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The Dissemination of Culture

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  • Robert Axelrod

    (School of Public Policy, University of Michigan)

Abstract

Despite tendencies toward convergence, differences between individuals and groups continue to exist in beliefs, attitudes, and behavior. An agent-based adaptive model reveals the effects of a mechanism of convergent social influence. The actors are placed at fixed sites. The basic premise is that the more similar an actor is to a neighbor, the more likely that that actor will adopt one of the neighbor's traits. Unlike previous models of social influence or cultural change that treat features one at a time, the proposed model takes into account the interaction between different features. The model illustrates how local convergence can generate global polarization. Simulations show that the number of stable homogeneous regions decreases with the number of features, increases with the number of alternative traits per feature, decreases with the range of interaction, and (most surprisingly) decreases when the geographic territory grows beyond a certain size.

Suggested Citation

  • Robert Axelrod, 1997. "The Dissemination of Culture," Journal of Conflict Resolution, Peace Science Society (International), vol. 41(2), pages 203-226, April.
    • Handle: RePEc:sae:jocore:v:41:y:1997:i:2:p:203-226
    DOI: 10.1177/0022002797041002001
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    References listed on IDEAS

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    Cited by:

    1. Giacomo Vaccario & Mario V. Tomasello & Claudio J. Tessone & Frank Schweitzer, 2018. "Quantifying knowledge exchange in R&D networks: a data-driven model," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 461-493, August.
    2. Mario V. Tomasello & Claudio J. Tessone & Frank Schweitzer, 2016. "A Model Of Dynamic Rewiring And Knowledge Exchange In R&D Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(01n02), pages 1-23, February.
    3. Kent D. Miller & Shu-Jou Lin, 2010. "Different Truths in Different Worlds," Organization Science, INFORMS, vol. 21(1), pages 97-114, February.
    4. Daron Acemoğlu & Giacomo Como & Fabio Fagnani & Asuman Ozdaglar, 2013. "Opinion Fluctuations and Disagreement in Social Networks," Mathematics of Operations Research, INFORMS, vol. 38(1), pages 1-27, February.
    5. Song, Xiao & Zhang, Shaoyun & Qian, Lidong, 2013. "Opinion dynamics in networked command and control organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5206-5217.

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