Beyond Market Consensus: Modeling Trader Heterogeneity through Behavioral Residuals

Realistic market simulation requires agents to preserve persistent behavioral differences, yet large language model (LLM) agents often converge toward average behavior when exposed to similar market conditions. We argue that trader heterogeneity can be characterized by how individuals systematically deviate from shared market consensus. Based on this insight, we propose Consensus-Guided Residual Behavior Modeling (CRBM), which decomposes trading behavior into a consensus component shared across traders and a behavioral residual capturing trader-specific deviations. CRBM derives consensus signals from local market contexts, estimates persistent residual patterns from individual transaction histories, and integrates them through a residual-conditioned expert mechanism to generate structured trading orders. Using real transaction records from Polymarket, we evaluate CRBM at three levels: individual behavioral fidelity, cross-trader heterogeneity, and emergent market dynamics. CRBM consistently improves transaction-level prediction and joint action–outcome matching over competitive baselines, while ablations demonstrate the complementary roles of consensus modeling and behavioral residuals. In Monte Carlo market replays, CRBM preserves wallet-specific behavioral differences and produces aggregate price dynamics consistent with observed market evolution. Zero-shot evaluation on Manifold further provides preliminary evidence of cross-platform generalization. These results suggest that modeling individual behavior as structured deviations from shared consensus provides an effective approach to preserving agent heterogeneity in LLM-based market simulation.