Learning from rewards and social information in naturalistic strategic behavior

Acting intelligently in complex environments poses a challenging learning problem: faced with many different situations and possible actions, how do people learn which action to take in each situation? While traditional laboratory-based experiments have been used to study specific learning mechanisms, these experiments often employ relatively simple tasks conducted over a short period of time. Thus, it is unclear to what extent these mechanisms are used in the significantly more complex and temporally extended environments people encounter in their everyday lives. To understand the processes by which people learn policies to guide their decisions, we investigate the opening strategies of novice online chess players over their first months of play. We use a large online data set consisting of 2,499,783 games, providing us with the necessary scale to explore learning mechanisms in a complex setting. In particular, we focus on two types of learning: reinforcement learning, or learning from rewards given repeated experiences, and social learning, or learning from the actions of others. We show that players’ choices are modulated by both game outcomes and observing their opponents’ actions, and that they exhibit important hallmarks of adaptive decision-making such as exploration and expertise. Our results provide evidence that people use sophisticated learning algorithms in naturalistic strategic behavior.