Title: InfoBid: A Simulation Framework for Studying Information Disclosure in Auctions with Large Language Model-based Agents Show Chinese title

Title: InfoBid：基于大型语言模型代理的拍卖信息披露模拟框架

Abstract: In online advertising systems, publishers often face a trade-off in information disclosure strategies: while disclosing more information can enhance efficiency by enabling optimal allocation of ad impressions, it may lose revenue potential by decreasing uncertainty among competing advertisers. Similar to other challenges in market design, understanding this trade-off is constrained by limited access to real-world data, leading researchers and practitioners to turn to simulation frameworks. The recent emergence of large language models (LLMs) offers a novel approach to simulations, providing human-like reasoning and adaptability without necessarily relying on explicit assumptions about agent behavior modeling. Despite their potential, existing frameworks have yet to integrate LLM-based agents for studying information asymmetry and signaling strategies, particularly in the context of auctions. To address this gap, we introduce InfoBid, a flexible simulation framework that leverages LLM agents to examine the effects of information disclosure strategies in multi-agent auction settings. Using GPT-4o, we implemented simulations of second-price auctions with diverse information schemas. The results reveal key insights into how signaling influences strategic behavior and auction outcomes, which align with both economic and social learning theories. Through InfoBid, we hope to foster the use of LLMs as proxies for human economic and social agents in empirical studies, enhancing our understanding of their capabilities and limitations. This work bridges the gap between theoretical market designs and practical applications, advancing research in market simulations, information design, and agent-based reasoning while offering a valuable tool for exploring the dynamics of digital economies. Show Chinese abstract

Abstract: 在在线广告系统中，发布商常常面临信息披露策略上的权衡：虽然披露更多信息可以通过实现广告展示的最佳分配来提高效率，但它可能会通过降低竞争广告商的不确定性而失去潜在收入。 与其他市场设计中的挑战类似，理解这种权衡受到对现实世界数据访问有限的制约，这促使研究人员和从业者转向模拟框架。 大型语言模型（LLMs）的近期出现提供了一种新的模拟方法，它能够提供类人推理和适应性，而不需要一定依赖于明确的代理行为建模假设。 尽管有潜力，现有的框架尚未整合基于LLM的代理来研究信息不对称和信号传递策略，尤其是在拍卖背景下。 为了解决这一差距，我们引入了InfoBid，这是一个灵活的模拟框架，利用LLM代理来研究多代理拍卖环境中信息披露策略的影响。 使用GPT-4o，我们实现了具有多种信息模式的二价拍卖的模拟。 结果显示了信号传递如何影响战略行为和拍卖结果的关键见解，这些结果与经济理论和社会学习理论相一致。 通过InfoBid，我们希望促进LLMs作为人类经济和社会代理的代理在实证研究中的应用，增强我们对其能力和局限性的理解。 这项工作弥合理论市场设计与实际应用之间的鸿沟，在市场模拟、信息设计和基于代理的推理研究方面取得进展，同时为探索数字经济发展动态提供了有价值的工具。