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Learning to game the system
[Toward a Theory of Discounted Repeated Games with Imperfect Monitoring]

Author

Listed:
  • Jin Li
  • Arijit Mukherjee
  • Luis Vasconcelos

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.

Suggested Citation

  • Jin Li & Arijit Mukherjee & Luis Vasconcelos, 2021. "Learning to game the system [Toward a Theory of Discounted Repeated Games with Imperfect Monitoring]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(4), pages 2014-2041.
    • Handle: RePEc:oup:restud:v:88:y:2021:i:4:p:2014-2041.
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    File URL: http://hdl.handle.net/10.1093/restud/rdaa065
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    Citations

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    Cited by:

    1. Iván Marinovic & Martin Szydlowski, 2022. "Monitoring with career concerns," RAND Journal of Economics, RAND Corporation, vol. 53(2), pages 404-428, June.
    2. Johannes Abeler & David Huffman & Collin Raymond & David B. Huffman, 2023. "Incentive Complexity, Bounded Rationality and Effort Provision," CESifo Working Paper Series 10541, CESifo.
    3. Abeler, Johannes & Huffman, David B. & Raymond, Collin, 2023. "Incentive Complexity, Bounded Rationality and Effort Provision," IZA Discussion Papers 16284, Institute of Labor Economics (IZA).

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