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Consensus in social networks: Revisited

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  • Kivinen, Steven
  • Tumennasan, Norovsambuu

Abstract

We analyze the convergence of opinions or beliefs in a general social network with non-Bayesian agents. We provide a new sufficient condition under which opinions converge to consensus and the condition is significantly more permissive than that of Lorenz (2005). This condition, which depends on properties of the network, requires agents to incorporate others’ opinions into their own posterior sufficiently often.

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  • Kivinen, Steven & Tumennasan, Norovsambuu, 2019. "Consensus in social networks: Revisited," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 11-18.
    • Handle: RePEc:eee:mateco:v:83:y:2019:i:c:p:11-18
    DOI: 10.1016/j.jmateco.2019.03.006
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    6. Mueller-Frank, Manuel, 2015. "Reaching Consensus in Social Networks," IESE Research Papers D/1116, IESE Business School.
    7. Mueller-Frank, Manuel, 2014. "Does one Bayesian make a difference?," Journal of Economic Theory, Elsevier, vol. 154(C), pages 423-452.
    8. Pooya Molavi & Alireza Tahbaz‐Salehi & Ali Jadbabaie, 2018. "A Theory of Non‐Bayesian Social Learning," Econometrica, Econometric Society, vol. 86(2), pages 445-490, March.
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    Keywords

    Networks; Consensus; Learning;
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