Premium
Dynamically Aggregating Diverse Information
Author(s) -
Liang Annie,
Mu Xiaosheng,
Syrgkanis Vasilis
Publication year - 2022
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta18324
Subject(s) - computer science , component (thermodynamics) , sample (material) , brownian motion , gaussian , point (geometry) , binary number , mathematical optimization , mathematics , statistics , chemistry , physics , geometry , arithmetic , chromatography , quantum mechanics , thermodynamics
An agent has access to multiple information sources, each modeled as a Brownian motion whose drift provides information about a different component of an unknown Gaussian state. Information is acquired continuously—where the agent chooses both which sources to sample from, and also how to allocate attention across them—until an endogenously chosen time, at which point a decision is taken. We demonstrate conditions on the agent's prior belief under which it is possible to exactly characterize the optimal information acquisition strategy. We then apply this characterization to derive new results regarding: (1) endogenous information acquisition for binary choice, (2) the dynamic consequences of attention manipulation, and (3) strategic information provision by biased news sources.