
TL;DR
This paper proves that linear contracts are optimal under distributional ambiguity in principal-agent problems with multiple tasks, offering a robust justification for their widespread use.
Contribution
It introduces a distributional robustness framework showing linear contracts outperform all others in worst-case scenarios, with new proof techniques and extensions to multi-party settings.
Findings
Linear contracts maximize worst-case payoff under distributional ambiguity.
Existence of self-inducing actions supports the optimality of linear contracts.
Affine contracts improve all principals' worst-case payoffs in multi-principal settings.
Abstract
Linear contracts are ubiquitous in practice, yet optimal contract theory often prescribes complex, nonlinear structures. We provide a distributional robustness justification for linear contracts. We study a principal-agent problem where the agent exerts costly effort across multiple tasks, generating a stochastic signal upon which the principal conditions payment. The principal faces distributional ambiguity: she knows the expected signal for each effort level, but not the full distribution. She seeks a contract maximizing her worst-case payoff over all distributions consistent with this partial knowledge. Our main result shows that linear contracts are optimal for such a principal. For any contract, there exists a linear contract achieving weakly higher worst-case payoff. The proof introduces the concavification approach built around the notion of self-inducing actions; these are…
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