A descentralized model for Vertical Cultural Transmission

Abstract

One of the main concerns in the simulation of adaptive behaviors is the study of the complexity of the systems. These studies are carried out through the construction of bottom-up, decentralized models. In this work, we present a decentralized model to study the Vertical Cultural Transmission (VCT), i.e., information transmission from parents to children, as a catalytic factor over the interaction between individual learning and population evolution. Our model is based on the incorporation of advice, in the form of suggestions, as a way of information transmission between agents. An evolutionary computational technique called classifier systems is used as the mechanism for individual learning. In particular, a recent variant based on the accuracy of the prediction made by the rules, called XCS, is applied. In order to control the environmental complexity we adopt the Latent Energy Environment model (LEE). Finally, experimental results showing that vertical cultural transmission does not improve the adaptation of the populations to the described environment are presented.