Emergent Mind

Fair and Truthful Mechanisms for Dichotomous Valuations

(2002.10704)
Published Feb 25, 2020 in cs.GT

Abstract

We consider the problem of allocating a set on indivisible items to players with private preferences in an efficient and fair way. We focus on valuations that have dichotomous marginals, in which the added value of any item to a set is either 0 or 1, and aim to design truthful allocation mechanisms (without money) that maximize welfare and are fair. For the case that players have submodular valuations with dichotomous marginals, we design such a deterministic truthful allocation mechanism. The allocation output by our mechanism is Lorenz dominating, and consequently satisfies many desired fairness properties, such as being envy-free up to any item (EFX), and maximizing the Nash Social Welfare (NSW). We then show that our mechanism with random priorities is envy-free ex-ante, while having all the above properties ex-post. Furthermore, we present several impossibility results precluding similar results for the larger class of XOS valuations. To gauge the robustness of our positive results, we also study $\epsilon$-dichotomous valuations, in which the added value of any item to a set is either non-positive, or in the range $[1, 1 + \epsilon]$. We show several impossibility results in this setting, and also a positive result: for players that have additive $\epsilon$-dichotomous valuations with sufficiently small $\epsilon$, we design a randomized truthful mechanism with strong ex-post guarantees. For $\rho = \frac{1}{1 + \epsilon}$, the allocations that it produces generate at least a $\rho$-fraction of the maximum welfare, and enjoy $\rho$-approximations for various fairness properties, such as being envy-free up to one item (EF1), and giving each player at least her maximin share.

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