Optimal and Myopic Information Acquisition

Annie Liang, Xiaosheng Mu, and Vasilis Syrgkanis

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.

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BibTeX

@inproceedings{liang2018optimal,
  author = {Annie Liang and Xiaosheng Mu and Vasilis Syrgkanis},
  title = {Optimal and Myopic Information Acquisition},
  booktitle = {Proceedings of the 2018 ACM Conference on Economics and Computation},
  year = {2018}
}