Process Information and Evolution

Universal Semantic Communication (USC) is a theory that models communication among agents without the assumption of a fixed protocol. We demonstrate a connection, via a concept we refer to as process information, between a special case of USC and evolutionary processes. In this context, one agent attempts to interpret a potentially arbitrary signal produced within its environment. Sources of this effective signal can be modeled as a single alternative agent. Given a set of common underlying concepts that may be symbolized differently by different sources in the environment, any given entity must be able to correlate intrinsic information with input it receives from the environment in order to accurately interpret the ambient signal and ultimately coordinate its own actions. This scenario encapsulates a class of USC problems that provides insight into the semantic aspect of a model of evolution proposed by Rivoire and Leibler. Through this connection, we show that evolution corresponds to a means of solving a special class of USC problems, can be viewed as a special case of the Multiplicative Weights Updates algorithm, and that infinite population selection with no mutation and no recombination conforms to the Rivoire-Leibler model. Finally, using process information we show that evolving populations implicitly internalize semantic information about their respective environments.