An Investigation of Crash Avoidance in a Complex System

TL;DR

This paper analyzes large endogenous crashes in complex systems like financial markets using a generalized Minority Game model, demonstrating how small interventions can prevent major future crashes.

Contribution

It introduces a Markov Chain approach to predict and potentially prevent large system crashes in a generalized Minority Game framework.

Findings

System can be 'immunized' against large changes

Small current interventions prevent future crashes

Predictive modeling of crash timing and prevention

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.