Crash Avoidance in a Complex System

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Complex systems can exhibit unexpected large changes, e.g. a crash in a financial market. We examine the large endogenous changes arising within a non-trivial generalization of the Minority Game: the Grand Canonical Minority Game (GCMG). Using a Markov Chain description, we study the many possible paths the system may take. This `many-worlds' view not only allows us to predict the start and end of a crash in this system, but also to investigate how such a crash may be avoided. We find that the system can be `immunized' against large changes: by inducing small changes today, much larger changes in the future can be prevented.
12 pages, 6 figures

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Disordered Systems and Neural Networks, Statistical Mechanics

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http://salesiana.dossiersoluciones.com/handle/123456789/19444

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cond-mat/0206228
 http://arxiv.org/abs/cond-mat/0206228

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arXiv