Work

Expected Maximin Fairness in Max-Cut and other Combinatorial Optimization Problems

Year: 2024

Type: preprint

Abstract: Maximin fairness is the ideal that the worst-off group (or individual) should be treated as well as possible. Literature on maximin fairness in various decision-making settings has grown in recent years, but theoretical results are sparse. In this paper, we explore the challenges inherent to maximin fairness in combinatorial optimization. We begin by showing that (1) optimal maximin-fair solutions are bounded by non-maximin-fair optimal solutions, and (2) stochastic maximin-fair solutions exceed their deterministic counterparts in expectation for a broad class of combinatorial optimization problems. In the remainder of the paper, we use the special case of Max-Cut to demonstrate challenges in defining and implementing maximin fairness.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2410.02589

Authors: Salem, Jad, Tate, Reuben, Eidenbenz, Stephan

Institutions: United States Naval Academy, Los Alamos National Laboratory

Language: English

Topic: Game Theory and Voting Systems

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

Funders: U.S. Department of Energy, National Nuclear Security Administration, Los Alamos National Laboratory

Awards: G1017065088, G1279347439, G1870943713, G6410514744, G6563880843

OpenAlex ID: W4403884089

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