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Optimal Robust Contract Design

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  • Bo Peng
  • Zhihao Gavin Tang

Abstract

We consider the robust contract design problem when the principal only has limited information about the actions the agent can take. The principal evaluates a contract according to its worst-case performance caused by the uncertain action space. Carroll (AER 2015) showed that a linear contract is optimal among deterministic contracts. Recently, Kambhampati (JET 2023) showed that the principal's payoff can be strictly increased via randomization over linear contracts. In this paper, we characterize the optimal randomized contract, which remains linear and admits a closed form of its cumulative density function. The advantage of randomized contracts over deterministic contracts can be arbitrarily large even when the principal knows only one non-trivial action of the agent. Furthermore, our result generalizes to the model of contracting with teams, by Dai and Toikka (Econometrica 2022).

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  • Bo Peng & Zhihao Gavin Tang, 2024. "Optimal Robust Contract Design," Papers 2406.11528, arXiv.org.
    • Handle: RePEc:arx:papers:2406.11528
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    References listed on IDEAS

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    1. Constantinos Daskalakis & Alan Deckelbaum & Christos Tzamos, 2017. "Strong Duality for a Multiple‐Good Monopolist," Econometrica, Econometric Society, vol. 85, pages 735-767, May.
    2. Tianjiao Dai & Juuso Toikka, 2022. "Robust Incentives for Teams," Econometrica, Econometric Society, vol. 90(4), pages 1583-1613, July.
    3. Gabriel Carroll, 2015. "Robustness and Linear Contracts," American Economic Review, American Economic Association, vol. 105(2), pages 536-563, February.
    4. Gabriel Carroll, 2017. "Robustness and Separation in Multidimensional Screening," Econometrica, Econometric Society, vol. 85, pages 453-488, March.
    5. Kambhampati, Ashwin, 2023. "Randomization is optimal in the robust principal-agent problem," Journal of Economic Theory, Elsevier, vol. 207(C).
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