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Strong random correlations in networks of heterogeneous agents

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  • Imre Kondor
  • Istv\'an Csabai
  • G\'abor Papp
  • Enys Mones
  • G\'abor Czimbalmos
  • M\'at\'e Csaba S\'andor
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    Abstract

    Correlations and other collective phenomena in a schematic model of heterogeneous binary agents (individual spin-glass samples) are considered on the complete graph and also on 2d and 3d regular lattices. The system's stochastic dynamics is studied by numerical simulations. The dynamics is so slow that one can meaningfully speak of quasi-equilibrium states. Performing measurements of correlations in such a quasi-equilibrium state we find that they are random both as to their sign and absolute value, but on average they fall off very slowly with distance in all instances that we have studied. This means that the system is essentially non-local, small changes at one end may have a strong impact at the other. Correlations and other local quantities are extremely sensitive to the boundary conditions all across the system, although this sensitivity disappears upon averaging over the samples or partially averaging over the agents. The strong, random correlations tend to organize a large fraction of the agents into strongly correlated clusters that act together. If we think about this model as a distant metaphor of economic agents or bank networks, the systemic risk implications of this tendency are clear: any impact on even a single strongly correlated agent will spread, in an unforeseeable manner, to the whole system via the strong random correlations.

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    File URL: http://arxiv.org/pdf/1210.3324
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    Paper provided by arXiv.org in its series Papers with number 1210.3324.

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    Date of creation: Oct 2012
    Date of revision: Feb 2014
    Handle: RePEc:arx:papers:1210.3324

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    1. Parisi, Giorgio, 1999. "Complex systems: a physicist's viewpoint," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 263(1), pages 557-564.
    2. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 8.
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