per-round utility
Near-Optimal Play in a Social Learning Game
Carr, Ryan (University of Maryland) | Raboin, Eric (University of Maryland) | Parker, Austin (University of Maryland) | Nau, Dana (University of Maryland)
We provide an algorithm to compute near-optimal strategies for the Cultaptation social learning game. We show that the strategies produced by our algorithm are near-optimal, both in their expected utility and their expected reproductive success. We show how our algorithm can be used to provide insight into evolutionary conditions under which learning is best done by copying others, versus the conditions under which learning is best done by trial-and-error.