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Coordination, intermittency and trends in generalized Minority Games

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  • A. Tedeschi
  • A. De Martino
  • I. Giardina

Abstract

The Minority Game framework was recently generalized to account for the possibility that agents adapt not only through strategy selection but also by diversifying their response according to the kind of dynamical regime, or the risk, they perceive. Here we study the effects of this mechanism in different information structures. We show that both the stationary macroscopic properties and the dynamical features depend strongly on whether the information supplied to the system is exogenous (`random') or endogenous (`real'). In particular, in the latter case one observes that a small amount of herding tendency suffices to alter the collective behavior dramatically. In such cases, the dynamics is characterized by the creation and destruction of trends, accompanied by intermittent features like volatility clustering.

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  • A. Tedeschi & A. De Martino & I. Giardina, 2005. "Coordination, intermittency and trends in generalized Minority Games," Papers cond-mat/0503762, arXiv.org.
    • Handle: RePEc:arx:papers:cond-mat/0503762
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    References listed on IDEAS

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    1. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
    2. Carl Chiarella, 1992. "The Dynamics of Speculative Behaviour," Working Paper Series 13, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
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