Fair Shares: Feasibility, Domination and Incentives

Moshe Babaioff, U. Feige
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引用次数: 6

Abstract

We consider fair allocation of a set M of indivisible goods to n equally-entitled agents, with no monetary transfers. Every agent i has a valuation function vi from some given class of valuation functions. A share s is a function that maps a pair (vi,n) to a non-negative value, with the interpretation that if an allocation of M to n agents fails to give agent i a bundle of value at least equal to s(vi,n), this serves as evidence that the allocation is not fair towards i. For such an interpretation to make sense, we would like the share to be feasible, meaning that for any valuations in the class, there is an allocation that gives every agent at least her share. The maximin share (MMS) was a natural candidate for a feasible share for additive valuations. However, Kurokawa, Procaccia and Wang [2018] show that it is not feasible. We initiate a systematic study of the family of feasible shares. We say that a share is self maximizing if truth-telling maximizes the implied guarantee (the worse true value of any bundle that gives the share with respect to the report). We show that every feasible share is dominated by some self-maximizing and feasible share. We seek to identify those self-maximizing feasible shares that are polynomial time computable, and offer the highest share values. We show that a SM-dominating feasible share -- one that dominates every self-maximizing (SM) feasible share -- does not exist for additive valuations (and beyond). Consequently, we relax the domination property to that of domination up to a multiplicative factor of ρ (called ρ-dominating ). For additive valuations we present shares that are feasible, self-maximizing and polynomial-time computable. For n agents we present such a share that is 2n/3n-1-dominating, and is 4/5-dominating when n ≤ 4. For two agents we present such a share that is (1 - ε)-dominating. Moreover, for each of these shares we present a polynomial time algorithm that computes allocations that give every agent at least her share.
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公平份额:可行性、支配和激励
我们考虑在没有货币转移的情况下,将一组M个不可分割商品公平分配给n个权利平等的代理人。每个智能体i都有一个估值函数vi,它来自某一类给定的估值函数。每股s是一个函数,一对(vi, n)映射到一个非负价值,解释,如果一个M, n分配代理未能给代理我一束值至少等于(vi, n),这是证明分配对我是不公平的。这样的解释是有意义的,我们希望分享是可行的,也就是说,对于任何估值班上,有一个分配给每个代理至少她分享。最大份额(MMS)是可加性估值的可行份额的自然候选。然而,Kurokawa、Procaccia和Wang[2018]表明,这是不可行的。我们对可行股份族进行了系统的研究。我们说,如果说真话使隐含保证最大化(与报告相比,给出该股票的任何捆绑包的最差真实价值),那么该股票就是自我最大化的。我们证明了每一个可行份额都被一些自我最大化的可行份额所支配。我们寻求识别那些自我最大化可行的股份是多项式时间可计算的,并提供最高的股份价值。我们证明了一个SM支配的可行份额——一个支配每一个自我最大化(SM)可行份额的可行份额——对于加性估值(及其他)并不存在。因此,我们将支配性质放宽为支配性质,直至ρ的一个乘法因子(称为ρ-支配)。对于加性估值,我们给出了可行的、自最大化的和多项式时间可计算的股份。对于n个agent,我们给出了这样一个份额,即2n/3n-1占主导地位,当n≤4时占4/5主导地位。对于两个代理,我们给出了(1 - ε)占主导地位的份额。此外,对于这些份额中的每一个,我们提出了一个多项式时间算法,该算法计算分配,使每个代理至少获得她的份额。
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