Interacting Behavior and Emerging Complexity
This work addresses the problem of quantifying complexity emergence in evolutionary processes for researchers in computational biology and complex systems, but it is incremental as it builds on existing cellular automata models.
The study investigated how complexity evolves in interacting systems by simulating two simple organisms competing for resources using Global Rules applied to Elementary Cellular Automata, finding that complexity increased most when initial rules had low complexity and that some rules were more fragile or robust to these interactions.
Can we quantify the change of complexity throughout evolutionary processes? We attempt to address this question through an empirical approach. In very general terms, we simulate two simple organisms on a computer that compete over limited available resources. We implement Global Rules that determine the interaction between two Elementary Cellular Automata on the same grid. Global Rules change the complexity of the state evolution output which suggests that some complexity is intrinsic to the interaction rules themselves. The largest increases in complexity occurred when the interacting elementary rules had very little complexity, suggesting that they are able to accept complexity through interaction only. We also found that some Class 3 or 4 CA rules are more fragile than others to Global Rules, while others are more robust, hence suggesting some intrinsic properties of the rules independent of the Global Rule choice. We provide statistical mappings of Elementary Cellular Automata exposed to Global Rules and different initial conditions onto different complexity classes.