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Dynamic Learning, Herding and Guru Effects in Networks

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Author Info
Sheri Markose ()
Amadeo Alentorn
Andreas Krause

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Abstract

It has been widely accepted that herding is the consequence of mimetic responses by agents interacting locally on a communication network. In extant models, this communication network linking agents, by and large, has been assumed to be fixed. In this paper we allow it to evolve endogenously by enabling agents to adaptively modify the weights of their links to their neighbours by reinforcing ‘good’ advisors and breaking away from ‘bad’ advisors with the latter being replaced randomly from the remaining agents. The resulting network not only allows for herding of agents, but crucially exhibits realistic properties of socio-economic networks that are otherwise difficult to replicate: high clustering, short average path length and a small number of highly connected agents, called "gurus". These properties are now well understood to characterize ‘small world networks’ of Watts and Strogatz (1998).

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Paper provided by University of Essex, Department of Economics in its series Economics Discussion Papers with number 582.

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Date of creation: 12 Oct 2004
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Handle: RePEc:esx:essedp:582

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  1. Alan Kirman, 1997. "The economy as an evolving network," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 339-353. [Downloadable!] (restricted)
  2. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October. [Downloadable!] (restricted)
  3. Venkatesh Bala & Sanjeev Goyal, 2000. "A Noncooperative Model of Network Formation," Econometrica, Econometric Society, vol. 68(5), pages 1181-1230, September.
  4. George J. Mailath & Larry Samuelson & Avner Shaked, 1997. "Endogenous Interactions," CARESS Working Papres endo-one, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences. [Downloadable!]
  5. Blume Lawrence E., 1993. "The Statistical Mechanics of Strategic Interaction," Games and Economic Behavior, Elsevier, vol. 5(3), pages 387-424, July. [Downloadable!] (restricted)
  6. Sheri M. Markose, 2004. "Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS)," Economics Discussion Papers 574, University of Essex, Department of Economics. [Downloadable!]
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  7. Sheri M. Markose, 2001. "The New Evolutionary Computational Paradigm of Complex Adaptive Systems: Challenges and Prospects for Economics and Finance," Economics Discussion Papers 532, University of Essex, Department of Economics. [Downloadable!]
  8. Chwe, Michael Suk-Young, 2000. "Communication and Coordination in Social Networks," Review of Economic Studies, Blackwell Publishing, vol. 67(1), pages 1-16, January.
  9. Scharfstein, David S & Stein, Jeremy C, 1990. "Herd Behavior and Investment," American Economic Review, American Economic Association, vol. 80(3), pages 465-79, June.
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  10. Kirman, Alan P., 1983. "Communication in markets : A suggested approach," Economics Letters, Elsevier, vol. 12(2), pages 101-108. [Downloadable!] (restricted)
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