Crash Avoidance in a Complex System
无序系统与神经网络
2007-05-23 v1 统计力学
摘要
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.
关键词
引用
@article{arxiv.cond-mat/0206228,
title = {Crash Avoidance in a Complex System},
author = {Michael L. Hart and David Lamper and Neil F. Johnson},
journal= {arXiv preprint arXiv:cond-mat/0206228},
year = {2007}
}
备注
12 pages, 6 figures