Parameterized Guarantees for Almost Envy-Free Allocations
Siddharth Barman, Debajyoti Kar, and Shraddha Pathak

TL;DR
This paper introduces new algorithms with approximation guarantees for fair division of indivisible goods, focusing on envy-freeness and maximin share notions, parameterized by the value range of goods across agents.
Contribution
It provides the first polynomial-time algorithms with improved approximation guarantees for EFx, PMMS, and tEFx fairness notions, based on an instance-dependent parameter.
Findings
Achieves a better than 0.618 approximation for EFx when $oldsymbol{ extit{ extgamma}} oldsymbol{ extgreater} 0.511$.
Provides a $rac{5}{6}oldsymbol{ extit{ extgamma}}$-approximate PMMS allocation algorithm.
Develops a $2oldsymbol{ extit{ extgamma}}$-approximate tEFx allocation algorithm.
Abstract
We study fair allocation of indivisible goods among agents with additive valuations. We obtain novel approximation guarantees for three of the strongest fairness notions in discrete fair division, namely envy-free up to the removal of any positively-valued good (EFx), pairwise maximin shares (PMMS), and envy-free up to the transfer of any positively-valued good (tEFx). Our approximation guarantees are in terms of an instance-dependent parameter that upper bounds, for each indivisible good in the given instance, the multiplicative range of nonzero values for the good across the agents. First, we consider allocations wherein, between any pair of agents and up to the removal of any positively-valued good, the envy is multiplicatively bounded. Specifically, the current work develops a polynomial-time algorithm that computes a $\left(…
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Taxonomy
TopicsGame Theory and Voting Systems · Economic theories and models · Decision-Making and Behavioral Economics
