NeurIPS 2025｜当AI学会"炒股"：用千个虚拟投资者重现金融市场涌现现象

Core Insights - The article discusses TwinMarket, a scalable behavioral and social simulation platform for financial markets driven by large language models (LLMs), aiming to replicate human-like decision-making and social interactions in trading environments [2][4]. Group 1: Traditional Market Simulation Limitations - Traditional market simulation methods rely on preset rules, leading to three fundamental limitations: behavior homogeneity, lack of social interaction, and black-box cognitive processes [5][6]. - These models often assume a "standard investor," failing to capture the heterogeneity of real market participants [6]. - Social media influences and the complexity of information dissemination are inadequately modeled in traditional frameworks [6]. Group 2: TwinMarket's Innovations - TwinMarket introduces the Belief-Desire-Intention (BDI) cognitive framework, marking a paradigm shift from rule-based to cognitive reasoning models [7][10]. - The BDI framework allows AI agents to reflect on their decisions, enhancing their learning capabilities through cognitive updates rather than gradient descent [12]. Group 3: Data-Driven Simulation Environment - TwinMarket is grounded in real data, utilizing trading records from 639 investors and 11,965 transactions to initialize user profiles [15][19]. - The platform incorporates various data sources, including stock recommendations and news articles, to simulate a realistic trading environment [20]. Group 4: Micro and Macro Behavioral Insights - The simulation reveals that wealth inequality naturally emerges and expands within a fair virtual market, with the Gini coefficient increasing over time [25][26]. - Frequent trading correlates with poorer returns, reflecting human behavioral biases such as overconfidence and emotional decision-making [27]. Group 5: Stylized Facts Validation - TwinMarket successfully replicates four stylized facts of real markets: fat-tailed distribution, leverage effect, volume-price relationship, and volatility clustering [31][32][33][34]. - The simulation captures the phenomenon of collective behavior leading to market volatility, demonstrating how individual biases can amplify into macroeconomic crises [36]. Group 6: Scalability and Practical Applications - TwinMarket exhibits strong scalability, maintaining high correlation with real market price movements even in large-scale experiments with 1,000 agents [44][46]. - The platform serves as a valuable tool for understanding complex socio-economic systems, allowing researchers to test theories and evaluate regulatory impacts in a controlled environment [52][56]. Group 7: Future Directions - Future developments aim to enhance market mechanisms and introduce macroeconomic interactions, expanding the simulation's applicability to various financial ecosystems [64][65]. - The potential for cross-disciplinary applications, including political and public health simulations, is also recognized [66].