Decomposing Financial Market Dynamics via Mechanism Analysis in an Evolutionary Multi-Agent Simulation
2026-06-22 • Artificial Intelligence
Artificial IntelligenceMultiagent SystemsNeural and Evolutionary Computing
AI summaryⓘ
The authors studied a simulated market with 120 different types of fake traders, where four key parts of the system could be changed independently. They found that the way traders are selected mostly affects how diverse the trading strategies are, but doesn’t make the market more realistic. Changing how prices affect traders helps realism, while increasing biases in trader behavior makes the market more fragile without improving realism. Lastly, how traders come to an agreement doesn’t seem to have a clear effect, showing these parts work like separate controls for diversity, realism, and fragility in the market.
agent-based modelsprice formationbehavioral biasmarket realismstrategy diversityevolutionary selectionQuality-Diversity optimizationmarket fragilityconsensus networksmicrostructure feedback
Authors
Zhibao Chen
Abstract
Evolutionary agent-based markets (ABMs) couple several mechanisms -- who reproduces, how price forms, how biased the agents are, how consensus propagates -- yet these are usually fixed by convention, so it is unclear which mechanism controls which emergent property. In a coevolving, endogenous-price simulator with 120 heterogeneous behavioral agents, we make four mechanisms pluggable and run matched 3x20-seed interventions. We find the levers are largely separable. (1) Selection -> diversity: a Quality-Diversity (QD/MAP-Elites) operator robustly raises strategy-mix entropy over truncation top-k (paired Delta entropy +0.27 to +1.12 bits; sign-test p<0.001; CIs exclude 0) and sustains more strategy cycling (strongest in crisis: Delta=+0.070, p=0.0004). (2) Selection does not improve realism: even a per-agent realism reward that provably steers selection does not raise 5-fact realism (Delta_5=-0.11,-0.08,+0.03; not significant). (3) Microstructure -> realism: enabling reflexive price feedback does raise realism (Delta_5=+0.13,+0.20,+0.20; crisis/bull p<0.05, all CIs positive). (4) Behavior -> fragility: amplifying behavioral bias raises a genomic fragility proxy (Delta=+10.5,+11.1,+14.4; bull p<0.001, all CIs positive) while leaving realism flat. The remaining mechanism -- consensus network topology -- shows no robust effect (honest null). The contribution is a decomposition: in these single-mechanism sweeps the mechanisms behave as approximately distinct control knobs over diversity, realism, and fragility.