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Effect of Interaction Topology and Activation Regime in Several Multi-Agent Systems

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  • Robert L. Axtell

Abstract

The effects of distinct agent interaction and activation structures are compared and contrasted in several multi-agent models of social phenomena. Random graphs and lattices represent two limiting kinds of agent interaction networks studied, with so-called 'small-world' networks being an intermediate form between these two extremes. A model of retirement behavior is studied with each network type, resulting in important differences in key model outputs. Then, in the context of a model of firm formation, in which multi-agent structures (firms) are emergent, it is demonstrated that the medium of interaction -- whether through individual agents or through firms -- affects the qualitative character of the results. Finally, alternative agent activation 'schedules' are studied. In particular, two activation modes are compared: (1) all agents being active exactly once each period, and (2) each agent having a random number of activations in every period with mean 1. In many circumstances these two regimes produce indistinguishable results at the aggregate level, but in certain cases the differences between them are significant.

Suggested Citation

  • Robert L. Axtell, 2000. "Effect of Interaction Topology and Activation Regime in Several Multi-Agent Systems," Working Papers 00-07-039, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:00-07-039
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    References listed on IDEAS

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    1. Ellison, Glenn, 1993. "Learning, Local Interaction, and Coordination," Econometrica, Econometric Society, vol. 61(5), pages 1047-1071, September.
    2. Young, H.P., 1999. "Diffusion in Social Networks," Papers 2, Brookings Institution - Working Papers.
    3. Page, Scott E, 1997. "On Incentives and Updating in Agent Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 10(1), pages 67-87, February.
    4. Robert Axtell & Robert Axelrod & Joshua M. Epstein & Michael D. Cohen, 1995. "Aligning Simulation Models: A Case Study and Results," Working Papers 95-07-065, Santa Fe Institute.
    5. Robert Axtell, 1999. "The Emergence of Firms in a Population of Agents," Working Papers 99-03-019, Santa Fe Institute.
    6. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, January.
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    Cited by:

    1. Jacques Laye & Charis Lina & Herve Tanguy, 2006. "E-consumers' search and emerging structure of B-to-C coalitions," Computing in Economics and Finance 2006 374, Society for Computational Economics.
    2. Denis Phan & Stephane Pajot & Jean-Pierre Nadal, 2003. "The Monopolist's Market with Discrete Choices and Network Externality Revisited: Small-Worlds, Phase Transition and Avalanches in an ACE Framework," Computing in Economics and Finance 2003 150, Society for Computational Economics.
    3. Paul L. Borrill & Leigh Tesfatsion, 2011. "Agent-based Modeling: The Right Mathematics for the Social Sciences?," Chapters,in: The Elgar Companion to Recent Economic Methodology, chapter 11 Edward Elgar Publishing.
    4. Yves Saillard, 2004. "L'analyse économique des normes : représentation et traitement des interactions dans les modèles de simulation," Post-Print halshs-00104866, HAL.
    5. van der Hoog, Sander, 2008. "On the disequilibrium dynamics of sequential monetary economies," Journal of Economic Behavior & Organization, Elsevier, vol. 68(3-4), pages 525-552, December.
    6. Levy, David M. & Makowsky, Michael D., 2010. "Price dispersion and increasing returns to scale," Journal of Economic Behavior & Organization, Elsevier, vol. 73(3), pages 406-417, March.

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