Publication | Open Access
Dynamically Aggregating Diverse Information
14
Citations
32
References
2022
Year
Bayesian Decision TheoryEngineeringBehavioral Decision MakingData AggregationGame TheoryCommunicationCombinatorial Data AnalysisJournalismText MiningInformation RetrievalData ScienceData MiningBiasManagementData IntegrationAggregating Diverse InformationDecision TheoryMechanism DesignCognitive ScienceKnowledge DiscoveryAttention ManipulationInformation AsymmetrySequential Decision MakingComputer ScienceBrownian MotionInformation ManagementGamesImperfect Information GameExploration V ExploitationUnknown Gaussian StateInformation EconomicsData HeterogeneityDecision ScienceEconomics Of Information
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.
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