The goals of this research are to understand how computation might take place in complex systems, and how evolution might design such systems. Understanding how idealized complex systems such as cellular automata can compute may someday contribute to our understanding of information processing in complex systems in nature, for example, how neurons in the brain process sensory information. In addition, understanding how genetic algorithms work and how best to use them is an important goal for computer science. Such models may someday help shed light on aspects of natural evolutionary processes. Biological systems provide abundant evidence of this type of redundancy.
Gene duplication is clearly a prominent mechanism generating redundant genes.
Because redundancy provides protection against harmful mutations, natural selection is likely to be involved in generating and maintaining partial redundancy. Current research at SFI focuses on the large body of experimental data on redundant genes, and proposes conceptually simple mathematical models for the evolution of redundancy. This approach shifts the emphasis from static structures and discrete operations to continuous change, puts cognition in the same dynamical domain as the brain, body and environment, and makes contact with the principles of self- organization. The critical question for distributed systems is to understand the relation between local mechanisms and the learning process of the whole. SFI researchers and off-site collaborators are mapping out a new initiative to address these issues.