公平分享:可行性、主导权和激励机制

IF 1.4 3区 数学 Q2 MATHEMATICS, APPLIED
Moshe Babaioff, Uriel Feige
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引用次数: 0

摘要

我们考虑将不可分割的商品公平分配给 n 个权利平等的代理人。每个代理人 i 都有一个估值函数 vi,该函数来自给定的某类估值函数。份额 s 是一个将[公式:见正文]映射为非负值的函数。如果对每个分配实例来说,有一种分配能给每个代理人 i 提供一个对 vi 来说是可接受的捆绑包,其价值至少等于她的份额值[公式:见正文],那么这种份额就是可行的。我们引入以下概念。如果报告真实估值能使就报告而言可接受的价值包的最小真实值最大化,那么份额就是自我最大化的。如果对于每个估值函数[公式:见正文],一个股票 s ρ支配另一个股票[公式:见正文]。我们开始系统地研究可行股份和自我最大化股份,并系统地研究股份间的ρ支配关系,提出了正反两方面的结果:M. Babaioff 的研究得到了 Golda Meir 奖学金的部分资助。U. Feige 的研究部分得到以色列科学基金会 [1122/22 号拨款] 的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fair Shares: Feasibility, Domination, and Incentives
We consider fair allocation of indivisible goods to n equally entitled agents. Every agent i has a valuation function vi from some given class of valuation functions. A share s is a function that maps [Formula: see text] to a nonnegative value. A share is feasible if for every allocation instance, there is an allocation that gives every agent i a bundle that is acceptable with respect to vi, one of value at least her share value [Formula: see text]. We introduce the following concepts. A share is self-maximizing if reporting the true valuation maximizes the minimum true value of a bundle that is acceptable with respect to the report. A share s ρ-dominates another share [Formula: see text] if [Formula: see text] for every valuation function. We initiate a systematic study of feasible and self-maximizing shares and a systematic study of ρ-domination relation between shares, presenting both positive and negative results.Funding: The research of M. Babaioff is supported in part by a Golda Meir Fellowship. The research of U. Feige is supported in part by the Israel Science Foundation [Grant 1122/22].
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来源期刊
Mathematics of Operations Research
Mathematics of Operations Research 管理科学-应用数学
CiteScore
3.40
自引率
5.90%
发文量
178
审稿时长
15.0 months
期刊介绍: Mathematics of Operations Research is an international journal of the Institute for Operations Research and the Management Sciences (INFORMS). The journal invites articles concerned with the mathematical and computational foundations in the areas of continuous, discrete, and stochastic optimization; mathematical programming; dynamic programming; stochastic processes; stochastic models; simulation methodology; control and adaptation; networks; game theory; and decision theory. Also sought are contributions to learning theory and machine learning that have special relevance to decision making, operations research, and management science. The emphasis is on originality, quality, and importance; correctness alone is not sufficient. Significant developments in operations research and management science not having substantial mathematical interest should be directed to other journals such as Management Science or Operations Research.
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