Learning to game the system

• Jin Li ^[1] ; Arijit Mukherjee ^[2] ; Luis Vasconcelos ^[3]
  1. [1] University of Hong Kong

     University of Hong Kong

     RAE de Hong Kong (China)

     
  2. [2] Michigan State University

     Michigan State University

     City of East Lansing, Estados Unidos

     
  3. [3] University of Technology

     University of Technology

     Rusia

     

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• Localización: Review of economic studies, ISSN 0034-6527, Vol. 88, Nº 4, 2021, págs. 2014-2041
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.