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 crashes.

Contribution

It introduces a Markov Chain approach to predict and potentially prevent crashes in a complex system modeled by the GCMG.

Findings

The system's crash can be predicted using a many-worlds analysis.

Small, strategic interventions can immunize the system against large crashes.

The approach offers a way to control complex system dynamics.

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.