title: TwinMarket: A Scalable Behavioral and Social Simulation for Financial
    Markets
summary: 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.
paper_authors._id: 67a4214f12b90b15dc5a648e
paper_authors.name: Yuzhe Yang
paper_authors.status: claimed_verified
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paper_authors.fullname: Yuzhe Yang
paper_authors.type: user
paper_authors.user: TobyYang7
paper_authors._id: 67a4214f12b90b15dc5a648f
paper_authors.name: Yifei Zhang
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paper_authors.fullname: Yifei Zhang
paper_authors.type: user
paper_authors.user: amstrongzyf
paper_authors._id: 67a4214f12b90b15dc5a6490
paper_authors.name: Minghao Wu
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/73c6228e317cf37b4e3c3e7a4b3d8ae8.svg
paper_authors.fullname: Minghao Wu
paper_authors.type: user
paper_authors.user: minghaowu
paper_authors._id: 67a4214f12b90b15dc5a6491
paper_authors.name: Kaidi Zhang
paper_authors._id: 67a4214f12b90b15dc5a6492
paper_authors.name: Yunmiao Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T10:03:21.159Z
paper_authors._id: 67aafd473b9cb77cc2223819
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paper_authors.fullname: Yunmiao Zhang
paper_authors.type: user
paper_authors.user: Yunwater
paper_authors._id: 67a4214f12b90b15dc5a6493
paper_authors.name: Honghai Yu
paper_authors._id: 67a4214f12b90b15dc5a6494
paper_authors.name: Yan Hu
paper_authors._id: 67a4214f12b90b15dc5a6495
paper_authors.name: Benyou Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-06T14:34:30.901Z
paper_authors._id: 637c6703ca8542a0ba900ccb
paper_authors.avatarUrl: /avatars/288ed63a1efa566c3f01e850c6ba5dd5.svg
paper_authors.fullname: Wang
paper_authors.type: user
paper_authors.user: Benyou
paper_title: TwinMarket: A Scalable Behavioral and Social Simulation for Financial
    Markets
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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