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The influence of random interactions and decision heuristics on norm evolution in social networks

Author

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  • Declan Mungovan

    (National University of Ireland)

  • Enda Howley

    (National University of Ireland)

  • Jim Duggan

    (National University of Ireland)

Abstract

In this paper we explore the effect that random social interactions have on the emergence and evolution of social norms in a simulated population of agents. In our model agents observe the behaviour of others and update their norms based on these observations. An agent’s norm is influenced by both their own fixed social network plus a second random network that is composed of a subset of the remaining population. Random interactions are based on a weighted selection algorithm that uses an individual’s path distance on the network to determine their chance of meeting a stranger. This means that friends-of-friends are more likely to randomly interact with one another than agents with a higher degree of separation. We then contrast the cases where agents make highest utility based rational decisions about which norm to adopt versus using a Markov Decision process that associates a weight with the best choice. Finally we examine the effect that these random interactions have on the evolution of a more complex social norm as it propagates throughout the population. We discover that increasing the frequency and weighting of random interactions results in higher levels of norm convergence and in a quicker time when agents have the choice between two competing alternatives. This can be attributed to more information passing through the population thereby allowing for quicker convergence. When the norm is allowed to evolve we observe both global consensus formation and group splintering depending on the cognitive agent model used.

Suggested Citation

  • Declan Mungovan & Enda Howley & Jim Duggan, 2011. "The influence of random interactions and decision heuristics on norm evolution in social networks," Computational and Mathematical Organization Theory, Springer, vol. 17(2), pages 152-178, May.
    • Handle: RePEc:spr:comaot:v:17:y:2011:i:2:d:10.1007_s10588-011-9085-7
    DOI: 10.1007/s10588-011-9085-7
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    Cited by:

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    2. Raúl M. Ortiz-Gaona & Marcos Postigo-Boix & José L. Melús-Moreno, 2021. "Extent prediction of the information and influence propagation in online social networks," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 195-230, June.
    3. Luis Almeida Costa & João Amaro Matos, 2014. "Attitude change in arbitrarily large organizations," Computational and Mathematical Organization Theory, Springer, vol. 20(3), pages 219-251, September.

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