TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets

• URL: http://arxiv.org/abs/2502.01506v2
• Date: Wed, 05 Feb 2025 13:18:13 GMT
• Title: TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets
• Authors: Yuzhe Yang, Yifei Zhang, Minghao Wu, Kaidi Zhang, Yunmiao Zhang, Honghai Yu, Yan Hu, Benyou Wang,
• Abstract summary: Large language model (LLM) agents have gained traction as simulation tools for modeling human behavior. We introduce TwinMarket, a novel multi-agent framework that leverages LLMs to simulate socio-economic systems. Our approach provides valuable insights into the complex interplay between individual decision-making and collective socio-economic patterns.
• Score: 24.354300029071418
• License:
• 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.

Related papers

• AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society [32.849311155921264]
  We propose AgentSociety, a large-scale social simulator that integrates a realistic societal environment. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions. We focus on four key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, and the impact of external shocks such as hurricanes.
  arXiv  Detail & Related papers  (2025-02-12T15:27:07Z)
• Emergence of human-like polarization among large language model agents [61.622596148368906]
  We simulate a networked system involving thousands of large language model agents, discovering their social interactions, result in human-like polarization. Similarities between humans and LLM agents raise concerns about their capacity to amplify societal polarization, but also hold the potential to serve as a valuable testbed for identifying plausible strategies to mitigate it.
  arXiv  Detail & Related papers  (2025-01-09T11:45:05Z)
• LMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation [66.52371505566815]
  Large language models (LLMs)-based AI agents have made significant progress, enabling them to achieve human-like intelligence. We present LMAgent, a very large-scale and multimodal agents society based on multimodal LLMs. In LMAgent, besides chatting with friends, the agents can autonomously browse, purchase, and review products, even perform live streaming e-commerce.
  arXiv  Detail & Related papers  (2024-12-12T12:47:09Z)
• From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents [47.935533238820334]
  Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and facilitating studies on many interdisciplinary studies. We categorize the simulations into three types: (1) Individual Simulation, which mimics specific individuals or demographic groups; (2) Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and (3) Simulation Society, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics.
  arXiv  Detail & Related papers  (2024-12-04T18:56:37Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents [18.961470450132637]
  This paper emphasizes the importance of spontaneous phenomena, wherein agents deeply engage in contexts and make adaptive decisions without explicit directions. We explored spontaneous cooperation across three competitive scenarios and successfully simulated the gradual emergence of cooperation.
  arXiv  Detail & Related papers  (2024-02-19T18:00:53Z)
• LLM-driven Imitation of Subrational Behavior : Illusion or Reality? [3.2365468114603937]
  Existing work highlights the ability of Large Language Models to address complex reasoning tasks and mimic human communication. We propose to investigate the use of LLMs to generate synthetic human demonstrations, which are then used to learn subrational agent policies. We experimentally evaluate the ability of our framework to model sub-rationality through four simple scenarios.
  arXiv  Detail & Related papers  (2024-02-13T19:46:39Z)
• Systematic Biases in LLM Simulations of Debates [12.933509143906141]
  We study the limitations of Large Language Models in simulating human interactions. Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases. These results underscore the need for further research to develop methods that help agents overcome these biases.
  arXiv  Detail & Related papers  (2024-02-06T14:51:55Z)
• Training Socially Aligned Language Models on Simulated Social Interactions [99.39979111807388]
  Social alignment in AI systems aims to ensure that these models behave according to established societal values. Current language models (LMs) are trained to rigidly replicate their training corpus in isolation. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions.
  arXiv  Detail & Related papers  (2023-05-26T14:17:36Z)
• Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning [72.23843557783533]
  We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types. Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
  arXiv  Detail & Related papers  (2022-01-03T17:00:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.