When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection

信息共享何时能改善去中心化发现？聚合、独立救援与均衡选择

Abstract: Information sharing can improve a pooled estimate while eliminating independent rescue actions. This paper separates those effects in exact finite discovery models. 摘要： 信息共享可以在消除独立救援行动的同时，改善汇总估计。本文在精确的有限发现模型中区分了这些效应。

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. 一个中心化的行动预算配置表明，相同的一人准确率可以与不同的投资组合价值并存。在注册的增量共享协议下，当汇总残差的收缩速度快于独立救援尝试时，共享步骤便能精确地改善发现效果。

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. 精确的有界注册表表现出压缩、聚合、中性曲线以及有界零混合类特征。在一个包含公共信号源和独立信号源隐藏混合的双智能体贝叶斯博弈中，注册选择均衡在信号准确率为 3/5 时产生了一个严格的正共享区间，而其他均衡则表明该结果取决于选择机制，而非普适性的。

The models are synthetic and finite; no human or organizational data are used. 这些模型是合成且有限的；未使用任何人类或组织数据。