研究信息共享何时提升分布式发现效率，揭示了共享与独立救援的权衡关系。
When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection
- 通过有限模型分离共享与独立救援效应，明确共享生效条件。
- 当联合残差误差收缩快于独立救援时，共享能提升发现效果。
- 结果依赖均衡选择，适用于信号准确率3/5以上场景。
信息共享可改善联合估计并消除独立救援行为。本文在精确的有限发现模型中分离二者效应。集中式行动预算配置显示，单人准确率相同时，组合价值可不同。在注册增量共享协议下，共享步骤仅在联合残差误差收缩速度超过独立救援时才提升发现。有限注册机制呈现压缩、聚合、中性曲线及有界零混合类。在两主体贝叶斯博弈中，隐藏混合信号源（共通与独立）下，注册选定均衡在信号准确率3/5时存在严格正向共享区间，而其他均衡表明结果依赖于均衡选择而非普遍成立。模型为合成且有限，未使用人类或组织数据。
原文摘要 · Abstract (English)
Information sharing can improve a pooled estimate while eliminating independent rescue actions. This paper separates those effects in exact finite discovery models. A centralized action-budget profile shows that equal one-person accuracy can coexist with different portfolio values. Under a registered incremental-sharing protocol, a sharing step improves discovery exactly when pooled residual error contracts faster than an independent rescue attempt. Exact bounded registries exhibit compression, aggregation, neutral curves, and a bounded zero mixed class. In a two-agent Bayesian game with a hidden mixture of common and independent signal sources, the registered selected equilibrium yields a strict positive sharing interval at signal accuracy 3/5, while alternative equilibria show that the result is selection-dependent rather than universal. The models are synthetic and finite; no human or organizational data are used.
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