A Multi-agent Model for Polarization Under Confirmation Bias in Social Networks

Mário S. Alvim, Bernardo Amorim, Sophia Knight, Santiago Quintero, Frank Valencia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe a model for polarization in multi-agent systems based on Esteban and Ray’s standard measure of polarization from economics. Agents evolve by updating their beliefs (opinions) based on an underlying influence graph, as in the standard DeGroot model for social learning, but under a confirmation bias; i.e., a discounting of opinions of agents with dissimilar views. We show that even under this bias polarization eventually vanishes (converges to zero) if the influence graph is strongly-connected. If the influence graph is a regular symmetric circulation, we determine the unique belief value to which all agents converge. Our more insightful result establishes that, under some natural assumptions, if polarization does not eventually vanish then either there is a disconnected subgroup of agents, or some agent influences others more than she is influenced. We also show that polarization does not necessarily vanish in weakly-connected graphs under confirmation bias. We illustrate our model with a series of case studies and simulations, and show how it relates to the classic DeGroot model for social learning.

Original languageEnglish (US)
Title of host publicationFormal Techniques for Distributed Objects, Components, and Systems - 41st IFIP WG 6.1 International Conference, FORTE 2021, Held as Part of the 16th International Federated Conference on Distributed Computing Techniques, DisCoTec 2021, Proceedings
EditorsKirstin Peters, Tim A. Willemse
PublisherSpringer Science and Business Media Deutschland GmbH
Pages22-41
Number of pages20
ISBN (Print)9783030780883
DOIs
StatePublished - Jun 8 2021
Externally publishedYes
Event41st IFIP WG 6.1 International Conference on Formal Techniques for Distributed Objects, Components, and Systems, FORTE 2021 held as part of 16th International Federated Conference on Distributed Computing Techniques, DisCoTec 2021 - Virtual, Online
Duration: Jun 14 2021Jun 18 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12719 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference41st IFIP WG 6.1 International Conference on Formal Techniques for Distributed Objects, Components, and Systems, FORTE 2021 held as part of 16th International Federated Conference on Distributed Computing Techniques, DisCoTec 2021
CityVirtual, Online
Period6/14/216/18/21

Bibliographical note

Funding Information:
Mário S. Alvim and Bernardo Amorim were partially supported by CNPq, CAPES and FAPEMIG. Santiago Quintero and Frank Valencia were partially supported by the ECOS-NORD project FACTS (C19M03).

Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.

Keywords

  • Confirmation bias
  • Multi-agent systems
  • Polarization
  • Social networks

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