Incentivizing Information Design

51 Pages Posted: 21 Jul 2017 Last revised: 26 Nov 2017

See all articles by Daniel Rappoport

Daniel Rappoport

University of Chicago - Booth School of Business - Economics

Valentin Somma

Columbia University, Graduate School of Arts and Sciences, Department of Economics

Date Written: August 21, 2017

Abstract

A principal hires an agent to acquire costly information that will influence the decision of a third party. While the realized piece of information is observable and contractible, the experimental process is not. Assuming a general family of information cost functions (inclusive of Shannon’s mutual information), we show that the first best is achievable when the agent has limited liability or when he is risk averse, in contrast to standard moral hazard models. However, when the agent is risk averse and has limited liability, efficiency losses arise generically. Specifically, we show that the principal obtains his first best outcome if and only if she intends to implement a ”symmetric” experiment, i.e. one in which the cost of generating each piece of evidence is the same. On the other hand, ”asymmetric” experiments that are relatively uninformative with high probability but occasionally produce conclusive evidence will bear large agency costs.

Keywords: Bayesian Persuasion, Costly Information Acquisition, Moral Hazard

JEL Classification: D82, D83

Suggested Citation

Rappoport, Daniel and Somma, Valentin, Incentivizing Information Design (August 21, 2017). Available at SSRN: https://ssrn.com/abstract=3001416 or http://dx.doi.org/10.2139/ssrn.3001416

Daniel Rappoport (Contact Author)

University of Chicago - Booth School of Business - Economics ( email )

5807 S Woodlawn Ave
Chicago, IL 60637

Valentin Somma

Columbia University, Graduate School of Arts and Sciences, Department of Economics ( email )

New York, NY
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
546
Abstract Views
2,347
Rank
94,094
PlumX Metrics