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Naive learning in social networks: Imitating the most successful neighbor


  • Tsakas, Nikolas


This paper considers a model of observational learning in social networks. Every period, the agents observe the actions of their neighbors and their realized outcomes, and they imitate the most successful. First, we study the case where the network has finite population and we show that, regardless of the structure, the population converges to a monomorphic steady state, i.e. where every agent chooses the same action. Subsequently, we extend our analysis to infinitely large networks and we differentiate the cases where agents have bounded neighborhoods, with those where they do not. Under bounded neighborhoods, an action is diffused to the whole population if it is the only one initially chosen by infinitely many agents. If there exist more than one such actions, we provide an additional sufficient condition in the payoff structure, which ensures convergence for any network. Without the assumption of bounded neighborhoods, we show that an action can survive even if it is initially chosen by a single agent and also that a network can be in steady state without this being monomorphic.

Suggested Citation

  • Tsakas, Nikolas, 2012. "Naive learning in social networks: Imitating the most successful neighbor," MPRA Paper 37796, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:37796

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    References listed on IDEAS

    1. Apesteguia, Jose & Huck, Steffen & Oechssler, Jorg, 2007. "Imitation--theory and experimental evidence," Journal of Economic Theory, Elsevier, vol. 136(1), pages 217-235, September.
    2. Schlag, Karl H., 1999. "Which one should I imitate?," Journal of Mathematical Economics, Elsevier, vol. 31(4), pages 493-522, May.
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    14. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
    15. Schlag, Karl H., 1998. "Why Imitate, and If So, How?, : A Boundedly Rational Approach to Multi-armed Bandits," Journal of Economic Theory, Elsevier, vol. 78(1), pages 130-156, January.
    16. Josephson, Jens & Matros, Alexander, 2004. "Stochastic imitation in finite games," Games and Economic Behavior, Elsevier, vol. 49(2), pages 244-259, November.
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    More about this item


    Social Networks; Learning; Diffusion; Imitation;

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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