Content area

Abstract

In repeated principal‐agent problems and games, more outcomes are implementable when performance signals are privately observed by a principal or mediator with commitment power than when the same signals are publicly observed and form the basis of a recursive equilibrium. We investigate the gains from nonrecursive equilibria (e.g., “review strategies”) based on privately observed signals. Under a pairwise identification condition, we find that the gains from nonrecursive equilibria are “small”: their inefficiency is of the same 1 − δ power order as that of recursive equilibria. Thus, while private strategies or monitoring can outperform public ones for a fixed discount factor, they cannot accelerate the power rate of convergence to the efficient payoff frontier when the folk theorem holds. An implication is that the gains from withholding performance feedback from agents are small when the parties are patient.

Details

1009240
Business indexing term
Title
Nonrecursive dynamic incentives: A rate of convergence approach
Author
Sugaya, Takuo 1 ; Wolitzky, Alexander 2 

 Stanford GSB, 
 Department of Economics, MIT, 
Publication title
Volume
20
Issue
4
Pages
1461-1520
Number of pages
61
Publication year
2025
Publication date
Nov 1, 2025
Section
Original Articles
Publisher
John Wiley & Sons, Inc.
Place of publication
New York
Country of publication
United States
ISSN
19336837
e-ISSN
15557561
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-11-27
Milestone dates
2025-03-07 (manuscriptRevised); 2025-11-27 (publishedOnlineFinalForm); 2024-09-11 (manuscriptReceived); 2025-03-11 (manuscriptAccepted)
Publication history
 
 
   First posting date
27 Nov 2025
ProQuest document ID
3276159145
Document URL
https://www.proquest.com/scholarly-journals/nonrecursive-dynamic-incentives-rate-convergence/docview/3276159145/se-2?accountid=208611
Copyright
Copyright John Wiley & Sons, Inc. 2025
Last updated
2025-11-28
Database
ProQuest One Academic