Relaxing Common Belief for Social Networks

Authors Noah Burrell , Grant Schoenebeck

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Author Details

Noah Burrell
  • University of Michigan, Ann Arbor, MI, USA
Grant Schoenebeck
  • University of Michigan, Ann Arbor, MI, USA

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Noah Burrell and Grant Schoenebeck. Relaxing Common Belief for Social Networks. In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 185, pp. 45:1-45:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


We propose a relaxation of common belief called factional belief that is suitable for the analysis of strategic coordination on social networks. We show how this definition can be used to analyze revolt games on general graphs, including by giving an efficient algorithm that characterizes a structural result about the possible equilibria of such games. This extends prior work on common knowledge and common belief, which has been too restrictive for use in understanding strategic coordination and cooperation in social network settings.

Subject Classification

ACM Subject Classification
  • Theory of computation → Social networks
  • Theory of computation → Network games
  • Theory of computation → Algorithmic game theory
  • Social networks
  • network revolt games
  • common belief


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