发表机构
Untapped Capital（Untapped资本）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文在精确有限发现模型中分离信息共享的两种效应，在特定协议与博弈中验证信息共享的效果依赖均衡选择，相关模型为合成有限模型。
AI 中文摘要
信息共享可提升汇总估计值，同时消除独立救援行动。本文在精确有限发现模型中分离了这些效应。集中式行动预算剖面显示，单人准确率相同时可共存不同的组合价值。在注册式增量共享协议下，当汇总残差误差的收缩速度快于独立救援尝试时，共享步骤可精确改善发现效果。精确有界注册表现出压缩、聚合、中性曲线及有界零混合类。在含共同与独立信号源隐藏混合的两智能体贝叶斯博弈中，注册选定均衡在信号准确率为3/5时产生严格正的共享区间，而其他均衡表明该结果依赖均衡选择而非普适性。模型为合成有限模型，未使用人类或组织数据。
英文摘要
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.
Commentsworking paper; deterministic source package; exact bounded evidence
