TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets

Yuzhe YANG, Yifei Zhang, Minghao Wu, Kaidi Zhang, Yunmiao Zhang, Honghai Yu, Yan Hu, Benyou Wang

Keywords: LLM Agent, Multi-Agent System, Human-Behavior, Computational Social Science, Computational Finance, Market Impact, Financial Market Simulation

TL;DR: We introduce TwinMarket, an LLM-based multi-agent framework that simulates socio-economic systems by modeling how individual behaviors interact to produce emergent market dynamics like bubbles and crashes.

Abstract: The study of social emergence has long been a central focus in social science. Traditional modeling approaches, such as rule-based Agent-Based Models (ABMs), struggle to capture the diversity and complexity of human behavior, particularly the irrational factors emphasized in behavioral economics. Recently, large language model (LLM) agents have gained traction as simulation tools for modeling human behavior in social science and role-playing applications. Studies suggest that LLMs can account for cognitive biases, emotional fluctuations, and other non-rational influences, enabling more realistic simulations of socio-economic dynamics. In this work, we introduce TwinMarket, a novel multi-agent framework that leverages LLMs to simulate socio-economic systems. Specifically, we examine how individual behaviors, through interactions and feedback mechanisms, give rise to collective dynamics and emergent phenomena. Through experiments in a simulated stock market environment, we demonstrate how individual actions can trigger group behaviors, leading to emergent outcomes such as financial bubbles and recessions. Our approach provides valuable insights into the complex interplay between individual decision-making and collective socio-economic patterns.

Primary Area: Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Flagged For Ethics Review: true

Submission Number: 852