2025/04/28 by Shuo Wu, Wu, Shuo, Pawan Poojary +3
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Game Theory and Applications #Machine Learning (cs.LG) #Machine Learning and Algorithms #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2504.19396
openalex publication_date 2025/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.