Prediction in evolutionary systems

Despite its explanatory clout, the theory of evolution has thus far compiled a modest record with respect to predictive power—that other major hallmark of scientific theories. This is considered by many to be an acceptable limitation of a theory that deals with events and processes that are intrinsically random (and historic). However, whether this is an inherent restriction or simply the sign of an incomplete theory is an open question. In an attempt to help answer that question, we propose a classification scheme for several types of prediction that might occur with regard to evolutionary systems, then explore the nature of these predictions in a system that simulates the evolution of neural architectures. This provides a platform from which to consider the relevance of such observations for real biological systems and illuminates a variety of key issues pertaining to prediction in those environments.