Evolving complexity in cooperative and competitive noisy prediction games

Abstract

We examine the effect of cooperative and competitive interactions on the evolution of complex strategies in a prediction game. We extend previous work to the domain of noisy games, defining a new organism and mutation model, and an accompanying novel complexity metric. We find that a mix of cooperation and competition is the most effective in driving complexity growth, confirming prior results. We also compare our complexity metric with simpler metrics such as raw strategy size, and demonstrate the effectiveness of our metric in distinguishing true complexity from mere genetic bloat.