmergic communication via pheromone fields enables indirect coordination mimicking market signals and information cascades. Deterministic RNG ensures reproducible experiments with SHA-256 output verification. Scientific validation employs 41 Pattern-Oriented Modeling targets including power-law wealth distributions, trust network topologies, crisis propagation dynamics, and recovery patterns.