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.

翻译：在在线广告系统中，发布者常面临信息揭示策略的权衡：披露更多信息虽可通过优化广告展示分配提升效率，却可能因降低竞标广告商间的不确定性而损失潜在收益。与市场设计中的其他挑战类似，对此权衡的理解受限于现实数据的获取，促使研究人员与从业者转向仿真框架。近期兴起的大语言模型为仿真提供了新途径，其具备类人的推理与适应能力，且无需依赖对智能体行为建模的显式假设。尽管潜力巨大，现有框架尚未整合基于大语言的智能体来研究信息不对称与信号传递策略，尤其在拍卖情境中。为填补这一空白，我们提出InfoBid——一个灵活的仿真框架，利用大语言模型智能体探究多智能体拍卖场景中信息揭示策略的影响。基于GPT-4o，我们实现了多种信息架构下的二价拍卖仿真。结果揭示了信号传递如何影响策略行为与拍卖结果的关键机制，这些发现与经济理论及社会学习理论相契合。通过InfoBid，我们期望推动大语言模型在实证研究中作为人类经济与社会智能体代理的应用，深化对其能力与局限的理解。本工作弥合了理论市场设计与实际应用间的鸿沟，推进了市场仿真、信息设计与基于智能体的推理研究，并为探索数字经济动态提供了有力工具。