Best-of-Both-Worlds Guarantees with Fairer Endings
Telikepalli Kavitha, Surya Panchapakesan, Rohit Vaish, Vignesh Viswanathan, Jatin Yadav

TL;DR
This paper advances fair division algorithms by achieving stronger ex-post fairness guarantees like EFX, while maintaining meaningful ex-ante fairness, across different valuation models using novel algorithms and relaxations.
Contribution
It introduces new algorithms and relaxations for achieving EFX and ex-ante fairness in fair division under various valuation models.
Findings
Efficiently computes ex-post EFX and PO with ex-ante 9/10-EF for lexicographic preferences.
Achieves ex-post EFX-with-charity with ex-ante 0.5-EF for monotone valuations.
Strengthens ex-post fairness to EFX-with-bounded-charity with ex-ante 0.5-proportionality for subadditive valuations.
Abstract
Fair allocation of indivisible goods is a fundamental problem at the interface of economics and computer science. Traditional approaches focus either on randomized allocations that are fair in expectation or deterministic allocations that are approximately fair. Recent work reconciles both these approaches via best-of-both-worlds guarantees, wherein one seeks randomized allocations that are fair in expectation (ex-ante fair) while being supported on approximately fair allocations (ex-post fair). Prior work has shown that under additive valuations, there always exists a randomized allocation that is ex-ante stochastic-dominance envy-free (sd-EF) and ex-post envy-free up to one good (EF1). Our work is motivated by the goal of achieving stronger ex-post fairness guarantees such as envy-freeness up to any good (EFX) along with meaningful ex-ante guarantees. We make the following…
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Taxonomy
TopicsGame Theory and Voting Systems · Auction Theory and Applications · Mobile Crowdsensing and Crowdsourcing
