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The survival of the weakest in networks

Author

Listed:
  • S. Nikoletseas

    (Computer Technology Institute
    University of Patras)

  • C. Raptopoulos

    (Computer Technology Institute
    University of Patras)

  • P. Spirakis

    (Computer Technology Institute
    University of Patras)

Abstract

We study here dynamic antagonism in a fixed network, represented as a graph G of n vertices. In particular, we consider the case of k≤n particles walking randomly independently around the network. Each particle belongs to exactly one of two antagonistic species, none of which can give birth to children. When two particles meet, they are engaged in a (sometimes mortal) local fight. The outcome of the fight depends on the species to which the particles belong. Our problem is to predict (i.e. to compute) the eventual chances of species survival. We prove here that this can indeed be done in expected polynomial time on the size of the network, provided that the network is undirected.

Suggested Citation

  • S. Nikoletseas & C. Raptopoulos & P. Spirakis, 2009. "The survival of the weakest in networks," Computational and Mathematical Organization Theory, Springer, vol. 15(2), pages 127-146, June.
    • Handle: RePEc:spr:comaot:v:15:y:2009:i:2:d:10.1007_s10588-008-9050-2
    DOI: 10.1007/s10588-008-9050-2
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    References listed on IDEAS

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    1. Kandori, Michihiro & Mailath, George J & Rob, Rafael, 1993. "Learning, Mutation, and Long Run Equilibria in Games," Econometrica, Econometric Society, vol. 61(1), pages 29-56, January.
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    Cited by:

    1. Saha Atanu & Havenner Arthur & Rauschenbach Sonya, 2019. "The Rise of Dominant Firms: The Role of Chance," Open Economics, De Gruyter, vol. 2(1), pages 76-91, January.

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