TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets (2502.01506v4)

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, 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.

• The paper introduces TwinMarket’s LLM-driven framework to realistically simulate investor behavior and emergent market dynamics.
• It utilizes a BDI model and dynamic social networks to capture cognitive biases, information cascades, and volatility clustering.
• Experimental results validate the approach by reproducing stylized market statistics and revealing mechanisms behind self-fulfilling prophecies.

Evaluation of TwinMarket: A Scalable Simulation for Financial Markets

The paper introduces TwinMarket, a novel multi-agent framework that leverages LLMs for simulating socio-economic systems, with a specific focus on financial markets. This research contributes to the ongoing exploration of social emergence phenomena within social sciences, particularly addressing the limitations of traditional rule-based Agent-Based Models (ABMs).

Summary of TwinMarket Framework

The TwinMarket paradigm utilizes LLMs to simulate investor behavior in a stock market environment. This approach departs from the conventional reliance on static rules and homogeneous agent assumptions, incorporating cognitive biases, emotional fluctuations, and other non-rational influences emphasized in behavioral economics. The framework allows for a more realistic simulation of socio-economic dynamics, portraying how micro-level individual behaviors, through interactions and feedback mechanisms, give rise to collective dynamics and emergent phenomena like financial bubbles and recessions.

Key innovations in TwinMarket include:

1. Belief-Desire-Intention (BDI) Framework: LLMs are deployed to simulate cognitive processes of agents, providing transparent and interpretable models of decision-making, specifically within financial contexts.
2. Dynamic Social Network: Agents participate in a socially embedded environment where dynamic interactions and information propagation occur, enabling the paper of phenomena such as opinion leadership and information cascades.
3. Scalability and Realism: Simulations incorporate real-world data, focusing on socio-economic interactions within a stock market, ensuring alignment with real-world conditions and behavioral theories.

Empirical Insights and Contributions

Through experiments conducted in a simulated stock market environment, TwinMarket demonstrates how individual actions can trigger group behaviors, leading to emergent outcomes such as self-fulfilling prophecies and market volatility. The findings align with observed market dynamics, offering potential explanations for phenomena previously challenging to model with static ABMs.

Notable experimental results include:

• Emergence of Group Behavior: The platform successfully captures collective investor actions like herd behavior and market polarization, driven by rumor exposure and sentiment shifts, elucidating mechanisms behind market turbulence and stability.
• Market Dynamics Representation: TwinMarket simulations reveal classic stylized facts of financial markets such as fat-tailed return distributions, leverage effects, and volatility clustering without relying on oversimplified agent rules.

Implications and Future Directions

The implications of integrating LLMs into multi-agent simulations extend beyond financial markets, providing a versatile tool for investigating complex social systems where human behavior and choices are pivotal. The paper suggests several avenues for future research, including examining agent trust dynamics across varied scenarios and further aligning LLM outputs with human cognitive processes.

Additionally, exploring the potential of LLM-based frameworks in wider socio-economic models and other role-playing applications could expand understanding of social emergence phenomena. The scalability demonstrated by TwinMarket opens pathways for more comprehensive, data-driven models that reflect macro-level outcomes from micro-level interactions in real-time economic and social systems.

In conclusion, TwinMarket offers a promising approach to modeling complex financial systems, advancing the synthesis of LLM capabilities and ABM methodologies. This research contributes valuable insights into the subtle interplay of individual decision-making and collective socio-economic patterns, paving the way for more sophisticated and realistic models across diverse fields.