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.
JEL codes
- D42: Monopoly
- D81: Criteria for Decision-Making under Risk and Uncertainty
- D82: Asymmetric and Private Information • Mechanism Design
Reference
Dirk Bergemann, Alex Smolin, and Alessandro Bonatti, “The Design and Price of Information”, American Economic Review, vol. 108, n. 1, 2018, pp. 1–48.
Published in
American Economic Review, vol. 108, n. 1, 2018, pp. 1–48