TwinMarket: A Scalable Behavioral and Social Simulation for Financial
  Markets
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.
67a4214f12b90b15dc5a648e
Yuzhe Yang
claimed_verified
2025-02-06T14:15:03.582Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63f622c69cbd6730302783eb/9cb96JVKiOm_JhF-shbFw.jpeg
Yuzhe Yang
user
TobyYang7
67a4214f12b90b15dc5a648f
Yifei Zhang
claimed_verified
2025-02-06T14:15:05.578Z
643c047326f177a3e41627b6
/avatars/ade75cebd049daf080ba80a80d516240.svg
Yifei Zhang
user
amstrongzyf
67a4214f12b90b15dc5a6490
Minghao Wu
claimed_verified
2025-06-09T10:14:39.422Z
65fbdbc8fc9132a2dfd67c8f
/avatars/9e404fca5f7b53d49e1aa73d525f834d.svg
Minghao Wu
user
magicc0nch
67a4214f12b90b15dc5a6491
Kaidi Zhang
67a4214f12b90b15dc5a6492
Yunmiao Zhang
claimed_verified
2025-02-11T10:03:21.159Z
67aafd473b9cb77cc2223819
/avatars/e285666fdc918564071be26136fe3312.svg
Yunmiao Zhang
user
Yunwater
67a4214f12b90b15dc5a6493
Honghai Yu
67a4214f12b90b15dc5a6494
Yan Hu
67a4214f12b90b15dc5a6495
Benyou Wang
admin_assigned
2025-02-06T14:34:30.901Z
637c6703ca8542a0ba900ccb
/avatars/288ed63a1efa566c3f01e850c6ba5dd5.svg
Wang
user
Benyou
TwinMarket: A Scalable Behavioral and Social Simulation for Financial
  Markets
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.
TwinMarket, a multi-agent framework using large language models, simulates socio-economic dynamics and emergent phenomena like financial bubbles and recessions in a simulated stock market.
Agent-Based Models large language models cognitive biases emotional fluctuations socio-economic dynamics collective dynamics emergent phenomena simulated stock market financial bubbles recessions individual decision-making