- Title
- Dynamically Aggregating Diverse Information
- Author(s)
- Annie Liang Annie Liang (Northwestern University)
- Xiaosheng Mu Xiaosheng Mu (Princeton University)
- Vasilis Syrgkanis Vasilis Syrgkanis (Microsoft Research)
- Abstract
- 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.
- Creation Date
- 2021-06
- Section URL ID
- Paper Number
- 2021-43
- URL
- https://uploads.strikinglycdn.com/files/df229e97-224f-496d-b8c7-c6fa0b4f0056/Dynamic_Info_Acquisition.pdf
- File Function
- Jel
- D83
- Keyword(s)
- information aggregation
- Suppress
- false
- Series
- 13