让智能体主动解释信念理由，提升信息聚合效率。
Tell Me Why: Incentivizing Explanations
- 设计新机制激励智能体说出信念背后的理由
- 理由披露后信息共享与独有部分更易识别
- 适用于需要高质量决策的协作场景
常识表明，当个体解释其信念原因时，我们能比仅听取结论获得更准确的判断。然而，目前尚无机制能有效激励智能体提供信念解释。这可能源于标准贝叶斯模型假设信号条件独立，从而无需解释即可实现高效信息聚合。本文认为，个体信念常来自重叠信息源，单纯报告信念无法揭示全部信息。因此，理性解释（即信念背后私有信息的理由）有助于高效识别共享与新信息，促进更优聚合。基于此，本文提出一种新型‘审议机制’，在该机制下，真实报告信念与理由构成完美贝叶斯均衡。
原文摘要 · Abstract (English)
Common sense suggests that when individuals explain why they believe something, we can arrive at more accurate conclusions than when they simply state what they believe. Yet, there is no known mechanism that provides incentives to elicit explanations for beliefs from agents. This likely stems from the fact that standard Bayesian models make assumptions (like conditional independence of signals) that preempt the need for explanations, in order to show efficient information aggregation. A natural justification for the value of explanations is that agents' beliefs tend to be drawn from overlapping sources of information, so agents' belief reports do not reveal all that needs to be known. Indeed, this work argues that rationales-explanations of an agent's private information-lead to more efficient aggregation by allowing agents to efficiently identify what information they share and what information is new. Building on this model of rationales, we present a novel 'deliberation mechanism' to elicit rationales from agents in which truthful reporting of beliefs and rationales is a perfect Bayesian equilibrium.
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