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Self-Organized Criticality In Evolutionary Systems With Local Interaction

Author

Listed:
  • Albert Díaz-Guilera

    (Universidad de Barcelona)

  • Alex Arenas Moreno

    (Universitat Rovira i Virgili)

  • Conrad J. Pérez Vicente

    (Universidad de Barcelona)

  • Fernando Vega Redondo

    (Instituto Valenciano de Investigaciones Económicas)

Abstract

This paper studies a stylized model of local interaction where agents choose from an ever increasing set of vertically ranked actions, e.g. technologies. The driving forces of the model are infrequent upward shifts ("updates"), followed by a rapid process of local imitation ("diffusion"). Our main focus is on the long-run regularities displayed by the long-run distribution of diffusion waves and their implication on the performance of the system. By integrating analytical techniques and numerical simulations, we come to the following two main conclusions: (1) When the penalty for "technological dis-coordination" (the single key parameter of the model) is high enough, the system behaves critically, in the sense customarily used in physics -that is, diffusion waves have their size (or reach) distributed according to power laws. (2) If the performance of the system is evaluated by how fast or cost-efficiently it attains any given technological level, the optimal configuration obtains (in parameter space) close to the frontier of the critical region. There, the system no longer displays synchronized behavior but starts to exhibit persistent and critical long-run heterogeneities. In the heuristic language used by Kauffman (1993), the above two conclusions may be interpreted as an indication that (performance-sensitive) evolutionary forces induce the system to be placed "at the edge of order and chaos".

Suggested Citation

  • Albert Díaz-Guilera & Alex Arenas Moreno & Conrad J. Pérez Vicente & Fernando Vega Redondo, 2000. "Self-Organized Criticality In Evolutionary Systems With Local Interaction," Working Papers. Serie AD 2000-30, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  • Handle: RePEc:ivi:wpasad:2000-30
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    References listed on IDEAS

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    Cited by:

    1. Otto, C. & Willner, S.N. & Wenz, L. & Frieler, K. & Levermann, A., 2017. "Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate," Journal of Economic Dynamics and Control, Elsevier, vol. 83(C), pages 232-269.
    2. Fosco, Constanza & Mengel, Friederike, 2011. "Cooperation through imitation and exclusion in networks," Journal of Economic Dynamics and Control, Elsevier, vol. 35(5), pages 641-658, May.
    3. Andergassen, Rainer & Nardini, Franco & Ricottilli, Massimo, 2006. "Innovation waves, self-organized criticality and technological convergence," Journal of Economic Behavior & Organization, Elsevier, vol. 61(4), pages 710-728, December.
    4. Blume, Andreas & Duffy, John & Temzelides, Ted, 2010. "Self-organized criticality in a dynamic game," Journal of Economic Dynamics and Control, Elsevier, vol. 34(8), pages 1380-1391, August.
    5. Andergassen, Rainer & Nardini, Franco & Ricottilli, Massimo, 2009. "Innovation and growth through local and global interaction," Journal of Economic Dynamics and Control, Elsevier, vol. 33(10), pages 1779-1795, October.
    6. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 145-165, October.
    7. Rainer Andergassen & Franco Nardini, 2005. "Firms’ Network Formation Through The Transmission Of Heterogeneous Knowledge," Computing in Economics and Finance 2005 322, Society for Computational Economics.
    8. R. Andergassen & F. Nardini & M. Ricottilli, 2013. "Innovation diffusion, technological convergence and economic growth," Working Papers wp912, Dipartimento Scienze Economiche, Universita' di Bologna.
    9. Otto, Christian & Willner, Sven Norman & Wenz, Leonie & Frieler, Katja & Levermann, Anders, 2017. "Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate," OSF Preprints 7yyhd, Center for Open Science.
    10. Andergassen, Rainer & Nardini, Franco & Ricottilli, Massimo, 2017. "Innovation diffusion, general purpose technologies and economic growth," Structural Change and Economic Dynamics, Elsevier, vol. 40(C), pages 72-80.
    11. Rainer Andergassen & Franco Nardini & Massimo Ricottilli, 2006. "The Emergence of Paradigm Setters Through Firms’ Interaction and Network Formation," Lecture Notes in Economics and Mathematical Systems, in: Akira Namatame & Taisei Kaizouji & Yuuji Aruka (ed.), The Complex Networks of Economic Interactions, pages 93-106, Springer.
    12. R. Andergassen & F. Nardini & M. Ricottilli, 2005. "Firms' Network Formation Through the Transmission of Heterogeneous Knowledge," Working Papers 543, Dipartimento Scienze Economiche, Universita' di Bologna.
    13. Cui, Zhiwei, 2014. "More neighbors, more efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 103-115.
    14. repec:pit:wpaper:276 is not listed on IDEAS

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