2017/03/18 by Annie Liang, Liang, Annie, Xiaosheng Mu +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Receptor Mechanisms and Signaling #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1703.06367
openalex publication_date 2017/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of optimal dynamic information acquisition from many correlated information sources. Each period, the decision-maker jointly takes an action and allocates a fixed number of observations across the available sources. His payoff depends on the actions taken and on an unknown state. In the canonical setting of jointly normal information sources, we show that the optimal dynamic information acquisition rule proceeds myopically after finitely many periods. If signals are acquired in large blocks each period, then the optimal rule turns out to be myopic from period 1. These results demonstrate the possibility of robust and "simple" optimal information acquisition, and simplify the analysis of dynamic information acquisition in a widely used informational environment.