Dominant Resource Fairness with Meta-Types
Steven Yin, Shatian Wang, Lingyi Zhang, Christian Kroer

TL;DR
This paper introduces a novel resource allocation mechanism called Dominant Resource Fairness with Meta Types, which handles heterogeneous demands, location constraints, and substitution effects, ensuring fairness and efficiency in complex real-world scenarios.
Contribution
It presents the first study of resource allocation with meta-types, incorporating constraints like location and substitution, and provides a scalable, fair, and strategy-proof solution.
Findings
Mechanism satisfies Pareto optimality, envy-freeness, strategy-proofness.
Method scales better to large problems than alternatives.
Captures real-life constraints more effectively than previous models.
Abstract
Inspired by the recent COVID-19 pandemic, we study a generalization of the multi-resource allocation problem with heterogeneous demands and Leontief utilities. Unlike existing settings, we allow each agent to specify requirements to only accept allocations from a subset of the total supply for each resource. These requirements can take form in location constraints (e.g. A hospital can only accept volunteers who live nearby due to commute limitations). This can also model a type of substitution effect where some agents need 1 unit of resource A \emph{or} B, both belonging to the same meta-type. But some agents specifically want A, and others specifically want B. We propose a new mechanism called Dominant Resource Fairness with Meta Types which determines the allocations by solving a small number of linear programs. The proposed method satisfies Pareto optimality, envy-freeness,…
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Taxonomy
TopicsGame Theory and Voting Systems · Risk and Portfolio Optimization · Auction Theory and Applications
