Simple versus Optimal Contracts

Dütting, Paul; Roughgarden, Tim; Talgam-Cohen, Inbal

doi:10.1145/3328526.3329591

Cited by 46 publications

(108 citation statements)

References 46 publications

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“…Before we proceed to our results, we give a brief sketch of analogous results for the principal-agent problem without types. Many of these results appear in [8], which was one of the first papers to study contracts from an algorithmic/computational perspective.…”

Section: Our Resultsmentioning

confidence: 99%

Contracts under Moral Hazard and Adverse Selection

Guruganesh

Schneider

Wang

2021

Proceedings of the 22nd ACM Conference on Economics and Computation

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In the classical principal-agent problem, a principal must design a contract to incentivize an agent to perform an action on behalf of the principal. We study the classical principal-agent problem in a setting where the agent can be of one of several types (affecting the outcome of actions they might take). This combines the contract theory phenomena of "moral hazard" (incomplete information about actions) with that of "adverse selection" (incomplete information about types).We examine this problem through the computational lens. We show that in this setting it is APX-hard to compute either the profit-maximizing single contract or the profit-maximizing menu of contracts (as opposed to in the absence of types, where one can efficiently compute the optimal contract). We then show that the performance of the best linear contract scales especially well in the number of types: if agent has available actions and possible types, the best linear contract achieves an ( log ) approximation of the best possible profit. Finally, we apply our framework to prove tight worst-case approximation bounds between a variety of benchmarks of mechanisms for the principal.CCS Concepts: • Theory of computation → Algorithmic game theory; Algorithmic mechanism design.

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Section: Our Resultsmentioning

confidence: 99%

Contracts under Moral Hazard and Adverse Selection

Guruganesh

Schneider

Wang

2021

Proceedings of the 22nd ACM Conference on Economics and Computation

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“…The optimal contract for incentivizing the costly action in a 2-action setting is well-understood (see e.g. [23]): The only positive payment should be for the single subset of items maximizing the likelihood that the agent has chosen action a 2 ; in our case this is the subset {2} containing item 2 only. Observe that its probability given action 1 is Proof of Claim A.2.…”

Section: A3 Connections Between Ic and δ-Ic Contractsmentioning

confidence: 99%

The Complexity of Contracts

Dütting¹,

Roughgarden²,

Cohen³

2020

Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms

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We initiate the study of computing (near-)optimal contracts in succinctly representable principal-agent settings. Here optimality means maximizing the principal's expected payoff over all incentive-compatible contracts-known in economics as "second-best" solutions. We also study a natural relaxation to approximately incentive-compatible contracts.We focus on principal-agent settings with succinctly described (and exponentially large) outcome spaces. We show that the computational complexity of computing a near-optimal contract depends fundamentally on the number of agent actions. For settings with a constant number of actions, we present a fully polynomial-time approximation scheme (FPTAS) for the separation oracle of the dual of the problem of minimizing the principal's payment to the agent, and use this subroutine to efficiently compute a δ-incentive-compatible (δ-IC) contract whose expected payoff matches or surpasses that of the optimal IC contract.With an arbitrary number of actions, we prove that the problem is hard to approximate within any constant c. This inapproximability result holds even for δ-IC contracts where δ is a sufficiently rapidly-decaying function of c. On the positive side, we show that simple linear δ-IC contracts with constant δ are sufficient to achieve a constant-factor approximation of the "firstbest" (full-welfare-extracting) solution, and that such a contract can be computed in polynomial time.

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“…A second line of research closely related to our work is contract design, a branch of microeconomics (Grossman and Hart 1983) that has recently gained interest in computer science (Babaioff, Feldman, and Nisan 2006;Dütting, Roughgarden, and Talgam-Cohen 2019). The precise relation between our model and the classic principal-agent, hidden-action 1 model from microeconomics is explained in Appendix A.…”

Section: Related Workmentioning

confidence: 99%

Multiagent Evaluation Mechanisms

Dobson

Procaccia

et al. 2020

AAAI

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We consider settings where agents are evaluated based on observed features, and assume they seek to achieve feature values that bring about good evaluations. Our goal is to craft evaluation mechanisms that incentivize the agents to invest effort in desirable actions; a notable application is the design of course grading schemes. Previous work has studied this problem in the case of a single agent. By contrast, we investigate the general, multi-agent model, and provide a complete characterization of its computational complexity.

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Simple versus Optimal Contracts

Cited by 46 publications

References 46 publications

Contracts under Moral Hazard and Adverse Selection

Contracts under Moral Hazard and Adverse Selection

The Complexity of Contracts

Multiagent Evaluation Mechanisms

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