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The coevolution of beliefs and networks

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  • Arifovic, Jasmina
  • Eaton, B. Curtis
  • Walker, Graeme

Abstract

Social psychologists have shown that people experience cognitive dissonance when two or more of their cognitions diverge, and that they actively manage the dissonance. With this in mind, we develop a model of social learning in networks to understand the coevolution of beliefs and networks. We focus on beliefs concerning an objective phenomenon. Initial beliefs are based on noisy, private and unbiased information. Because the information is noisy, initial beliefs differ, creating dissonance. In our model, behavior is motivated by a desire to minimize this dissonance. In many circumstances this behavior adversely affects the efficiency of social learning, such that in equilibrium the mean aggregate belief is biased and there is significant variation of beliefs across the population. The parameterizations of our model that result in the most inefficient learning produce a fractionalized network structure in which there are a number of distinct groups: within any group all beliefs are identical; beliefs differ from group to group, sometimes greatly; there is no intergroup interaction. Since dissonance minimizing behavior is apparently a deeply rooted feature of humans, we are led to ask: What policies could improve the situation? Our results suggest that policies that improve the availability of objective information and/or increase the size of networks enhance efficiency of social learning. On the other hand, anything that makes changing networks more attractive as a dissonance minimizing strategy has the opposite effect.

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  • Arifovic, Jasmina & Eaton, B. Curtis & Walker, Graeme, 2015. "The coevolution of beliefs and networks," Journal of Economic Behavior & Organization, Elsevier, vol. 120(C), pages 46-63.
    • Handle: RePEc:eee:jeborg:v:120:y:2015:i:c:p:46-63
    DOI: 10.1016/j.jebo.2015.08.011
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    1. Arifovic, Jasmina & Eaton, B. Curtis & Walker, Graeme, 2015. "The coevolution of beliefs and networks," Journal of Economic Behavior & Organization, Elsevier, vol. 120(C), pages 46-63.
    2. repec:cup:cbooks:9780511771576 is not listed on IDEAS
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    2. Antonio Parravano & Ascensión Andina-Díaz & Miguel A Meléndez-Jiménez, 2016. "Bounded Confidence under Preferential Flip: A Coupled Dynamics of Structural Balance and Opinions," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    3. Ascensión Andina-Díaz & José A. García-Martínez & Antonio Parravano, 2019. "The market for scoops: a dynamic approach," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(2), pages 175-206, June.
    4. Goldbaum David, 2019. "Conformity and Influence," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 19(1), pages 1-29, January.
    5. Anufriev, Mikhail & Borissov, Kirill & Pakhnin, Mikhail, 2023. "Dissonance minimization and conversation in social networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 167-191.
    6. Rapanos, Theodoros, 2023. "What makes an opinion leader: Expertise vs popularity," Games and Economic Behavior, Elsevier, vol. 138(C), pages 355-372.
    7. Neugart, Michael & Yildirim, Selen, 2022. "Heritability in friendship networks," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 41-55.
    8. David Goldbaum, 2016. "Networks formation to assist decision making," Working Paper Series 37, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    9. Arifovic, Jasmina & Eaton, B. Curtis & Walker, Graeme, 2015. "The coevolution of beliefs and networks," Journal of Economic Behavior & Organization, Elsevier, vol. 120(C), pages 46-63.
    10. Gustavo Adolfo Caballero Orozco, 2016. "Luck and Effort: Learning about Income from Friends and Neighbors," 2016 Papers pca706, Job Market Papers.
    11. Kivinen, Steven, 2017. "Polarization in strategic networks," Economics Letters, Elsevier, vol. 154(C), pages 81-83.

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