用有限预算提升群体决策准确率，优化信号分配策略
Observational Learning with a Budget
- 基于贝叶斯观测学习模型，动态分配预算优化信号质量
- 提出两种最优分配策略，可最大化正确信息级联概率
- 适合研究群体智能、资源受限的决策系统设计者
我们研究一种贝叶斯观测学习模型，其中一系列代理根据关于世界二元状态的私有信号做出决策，同时观察前序代理的行为。中央规划者拥有有限预算，可通过提升信号质量来改进整体判断准确性。本文提出了预算分配问题的建模与分析方法，并设计了两种最优分配策略。至少一种策略被证明能最大化实现正确信息级联的概率。
原文摘要 · Abstract (English)
We consider a model of Bayesian observational learning in which a sequence of agents receives a private signal about an underlying binary state of the world. Each agent makes a decision based on its own signal and its observations of previous agents. A central planner seeks to improve the accuracy of these signals by allocating a limited budget to enhance signal quality across agents. We formulate and analyze the budget allocation problem and propose two optimal allocation strategies. At least one of these strategies is shown to maximize the probability of achieving a correct information cascade.
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