- Title
- Optimal and Myopic Information Acquisition
- Author(s)
- Annie Liang Annie Liang (University of Pennsylvania)
- Xiaosheng Mu Xiaosheng Mu (Columbia University)
- Vasilis Syrgkanis Vasilis Syrgkanis (Microsoft Research)
- Abstract
- A decision-maker (DM) faces an intertemporal decision problem, where his payoff depends on actions taken across time as well as on an unknown Gaussian state. The DM can learn about the state from different (correlated) information sources, and allocates a budget of samples across these sources each period. A simple information acquisition strategy for the DM is to neglect dynamic considerations and allocate samples myopically. How inefficient is this strategy relative to the optimal information acquisition strategy? We show that if the budget of samples is sufficiently large then there is no inefficiency: myopic information acquisition is exactly optimal.
- Creation Date
- 2019-04
- Section URL ID
- Paper Number
- 2019-25
- URL
- https://uploads.strikinglycdn.com/files/9ce89777-bcec-429c-a4e1-b47c7eb903c7/Optimal_myopic.pdf
- File Function
- Jel
- C44, D81, D83
- Keyword(s)
- Information Acquisition, Correlation, Endogenous Attention, Myopic Choice, Robustness, Value of Information
- Suppress
- false
- Series
- 13