Crash Avoidance in a Complex System
Michael L. Hart, David Lamper, Neil F. Johnson
Abstract
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.
Create a lesson
Related papers
The Stone-Wales trefoil: a new topological defect in graphene
M. V. Kondrin, Y. B. Lebed, V. V. Brazhkin
Short-Range Vector Spin Glasses: Parisi Hierarchies and Real-Space Ultrametricity
Akash Vijay, Jaewon Kim, Jong Yeon Lee
Lyapunov spectrum of random neural networks
David G. Clark
Revealing more on complex energy landscapes by passing less local information: Cavity approach with trust region in non-convex optimization problems
Zhiqi Xu, Chi Ho Yeung
Predicting activation-barrier and plasticity-onset statistics in a model of glasses
Makoto Suda, Edan Lerner, Eran Bouchbinder
Classifications in modular restricted Boltzmann machines
Elena Agliari, Andrea Lepre, Edoardo Roscani