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Boundedly rational opinion dynamics in social networks: Does indegree matter?

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  • Battiston, Pietro
  • Stanca, Luca

Abstract

This paper investigates opinion dynamics and social influence in directed communication networks. We study the theoretical properties of a boundedly rational model of opinion formation in which individuals aggregate the information they receive from their neighbors by using weights that are a function of neighbors’ indegree. We then present the results of a laboratory experiment explicitly designed to test the causal effect of indegree on social influence. We find that the social influence of an agent is positively affected by the number of individuals she listens to. When forming their opinions, agents take into account the structure of their communication network, although only to a limited extent.

Suggested Citation

  • Battiston, Pietro & Stanca, Luca, 2015. "Boundedly rational opinion dynamics in social networks: Does indegree matter?," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 400-421.
  • Handle: RePEc:eee:jeborg:v:119:y:2015:i:c:p:400-421
    DOI: 10.1016/j.jebo.2015.08.013
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    Cited by:

    1. Goldbaum David, 2019. "Conformity and Influence," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 19(1), pages 1-29, January.
    2. Foerster, Manuel, 2018. "Finite languages, persuasion bias, and opinion fluctuations," Journal of Economic Behavior & Organization, Elsevier, vol. 149(C), pages 46-57.
    3. Berno Buechel & Stefan Klößner & Martin Lochmüller & Heiko Rauhut, 2020. "The strength of weak leaders: an experiment on social influence and social learning in teams," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 259-293, June.
    4. Vegard H. Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Paper 2018/3, Norges Bank.
    5. Bashari, Masoud & Akbarzadeh-T, Mohammad-R., 2020. "Controlling opinions in Deffuant model by reconfiguring the network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 544(C).
    6. Pietro Battiston & Sharon G. Harrison, 2019. "Believe it or not: Experimental Evidence on Sunspot Equilibria with Social Networks," Working Papers 422, University of Milano-Bicocca, Department of Economics, revised Nov 2019.
    7. Wang, Zongrun & Chen, Songsheng, 2019. "Market efficiency, strategies and incomes of heterogeneously informed investors in a social network environment," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 15-32.
    8. Angelo Antoci & Guido Ferilli & Paolo Russu & Pier Luigi Sacco, 2020. "Rational populists: the social consequences of shared narratives," Journal of Evolutionary Economics, Springer, vol. 30(2), pages 479-506, April.
    9. Berno Buechel & Stefan Klößner & Martin Lochmüller & Heiko Rauhut, 2020. "The strength of weak leaders: an experiment on social influence and social learning in teams," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 259-293, June.

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    More about this item

    Keywords

    Social networks; Learning; Social influence; Bounded rationality;
    All these keywords.

    JEL classification:

    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • A14 - General Economics and Teaching - - General Economics - - - Sociology of Economics
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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