Learning to game the system

Jin Li, Arijit Mukherjee, Luis Vasconcelos

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

An agent may privately learn which aspects of his job are more important by shirking on some of them, and use that information to shirk more effectively in the future. In a model of long-term employment relationship, we characterize the optimal relational contract in the presence of such learning-by-shirking and highlight how the performance measurement system can be managed to sharpen incentives. Two related policies are studied: intermittent replacement of existing measures, and adoption of new ones. In spite of the learning-by-shirking effect, the optimal contract is stationary, and may involve stochastic replacement/adoption policies that dilute the agent’s information rents from learning how to game the system.
Original languageEnglish
Pages (from-to)2014-2041
JournalReview of Economic Studies
Volume88
Issue number4
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Performance evaluation systems
  • Learning
  • Gaming
  • Relational contracts

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