we regard computation as a general principle for generating open-ended complexity. Turing's universal computation, and Von Neumann's principle of open-ended complexity growth, formalized a principle of computation which happens to also be the most fundamental principle of life: neo-Darwinian evolution. From this computational understanding of evolutionary systems, we am interested in several questions: how do cells and collectives of cells compute? Is language an evolutionary system operating under the same principle? Can artificial systems implement the same principle?
We are interested in the linguistic/symbolic aspects of the living organization (the gene as a carrier of information, and DNA as memory) which play a large role in the seemingly open-ended evolution defined by natural selection. This symbolic vision of biology (bio-semiotics), at first glance, seems to be at odds with notions of self-organization so dear to complex systems scientists and a more developmental approach to biology. Therefore, we have been studying the interplay between self-organization and natural selection (in embodied agents), introducing the concept of selected self-organization [Rocha ,1996a; Rocha, 1998a].
We are particularly interested in the problem of how information, symbols, representations and the like can arise from a purely dynamical system of many components. In addition to our work on collective computation and origin of representations, we have worked on simulations of evolving agents with different kinds of reproduction strategies (self-inspection and via a symbolic genotype-phenotype mapping). For these simulations we developed a genetic algorithm with an indirect encoding implemented with Fuzzy Development Programs, which model self-organizing development processes. More information on these simulations is available in the Fuzzy Development Programs’ Resource page, which contains publications and software for understanding and using these. You can also check a paper where these simulations are detailed. The figure depicts a run of our agent-based model where agents which reproduce via a genotype-phenotype mapping completely overtake a population, in a few generations, also containing agents which reproduce by self-inspection without such mappings.
Ian B Wood