In our work we are studying how genetic algorithms (GAs) can evolve cellular automata (CAs) to perform computations that require global coordination. The "evolving cellular automata" framework is an idealized means for studying how evolution (natural or computational) can create systems that...

We introduce a class of embedded-particle models for describing the emergent computational strategies observed in cellular automata (CAs) that were evolved for performing certain computational tasks. The models are evaluated by comparing their estimated performances with the actual performances...

Computational mechanics is a method for discovering, describing and quantifying patterns, using tools from statistical physics. It contructs optimal, minimal models of stochastic processes and their underlying causal structures. These models tell us about the intrinsic computation embedded...

Discovering relevant, but possibly hidden, variables is a key step in constructing useful and predictive theories about the natural world. This brief note explains the connections between three approaches to this problem: the recently introduced information-bottleneck method, the computational...

Thermodynamic depth is an appealing but flawed complexity measure. It depends on a set of macroscopic states for a system, but neither its original introduction by Lloyd and Pagels nor any follow-up work has considered how to select these states. Depth, therefore, is at root subjective....

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 e-machine--is the minimal one consistent with accurate prediction. We establish several results on e-machine...

We critique the measure of complexity introduced by Shiner, Davison, and Landsberg in Ref. [1]. In particular, we point out that it is over-universal, in the sense that it has the same dependence on disorder for structurally distinct systems. We also point out a misinterpretation of a result...

Discovering relevant, but possibly hidden, variables is a key step in constructing useful and predictive theories about the natural world. This brief note explains the connections between three approaches to this problem: the recently introduced information-bottleneck method, the computational...

In many complex systems control situations, searching for solutions or alternatives is involved. Searching for solutions can be modeled by a search on a fitness landscape. Knowing the structure of the underlying landscape can help in explaining or predicting aspects of an actual seach on it, and...

We cast some classes of fitness landscapes as problems in spectral analysis on various Cayley graphs. In particular, landscapes derived from RNA folding are realized on Hamming graphs and analyzed in terms of Walsh transforms; assignment problems are interpreted as functions on the symmetric...