A unified framework for strong price of anarchy in clustering games

Michal Feldman, Ophir Friedler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We devise a unified framework for quantifying the inefficiency of equilibria in clustering games on networks. This class of games has two properties exhibited by many real-life social and economic settings: (a) an agent’s utility is affected only by the behavior of her direct neighbors rather than that of the entire society, and (b) an agent’s utility does not depend on the actual strategies chosen by agents, but rather by whether or not other agents selected the same strategy. Our framework is sufficiently general to account for unilateral versus coordinated deviations by coalitions of different sizes, different types of relationships between agents, and different structures of strategy spaces. Many settings that have been recently studied are special cases of clustering games on networks. Using our framework: (1) We recover previous results for special cases and provide extended and improved results in a unified way. (2) We identify new settings that fall into the class of clustering games on networks and establish price of anarchy and strong price of anarchy bounds for them.

Original languageEnglish
Title of host publicationAutomata, Languages, and Programming - 42nd International Colloquium, ICALP 2015, Proceedings
EditorsNaoki Kobayashi, Bettina Speckmann, Kazuo Iwama, Magnus M. Halldorsson
Pages601-613
Number of pages13
DOIs
StatePublished - 2015
Event42nd International Colloquium on Automata, Languages and Programming, ICALP 2015 - Kyoto, Japan
Duration: 6 Jul 201510 Jul 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9135

Conference

Conference42nd International Colloquium on Automata, Languages and Programming, ICALP 2015
Country/TerritoryJapan
CityKyoto
Period6/07/1510/07/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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