Financial Market as a Self-Organized Ecosystem: Simulation via Learning with Heterogeneous Preferences

TL;DR

This paper presents a multi-agent reinforcement learning model of financial markets where agents with diverse preferences develop differentiated strategies, leading to realistic market phenomena like fat tails and volatility clustering.

Contribution

It introduces a joint modeling framework of learning and heterogeneity in agents, demonstrating their combined effect on emergent market dynamics.

Findings

Agents develop role-specific strategies through interaction.

Heterogeneous preferences and learning produce realistic market features.

Interactions are crucial for market self-organization.

Abstract

Agent-based models provide a constructive approach to studying emergent dynamics in life-like systems composed of interacting, adaptive agents. Financial markets serve as a canonical example of such systems, where collective price dynamics arise from individual decision-making. In this modeling tradition, investor behavior has typically been captured by two distinct mechanisms -- learning and heterogeneous preferences -- which have been explored as separate paradigms in prior studies. However, the impact of their joint modeling on the resulting collective dynamics remains largely unexplored. We develop a multi-agent reinforcement learning framework in which agents endowed with heterogeneous risk aversion, time discounting, and information access learn trading strategies interactively within an artificial market. The experiment reveals that (i) learning under heterogeneous preferences drives agents to develop functionally differentiated strategies through interaction, rather than trait-specific rules, resulting in role specialization, and (ii) the interactions by the differentiated agents are essential for the emergence of realistic market dynamics such as fat-tailed price fluctuations and volatility clustering. Overall, this study demonstrates that the joint design of heterogeneous preferences and learning mechanisms enables the synthesis of an artificial market in which adaptive interactions drive the self-organization of a market ecology, providing a computational realization of the Adaptive Market Hypothesis.