对抗性筛选策略披露与注意力设计
Strategic Disclosure and the Design of Attention
- University of International Business and Economics（对外经济贸易大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究探讨如何分配曝光量以平衡对立倡导者的披露策略，通过博弈分析得出最优曝光规则，为法院、平台等设计决策机制提供理论依据。
AI中文摘要：
持相反立场的倡导者可能无法伪造证据，但可以选择呈现哪些真实观察结果。我们研究法院、编辑或平台应如何在偏好更高决策的倡导者与偏好更低决策的倡导者之间分配潜在曝光量。每位倡导者控制着独立的证据池，曝光量在状态和证据已知前就已确定，且存在选择偏差的接收者会对展示的观察结果取平均值。在基线线性偏好和已实现证据池为共同知识的条件下，所有有限证据池的纳什均衡都会产生相同的行动和共同披露阈值：向上调整的倡导者会披露高于该阈值的观察结果，向下调整的倡导者则披露低于该阈值的观察结果。因此，注意力既改变了选择也改变了权重。在普通证据法下，更多曝光会使倡导者发言频率降低、言论更极端，同时推动决策向其偏好方向移动。在大型证据池中，能重现完整证据决策的唯一状态依存曝光份额，需平衡倡导者的方向尾部矩而非其观察到的发言。由于该份额通常取决于未知状态，我们刻画了最优事前折中方案，并求解了具有唯一次优政策的原始两状态经济模型。对于有限证据池，我们推导了精确的风险最小化调整，区分了选择诱导的阈值偏差、接收者的固定基准以及抽样方差。注意力与阈值的反馈在部分权重空槽时仍存在，在完全归责时消失。
英文摘要：
An institution that cannot compel disclosure can still allocate exposure. We study two opposed advocates who select verifiable observations and a receiver who averages the selected record without correcting for selection. The institution commits exposure before the state and evidence are known; each withheld item's assigned weight expires. With commonly known pools and strictly opposed monotone preferences, all finite-game Nash equilibria produce one action and common cutoff. In large pools with a vanishing anchor, directional tail moments determine the implementing exposure share. Under common evidence laws, equal exposure implements every state. The institutional restriction matters: full within-camp reallocation induces extreme selection and, on a fixed support, eliminates state responsiveness. In a finite-state common-law benchmark, small reallocation separates disclosure cutoffs and changes implementing shares according to favorable-tail frequencies. Variation in these frequencies across states creates a positive local welfare cost even after exposure is optimized. Ex ante results bound the cost of commitment and identify information sufficient for limiting implementation. Separately, under linear payoffs, uniform item weights and continuous evidence laws on a compact state space, all Bayesian equilibria converge to the baseline outcome when advocates observe only their own samples and learn the state. Finite-pool results characterize a precision cost under common laws, balanced exposure, uniform weights and equal limiting camp sizes, and first corrections to exact risk-minimizing shares under additional regularity.
