Computational Mechanics: Pattern and Prediction, Structure and Simplicity

Abstract

Computational mechanics, an approach to structural complexity, defines a process's causal states and gives a procedure for finding them. We show that the causal-state representation--an $\epsilon$-machine--is the minimal one consistent with accurate prediction. We establish several results on $\epsilon$-machine optimality and uniqueness and on how $\epsilon$-machines compare to alternative representations. Further results relate measures of randomness and structural complexity obtained from $\epsilon$-machines to those from ergodic and information theories.