信息讨价还价：贝叶斯说服中的双边承诺
Information Bargaining: Bilateral Commitment in Bayesian Persuasion
AI总结：
针对长期贝叶斯说服的 NP 困难问题，论文提出双边承诺的信息讨价还价框架，区分信息优势与先提议者优势，并用 GPT-o3、DeepSeek-R1 验证其预测。
摘要：
贝叶斯说服是廉价磋商的扩展，指掌握信息的发送者承诺采用某种信号方案以影响接收者的行动。与廉价磋商相比，发送者的这种承诺使接收者能够事先验证信号的激励相容性，从而促进合作。尽管在一次性场景中有效，但当扩展到长期互动时，贝叶斯说服面临计算复杂性（NP 困难性），因为接收者可能会根据过去的结果和未来预期采取动态策略。为应对这一复杂性，我们引入讨价还价视角，它能够：（1）为长期说服提供统一框架和结构良好的解概念，并具备公平性和帕累托效率等理想性质；（2）清晰区分此前被混为一谈的两种优势：发送者的信息优势与先提议者优势。只需对标准设定作适度修改，该视角便明确了博弈结构的共同知识，并赋予接收者可比的承诺能力，从而将经典的单边说服重新解释为一个平衡的信息讨价还价框架。该框架通过一个两阶段验证与推理范式得到检验：我们首先证明，在公开可用的 LLM 中，GPT-o3 和 DeepSeek-R1 能够可靠处理标准任务；随后将其应用于说服场景，以检验结果是否与我们的信息讨价还价框架所预示的一致。所有代码、结果和终端日志均公开于 github.com/YueLin301/InformationBargaining。
英文摘要：
Bayesian persuasion, an extension of cheap-talk communication, involves an informed sender committing to a signaling scheme to influence a receiver's actions. Compared to cheap talk, this sender's commitment enables the receiver to verify the incentive compatibility of signals beforehand, facilitating cooperation. While effective in one-shot scenarios, Bayesian persuasion faces computational complexity (NP-hardness) when extended to long-term interactions, where the receiver may adopt dynamic strategies conditional on past outcomes and future expectations. To address this complexity, we introduce the bargaining perspective, which allows: (1) a unified framework and well-structured solution concept for long-term persuasion, with desirable properties such as fairness and Pareto efficiency; (2) a clear distinction between two previously conflated advantages: the sender's informational advantage and first-proposer advantage. With only modest modifications to the standard setting, this perspective makes explicit the common knowledge of the game structure and grants the receiver comparable commitment capabilities, thereby reinterpreting classic one-sided persuasion as a balanced information bargaining framework. The framework is validated through a two-stage validation-and-inference paradigm: We first demonstrate that GPT-o3 and DeepSeek-R1, out of publicly available LLMs, reliably handle standard tasks; We then apply them to persuasion scenarios to test that the outcomes align with what our information-bargaining framework suggests. All code, results, and terminal logs are publicly available at github.com/YueLin301/InformationBargaining.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
