发表机构
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出结合LLM智能体与上下文赌博机的分散二分匹配框架，在模拟婚姻市场中验证其优于经典方法，实现更高福利和更少阻塞对。
AI 中文摘要
二分匹配是博弈论和市场设计中的基本问题。Gale-Shapley等经典方法假设完全偏好和集中式计算，而许多现实世界的匹配过程是分散的、异步的，并在有限信息下由顺序互动塑造。我们提出了一种动态二分匹配框架，将大语言模型（LLM）智能体与上下文赌博机相结合。在模拟的中国婚姻市场中，经济上合理的LLM智能体评估本地遇到的候选人，而特定于智能体的Logistic-UCB模型从已实现的提案结果中学习互惠接受。因此，该机制分离了两个决策——\textit{我喜欢谁？}和\textit{谁可能喜欢我？}——而无需事前市场范围的偏好排名。我们首先针对多个LLM骨干，将LLM诱导的伴侣偏好与经验条件Logit参考进行验证。在$50\ imes50$匹配实验中，Bandit-UCB实现了最高的平均互惠福利（56.01，而Gale-Shapley为54.87），比经典基线更小的性别排名差距，以及在LLM-ABM策略中最少的阻塞对。学习到的接受模型显示出经济上可解释的性别差异化关联，而反事实设置则揭示先前搜索知识没有系统性的单边优势。总体而言，这些结果支持在信息不完整下，基于LLM的行为建模和在线学习的分散匹配在经济模拟和计算社会科学研究中的优势。
英文摘要
Bipartite matching is a fundamental problem in game theory and market design. Classical approaches such as Gale--Shapley assume complete preferences and centralized computation, whereas many real-world matching processes are decentralized, asynchronous, and shaped by sequential interaction under limited information. We propose a dynamic bipartite matching framework that combines large language model (LLM) agents with contextual bandits. In a simulated Chinese marriage market, economically grounded LLM agents evaluate locally encountered candidates, while agent-specific Logistic-UCB models learn reciprocal acceptance from realized proposal outcomes. The mechanism therefore separates two decisions---\emph{whom do I like?} and \emph{who is likely to like me back?}---without requiring ex ante market-wide preference rankings. We first validate LLM-induced mate preferences against the empirical conditional-logit reference across multiple LLM backbones. In the $50\times50$ matching experiment, Bandit-UCB achieves the highest mean mutual welfare (56.01 versus 54.87 for Gale--Shapley), a smaller gender rank gap than the classical baselines, and the fewest blocking pairs among the LLM-ABM policies. Learned acceptance models show economically interpretable gender-differentiated associations, while counterfactual setups reveal no systematic unilateral advantage from prior search knowledge. Overall, these results support the advantages of decentralized matching with LLM-based behavioral modeling and online learning under incomplete information for economic simulation and computational social science research.
