%0 Conference Proceedings
%T A Multi-agent Model for Polarization Under Confirmation Bias in Social Networks
%+ Departamento de Ciência da Computação [Minas Gerais] (DCC - UFMG)
%+ Department of Computer Science (USP)
%+ Department of Computer Science
%+ Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX)
%+ Pontificia universidad Javeriana, Cali
%A Alvim, Mário, S.
%A Amorim, Bernardo
%A Knight, Sophia
%A Quintero, Santiago
%A Valencia, Frank
%Z Part 1: Full Papers
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 41th International Conference on Formal Techniques for Distributed Objects, Components, and Systems (FORTE)
%C Valletta, Malta
%Y Kirstin Peters
%Y Tim A.C. Willemse
%I Springer International Publishing
%3 Formal Techniques for Distributed Objects, Components, and Systems
%V LNCS-12719
%P 22-41
%8 2021-06-14
%D 2021
%R 10.1007/978-3-030-78089-0_2
%K Polarization
%K Confirmation bias
%K Multi-agent systems
%K Social networks
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X 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.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-03740263/document
%2 https://inria.hal.science/hal-03740263/file/509782_1_En_2_Chapter.pdf
%L hal-03740263
%U https://inria.hal.science/hal-03740263
%~ X
%~ CNRS
%~ LIX
%~ X-LIX
%~ X-DEP
%~ X-DEP-INFO
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-FORTE
%~ IP_PARIS
%~ IFIP-LNCS-12719