Maturation and the Evolution of Imitative Learning in Artificial Organisms

The traditional explanation of delayed maturation age, as part of an evolved life history, focuses on the increased costs of juvenile mortality due to early maturation. Prior quantitative models of these trade-offs, however, have addressed only morphological phenotypic traits, such as body size. We argue that the development of behavioral skills prior to reproductive maturity also constitutes an advantage of delayed maturation and thus should be included among the factors determining the trade-off for optimal age at maturity. Empirical support for this hypothesis from animal field studies is abundant. This paper provides further evidence drawn from simulation experiments. “Latent Energy Environments” (LEE) are a class of tightly controlled environments in which learning organisms are modeled by neural networks and evolved according to a type of genetic algorithm. An advantage of this artificial world is that it becomes possible to discount all non-behavioral costs of early maturity in order to focus on exclusively behavioral consequences. In spite of large selective costs imposed on parental fitness due to prolonged immaturity, the optimal age at maturity is shown to be significantly delayed when offspring learn from their parents' behavior via imitation.