Proportional Rankings

December 05, 2016 ยท Declared Dead ยท ๐Ÿ› International Joint Conference on Artificial Intelligence

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Authors Piotr Skowron, Martin Lackner, Markus Brill, Dominik Peters, Edith Elkind arXiv ID 1612.01434 Category cs.GT: Game Theory Cross-listed cs.AI, cs.MA Citations 67 Venue International Joint Conference on Artificial Intelligence Last Checked 3 months ago
Abstract
In this paper we extend the principle of proportional representation to rankings. We consider the setting where alternatives need to be ranked based on approval preferences. In this setting, proportional representation requires that cohesive groups of voters are represented proportionally in each initial segment of the ranking. Proportional rankings are desirable in situations where initial segments of different lengths may be relevant, e.g., hiring decisions (if it is unclear how many positions are to be filled), the presentation of competing proposals on a liquid democracy platform (if it is unclear how many proposals participants are taking into consideration), or recommender systems (if a ranking has to accommodate different user types). We study the proportional representation provided by several ranking methods and prove theoretical guarantees. Furthermore, we experimentally evaluate these methods and present preliminary evidence as to which methods are most suitable for producing proportional rankings.
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