Approximate Optimality of Linear Contracts Under Uncertainty

Fuente: arXiv
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Autores principales: Alon, Tal, Dütting, Paul, Li, Yingkai, Talgam-Cohen, Inbal
Formato: Preprint
Publicado: 2022
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author Alon, Tal
Dütting, Paul
Li, Yingkai
Talgam-Cohen, Inbal
author_facet Alon, Tal
Dütting, Paul
Li, Yingkai
Talgam-Cohen, Inbal
contents We consider a hidden-action principal-agent model, in which actions require different amounts of effort, and the agent privately knows his ability that determines his cost of effort. We show that linear contracts admit approximation guarantees that improve with a natural metric that captures the degree of uncertainty in the contracting setting. We thus show that linear contracts are near-optimal whenever there is enough uncertainty. In contrast, other simple contract formats such as debt contracts may suffer from a loss linear in the number of possible actions, even when there is sufficient uncertainty.
format Preprint
id arxiv_https___arxiv_org_abs_2211_06850
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Approximate Optimality of Linear Contracts Under Uncertainty
Alon, Tal
Dütting, Paul
Li, Yingkai
Talgam-Cohen, Inbal
Computer Science and Game Theory
Theoretical Economics
We consider a hidden-action principal-agent model, in which actions require different amounts of effort, and the agent privately knows his ability that determines his cost of effort. We show that linear contracts admit approximation guarantees that improve with a natural metric that captures the degree of uncertainty in the contracting setting. We thus show that linear contracts are near-optimal whenever there is enough uncertainty. In contrast, other simple contract formats such as debt contracts may suffer from a loss linear in the number of possible actions, even when there is sufficient uncertainty.
title Approximate Optimality of Linear Contracts Under Uncertainty
topic Computer Science and Game Theory
Theoretical Economics
url https://arxiv.org/abs/2211.06850