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

Autor: Sophia Knight, Bernardo Nascimento de Amorim, Santiago Quintero, Mário S. Alvim, Frank D. Valencia
Přispěvatelé: Federal University of Minas Gerais (UFMG), University of Minnesota [Duluth], University of Minnesota System, Concurrency, Mobility and Transactions (COMETE), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), This work has been partially supported by the ECOS-NORD project FACTS (C19M03), Universidade Federal de Minas Gerais = Federal University of Minas Gerais [Belo Horizonte, Brazil] (UFMG), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: FORTE 2021-41st International Conference on Formal Techniques for Distributed Objects, Components, and Systems
FORTE 2021-41st International Conference on Formal Techniques for Distributed Objects, Components, and Systems, Jun 2021, Valletta, Malta
Formal Techniques for Distributed Objects, Components, and Systems ISBN: 9783030780883
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Popis: 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.
24 pages, 6 figures. Pre-print of work to appear in the 41st International Conference on Formal Techniques for Distributed Objects, Components, and Systems (FORTE 2021). arXiv admin note: text overlap with arXiv:2012.02703
Databáze: OpenAIRE