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A Dynamic Model of Social Network Formation

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Adaptive Networks

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

We consider a dynamic social network model in which agents play repeated games in pairings determined by a stochastically evolving social network. Individual agents begin to interact at random, with the interactions modeled as games. The game payoffs determine which interactions are reinforced, and the network structure emerges as a consequence of the dynamics of the agents’ learning behavior. We study this in a variety of game-theoretic conditions and show that the behavior is complex and sometimes dissimilar to behavior in the absence of structural dynamics. We argue that modeling network structure as dynamic increases realism without rendering the problem of analysis intractable.

This chapter is based on B. Skyrms and R. Pemantle, “A Dynamic Model of Social Network Formation” PNAS 97 (16), 9340-9346 (2000).

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Author information

Authors and Affiliations

  1. School of Social Sciences, University of California at Irvine, 92607, Irvine, CA, USA

    Brian Skyrms

  2. Department of Mathematics, University of Pennsylvania, 19104, Philadelphia, PA, USA

    Robin Pemantle

Authors
  1. Brian Skyrms
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  2. Robin Pemantle
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Correspondence to Brian Skyrms .

Editor information

Editors and Affiliations

  1. MPI für Physik Komplexer Systeme, Nöthnitzer Str. 38, Dresden, 01187, Germany

    Thilo Gross

  2. Binghamton University, State University of New York, Binghamton, 13902-6000, U.S.A.

    Hiroki Sayama

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Skyrms, B., Pemantle, R. (2009). A Dynamic Model of Social Network Formation. In: Gross, T., Sayama, H. (eds) Adaptive Networks. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01284-6_11

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  • DOI: https://doi.org/10.1007/978-3-642-01284-6_11

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  • Online ISBN: 978-3-642-01284-6

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