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Randomization and the Robustness of Linear Contracts

Author

Listed:
  • Ashwin Kambhampati

    (United States Naval Academy)

  • Bo Peng

    (Shanghai University of Finance and Economics)

  • Zhihao Gavin Tang

    (Shanghai University of Finance and Economics)

  • Juuso Toikka

    (University of Pennsylvania)

  • Rakesh Vohra

    (University of Pennsylvania)

Abstract

We consider a principal-agent model with moral hazard, bilateral risk-neutrality, and limited liability. The principal knows only some of the actions the agent can take and evaluates contracts by their guaranteed payoff over possible unknown actions. We show that linear contracts are a robustly optimal way to incentivize the agent: any randomization over contracts can be improved by making each contract in its support linear. We then identify an optimal random linear contract characterized by a single parameter that bounds its continuous support. Several corollaries arise: the gain from randomization can be arbitrarily large; optimal randomization does not require commitment; and screening cannot improve the principal’s guarantee.

Suggested Citation

  • Ashwin Kambhampati & Bo Peng & Zhihao Gavin Tang & Juuso Toikka & Rakesh Vohra, 2025. "Randomization and the Robustness of Linear Contracts," PIER Working Paper Archive 25-004, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:25-004
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