Overview of TwinMarket environment, where each user has a unique persona
within the social network, interacts with the environment in real-time, and influences it
through their actions. This framework enables the study of emergent social phenomena.
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.
@misc{yang2025twinmarketscalablebehavioralsocial,
title={TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets},
author={Yuzhe Yang and Yifei Zhang and Minghao Wu and Kaidi Zhang and Yunmiao Zhang and Honghai Yu and Yan Hu and Benyou Wang},
year={2025},
eprint={2502.01506},
archivePrefix={arXiv},
primaryClass={cs.CE},
url={https://arxiv.org/abs/2502.01506},
}