# TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets

arXiv: https://arxiv.org/abs/2502.01506 · HTML: https://mlsanity.com/paper/2502.01506/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2025.

## Built on by (most cited first, 30 of 48)

- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)
- [R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science](https://mlsanity.com/paper/2505.14738/) (2025)
- [Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook](https://mlsanity.com/paper/2602.14299/) (2026)
- [The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies](https://mlsanity.com/paper/2509.18052/) (2025)
- [MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework](https://mlsanity.com/paper/2504.21582/) (2025)
- [Evaluating LLMs in Finance Requires Explicit Bias Consideration](https://mlsanity.com/paper/2602.14233/) (2026)
- [MASS: Muli-agent simulation scaling for portfolio construction](https://mlsanity.com/paper/2505.10278/) (2025)
- [Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits](https://mlsanity.com/paper/2605.18890/) (2026)
- [Building Social World Models with Large Language Models](https://mlsanity.com/paper/2606.11482/) (2026)
- [Integrating Large Language Models in Financial Investments and Market Analysis: A Survey](https://mlsanity.com/paper/2507.01990/) (2025)
- [MASLegalBench: Benchmarking Multi-Agent Systems in Deductive Legal Reasoning](https://mlsanity.com/paper/2509.24922/) (2025)
- [Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through Stock-Market Simulation](https://mlsanity.com/paper/2602.07023/) (2026)
- [Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations](https://mlsanity.com/paper/2510.13982/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [MEME: Modeling the Evolutionary Modes of Financial Markets](https://mlsanity.com/paper/2602.11918/) (2026)
- [Beyond Self-Interest: Modeling Social-Oriented Motivation for Human-like Multi-Agent Interactions](https://mlsanity.com/paper/2603.13890/) (2026)
- [Strategic Exploitation in LLM Agent Markets: A Simulation Framework for E-Commerce Trust](https://mlsanity.com/paper/2605.10059/) (2026)
- [InsurAgent: A Large Language Model-Empowered Agent for Simulating Individual Behavior in Purchasing Flood Insurance](https://mlsanity.com/paper/2511.02119/) (2025)
- [LLM-based Multi-Agent System for Simulating Strategic and Goal-Oriented Data Marketplaces](https://mlsanity.com/paper/2511.13233/) (2025)
- [Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform](https://mlsanity.com/paper/2512.08478/) (2025)
- [Interpreting Emergent Extreme Events in Multi-Agent Systems](https://mlsanity.com/paper/2601.20538/) (2026)
- [Invariant Causal Routing for Governing Social Norms in Online Market Economies](https://mlsanity.com/paper/2603.04534/) (2026)
- [From Natural Language to Executable Option Strategies via Large Language Models](https://mlsanity.com/paper/2603.16434/) (2026)
- [Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents](https://mlsanity.com/paper/2604.22452/) (2026)
- [Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench](https://mlsanity.com/paper/2605.17079/) (2026)
- [Can LLM Agents Sustain Long-Horizon Organizational Dynamics?](https://mlsanity.com/paper/2606.01199/) (2026)
- [FinPersona-Bench: A Benchmark for Longitudinal Psychometric Stability of Autonomous Financial Agents](https://mlsanity.com/paper/2606.31522/) (2026)
- [Improving Behavioral Alignment in LLM Social Simulations via Context Formation and Navigation](https://mlsanity.com/paper/2601.01546/) (2026)
- [The textitSilicon Society Cookbook: Design Space of LLM-based Social Simulations](https://mlsanity.com/paper/2605.00197/) (2026)

## Abstract (arXiv)

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.