Agent-based models help explain stock price dynamics as emergent phenomena driven by interacting investors. 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 collectively learn trading strategies within a unified shared-policy framework. The experiment reveals that (i) learning with heterogeneous preferences drives agents to develop strategies aligned with their individual traits, fostering behavioral differentiation and niche specialization within the market, 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. This study presents a constructive paradigm for financial market modeling in which the joint design of heterogeneous preferences and learning mechanisms enables two-stage emergence: individual behavior and the collective market dynamics.

翻译：基于智能体的模型有助于将股票价格动态解释为由相互作用的投资者驱动的涌现现象。在这一建模传统中，投资者行为通常通过两种不同的机制——学习和异质偏好——来刻画，这两种机制在先前研究中作为独立的范式被探索。然而，它们的联合建模对所产生的集体动态的影响在很大程度上仍未得到探索。我们开发了一个多智能体强化学习框架，其中被赋予异质风险厌恶、时间贴现和信息访问能力的智能体在统一的共享策略框架内共同学习交易策略。实验表明：（i）异质偏好学习驱动智能体发展出与其个体特征相一致的策略，促进了市场内的行为分化和生态位专业化；（ii）差异化智能体之间的相互作用对于实现现实市场动态（如厚尾价格波动和波动率聚集）的涌现至关重要。本研究提出了一个金融市场建模的建构性范式，其中异质偏好与学习机制的联合设计实现了两阶段涌现：个体行为和集体市场动态。