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

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.
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Contributor : Frank D. Valencia Connect in order to contact the contributor
Submitted on : Thursday, November 25, 2021 - 8:19:48 PM
Last modification on : Saturday, April 9, 2022 - 3:12:07 AM
Long-term archiving on: : Saturday, February 26, 2022 - 10:03:15 PM


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  • HAL Id : hal-03095987, version 1


Mário S. Alvim, Bernardo Amorim, Sophia Knight, Santiago Quintero, Frank Valencia. A Multi-agent Model for Polarization Under Confirmation Bias in Social Networks. FORTE 2021 - 41st International Conference on Formal Techniques for Distributed Objects, Components, and Systems, Jun 2021, Valletta, Malta. ⟨hal-03095987⟩



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