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

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

doi:10.1007/978-3-030-78089-0_2

Cited by 12 publications

(10 citation statements)

References 27 publications

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“…Confirmation bias and polarization 4.8 Confirmation bias has been found to lead to polarization (Del Vicario et al 2017;Alvim et al 2021). However, most models showing this effect model social interactions, by using approaches such as the DeGroot model or the Bounded Confidence Model.…”

Section: Confirmation Bias and First Impressionsmentioning

confidence: 99%

Confirmation Bias as a Mechanism to Focus Attention Enhances Signal Detection

Vogrin

Wood

Schmickl

2023

JASSS

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Confirmation bias has been traditionally seen as a detrimental aspect of the human mind, but recently researchers have also argued that it might be advantageous under certain circumstances, e.g. when coupled with meta cognition or as a means to arrive at a cognitive division of labor. To test if confirmation bias can improve performance in a perception task, we developed a minimally complex agent-based model in which agents detect binary signals. In our model, biased agents have -compared to unbiased agents -a higher chance to detect the signal they are biased for, and a lower chance to detect other signals. Additionally, detecting signals is associated with benefits, while missing signals is associated with costs. Given these basic model assumptions, biased agents perform better than unbiased agents in a wide variety of possible scenarios. Thus, we can show that confirmation bias increases the fitness of agents and we use an evolutionary algorithm to find optimal bias strengths which make them more successful at detecting signals. We conclude that confirmation bias sensitizes agents towards a certain type of data, which allows them to detect more signals. We discuss our findings in relation to topics such as polarization of opinions, the persistence of first impressions, and the social theory of reasoning.

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Section: Confirmation Bias and First Impressionsmentioning

confidence: 99%

Confirmation Bias as a Mechanism to Focus Attention Enhances Signal Detection

Vogrin

Wood

Schmickl

2023

JASSS

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show abstract

“…The results, depicted in Figure 4, will be used throughout this paper to illustrate some of our formal results. Both the Python implementation of the model and the Jupyter Notebook containing the simulations are available on Github [1].…”

Section: Running Example and Simulationsmentioning

confidence: 99%

“…Full proofs are in the Appendix. An implementation of the model in Python and the simulations are available on Github [1].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent Model for Polarization under Confirmation Bias in Social Networks

Alvim,

Amorim,

Knight

et al. 2021

Preprint

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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 weaklyconnected 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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“…The results, depicted in Figure 3, will be used throughout this paper to illustrate some of our formal results. Both the Python implementation of the model and the Jupyter Notebook containing the simulations are available on Github [1].…”

Section: Simulations and Motivating Examplesmentioning

confidence: 99%

Polarization and Belief Convergence of Agents in Strongly-Connected Influence Graphs

Alvim¹,

Amorim²,

Knight³

et al. 2020

Preprint

Self Cite

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We describe a model for polarization in multi-agent systems based on Esteban and Ray's classic measure of polarization from economics. Agents evolve by updating their beliefs (opinions) based on the beliefs of others and an underlying influence graph. We show that polarization eventually disappears (converges to zero) if the influence graph is strongly-connected. If the influence graph is a circulation we determine the unique belief value all agents converge to. For clique influence graphs we determine the time after which agents will reach a given difference of opinion. Our results imply that if polarization does not disappear then either there is a disconnected subgroup of agents or some agent influences others more than she is influenced. Finally, we show that polarization does not necessarily vanish in weakly-connected graphs, and illustrate the model with a series of case studies and simulations giving some insights about polarization.

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

Cited by 12 publications

References 27 publications

Confirmation Bias as a Mechanism to Focus Attention Enhances Signal Detection

Confirmation Bias as a Mechanism to Focus Attention Enhances Signal Detection

A Multi-Agent Model for Polarization under Confirmation Bias in Social Networks

Polarization and Belief Convergence of Agents in Strongly-Connected Influence Graphs

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