Simple versus Optimal Contracts

Simple versus Optimal Contracts
复制标题

简单合约与最优合约

DOI:
10.1145/3328526.3329591
复制
发表时间:
2019
期刊:
ACM EC
影响因子:
--
通讯作者:
Talgam-Cohen, Inbal
Talgam-Cohen, Inbal
中科院分区:
--
文献类型:
--
作者:
Dütting, Paul;Roughgarden, Tim;Talgam-Cohen, Inbal

文献摘要

参考文献

被引文献

相似文献

我们考虑经典的委托代理模型的合同理论,其中一个主要的设计结果依赖的补偿计划,以激励代理人采取昂贵的和不可观察的行动。当所有的模型参数-包括全部分布的主要奖励所产生的每个代理人的行动-是已知的设计者,最优的合同原则上可以计算线性规划。然而,除了它们苛刻的信息要求之外,这种最优契约通常是复杂和不直观的,并且不类似于实践中使用的契约。本文通过理论计算机科学的透镜来考察契约理论,目的是发展新的理论来解释和证明相对简单的契约的流行,如线性(纯佣金)契约。首先,我们考虑的情况下,主要只知道每个动作的奖励分布的第一时刻,我们证明了线性合同保证是最坏情况下的最优,在所有的奖励分布与给定的时刻一致。其次,我们从最坏情况近似的角度研究了线性合约,并证明了几个紧的参数化近似界。
We consider the classic principal-agent model of contract theory, in which a principal designs an outcome-dependent compensation scheme to incentivize an agent to take a costly and unobservable action. When all of the model parameters---including the full distribution over principal rewards resulting from each agent action---are known to the designer, an optimal contract can in principle be computed by linear programming. In addition to their demanding informational requirements, however, such optimal contracts are often complex and unintuitive, and do not resemble contracts used in practice. This paper examines contract theory through the theoretical computer science lens, with the goal of developing novel theory to explain and justify the prevalence of relatively simple contracts, such as linear (pure commission) contracts. First, we consider the case where the principal knows only the first moment of each action's reward distribution, and we prove that linear contracts are guaranteed to be worst-case optimal, ranging over all reward distributions consistent with the given moments. Second, we study linear contracts from a worst-case approximation perspective, and prove several tight parameterized approximation bounds.
DOI: 10.1214/aop/1176993150
发表时间: 1984-11
影响因子: 2.3
作者:
E. Samuel-Cahn
通讯作者: E. Samuel-Cahn
拍卖设计中的约束信号
DOI: 10.1137/1.9781611973402.99
发表时间: 2013
期刊: Proceedings of the fifteenth ACM conference on Economics and computation
影响因子: --
作者:
S. Dughmi;Nicole Immorlica;Aaron Roth
通讯作者: Aaron Roth
分类器如何引导智能体进行战略性投入?
DOI: --
发表时间: 2018
期刊: ACM Conference on Economics and Computation
影响因子: --
作者:
J. Kleinberg;Manish Raghavan
通讯作者: Manish Raghavan
DOI: 10.1137/1.9781611974331.ch72
发表时间: 2015-08
期刊: --
影响因子: --
作者:
Moran Feldman;O. Svensson;R. Zenklusen
通讯作者: Moran Feldman;O. Svensson;R. Zenklusen
DOI: --
发表时间: 2017
期刊: SeCO Workshops
影响因子: --
作者:
S. Dughmi
通讯作者: S. Dughmi