  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.

Disordered Systems and Neural Networks