The Degree of (Extended) Justified Representation and Its Optimization
Biaoshuai Tao, Chengkai Zhang, Houyu Zhou

TL;DR
This paper introduces the concept of optimizing the degree of (Extended) Justified Representation in multiwinner approval voting, analyzing its computational complexity, approximation algorithms, and fixed-parameter tractability.
Contribution
It defines the optimization problems extMDJR and extMDEJR for maximizing (E)JR degree, and studies their complexity, approximability, and fixed-parameter tractability.
Findings
Polynomial-time algorithms provide basic approximation guarantees.
Finding optimal (E)JR committees is NP-hard to approximate within certain factors.
Both problems are W[2]-hard when parameterized by committee size, but fixed-parameter tractable with additional parameters.
Abstract
Justified Representation (JR)/Extended Justified Representation (EJR) is a desirable axiom in multiwinner approval voting. In contrast to that (E)JR only requires at least \emph{one} voter to be represented in every cohesive group, we study its optimization version that maximizes the \emph{number} of represented voters in each group. Given an instance, we say a winning committee provides a JR degree (EJR degree, resp.) of if at least voters in each -cohesive group (-cohesive group, resp.) have approved (, resp.) winning candidates. Hence, every (E)JR committee provides the (E)JR degree of at least . Besides proposing this new property, we propose the optimization problem of finding a winning committee that achieves the maximum possible (E)JR degree, called \MDJR and \MDEJR, corresponding to JR and EJR respectively. We study the computational complexity…
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Epistemology, Ethics, and Metaphysics · Computability, Logic, AI Algorithms
