Crash Avoidance in a Complex System
| dc.creator | Hart, Michael L. | |
| dc.creator | Lamper, David | |
| dc.creator | Johnson, Neil F. | |
| dc.date | 2002-06-13 | |
| dc.date.accessioned | 2026-07-07T02:45:50Z | |
| dc.date.available | 2026-07-07T02:45:50Z | |
| dc.description | 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. | |
| dc.description | 12 pages, 6 figures | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0206228 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0206228 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/19444 | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.subject | Statistical Mechanics | |
| dc.title | Crash Avoidance in a Complex System | |
| dc.type | text |