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Imitating the Most Successful Neighbor in Social Networks

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  • Tsakas Nikolas

    () (Department of Economics, Universidad Carlos III de Madrid, Calle Madrid 126, 28903, Getafe, Spain)

Abstract

We study a model of observational learning in a set of agents who are connected through a social network. The agents face identical decision problems under uncertainty and update their choices myopically, imitating the choice of their most successful neighbor. We show that in finite networks, regardless of the network structure, the population converges to a monomorphic steady state, i.e., one at which every agent chooses the same action, and it cannot be predicted which this action will be. In arbitrarily large networks with bounded neighborhoods, an action is diffused to the whole population either if it is the only one initially chosen by a non-negligible share of the population, or if the payoffs satisfy a sufficient condition. Without the assumption of bounded neighborhoods, (i) an action can survive even if only one agent chooses it initially, and (ii) there may exist steady states that are not monomorphic.

Suggested Citation

  • Tsakas Nikolas, 2014. "Imitating the Most Successful Neighbor in Social Networks," Review of Network Economics, De Gruyter, vol. 12(4), pages 1-33, February.
  • Handle: RePEc:bpj:rneart:v:12:y:2014:i:4:p:33:n:4
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    References listed on IDEAS

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    1. 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.
    2. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, September.
    3. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
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    Cited by:

    1. Tsakas, Nikolas, 2017. "Diffusion by imitation: The importance of targeting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 118-151.
    2. Nikolas Tsakas, 2014. "Optimal influence under observational learning," Gecomplexity Discussion Paper Series 4, Action IS1104 "The EU in the new complex geography of economic systems: models, tools and policy evaluation", revised Nov 2014.

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