🤖 AI Summary
研究探讨了专家小组在报告公开且专家关注自身声誉的情况下，如何通过匿名方式披露利益冲突。使用不同的信息披露策略（完全不披露、个人归属、组成披露）来平衡信息透明度与专家激励机制。
📝 Abstract
We ask how an expert panel should disclose auditable conflicts of interest when reports are named and experts care about their reputations for ability. Before sealed binary assessments are filed, an auditor commits to disclosing no orientation information, each named expert's orientation, or only the number of experts oriented in each direction; disclosure occurs after reports are locked. Holding reporting behaviour fixed, composition disclosure and individual attribution are exactly Blackwell-equivalent for learning the state at every finite panel size. Conditional on named reports remaining public, composition is minimal among state-lossless disclosures of orientations: every lossless public message must make the composition recoverable together with the reports. Incentives nevertheless differ sharply. In the large-panel limit near pooling, the local career returns to following evidence against one's orientation under attribution, secrecy, and composition disclosure stand in the ratio 1:4:5. On an open set of parameters, the two extreme regimes have only pooling within symmetric cutoff strategies, while composition disclosure admits a tremble-robust informative equilibrium. Near the lower boundary of this region, a sequence of composition equilibria is sparse---the evidence-following rate is of order 1/N---yet induces a nondegenerate state experiment. Anonymous disclosure can therefore preserve the information available under attribution while creating stronger incentives to produce it.