CFDP 2049R
Author(s): Dirk BergemannAlessandro BonattiAlex Smolin
Publication Date: July 2016
Revision Date: June 2017
Pages: 62
Abstract:
A data buyer faces a decision problem under uncertainty. He can augment his initial private information with supplemental data from a data seller. His willingness to pay for supplemental data is determined by the quality of his initial private information. The data seller optimally offers a menu of statistical experiments. We establish the properties that any revenue-maximizing menu of experiments must satisfy. Every experiment is a non-dispersed stochastic matrix, and every menu contains a fully informative experiment. In the cases of binary states and actions, or binary types, we provide an explicit construction of the optimal menu of experiments.
Keywords:
Information design, Price of information, Statistical experiments, Mechanism design, Price discrimination, Hypothesis testing.
JEL Classification Codes: D42, D82, D83