Multiple Worlds Model of Evolution for demographic appropriate radio playlists

Abstract

This study presents an application of the Multiple Worlds Model of Evolution. The goal is to model radio stations in a given market. The model captures listener demographics and maximizes listeners, while securing advertising revenue. Listener preferences for different types of content are set as positive (like) and negative (dislike) integers, allowing surveys of the demographic to act as the model parameters directly. Fitness evaluation is performed with a modeled hour of radio playtime where stations can select between a set of content types and advertisements. Advertisements provide fitness in the form of advertising revenues; however, listeners will only stay on a station which provides content they enjoy. The Multiple Worlds Model is a form of multiple population evolutionary algorithm. It evaluates fitness based on the actions of one member from each population, and has no genetic transfer of information between populations. Each population can thus specialize. In the current study, such specialization is a self-organization of focused (e.g. rock or country) stations via adaption to listener preferences. The model is examined using different numbers of independent populations with even splits among demographic types. The evolved stations show differences in playlists where the profiles differ in their enjoyments and convergence between stations where the listener profiles are similar.