Adaptive Reasoning in Rock-Paper-Scissors

Human conflict and coordination relies on our ability to reason about and predict the behavior of others. We investigate how people adapt to and exploit their opponents in repeated adversarial interactions through iterated play of Rock-Paper-Scisssors (RPS).In Experiment 1, we investigate naturalistic adversarial interactions between two humans. Participants (N=116) played 300 rounds of RPS in 58 stable dyads. We find that the distribution of win count differentials differs significantly from Nash equilibrium random play (χ^2(5)=133.27, p < 0.001), suggesting that many participants are able to exploit dependencies in their opponent’s move choices. However, expected win count differentials based on observed regularities in participant behavior reveal that people fail to maximally exploit their opponents. This raises the question of what kinds of patterned behavior people are able to detect and exploit.In Experiment 2, participants (N=217) were paired against bots employing stable RPS strategies. We tested seven strategies that parametrically varied the number and source of their behavioral regularities. This allowed us to establish levels of complexity that people exploit maximally, partially, and not at all. For partially exploitable bots, participants reach close to maximal exploitation of subparts of the bot’s strategy, with chance performance otherwise, suggesting that people are selectively sensitive to particular patterns of opponent behavior.Our results show that the ability to exploit opponents in adaptive settings relies on successful detection of a limited set of patterns. A concrete understanding of the inputs people use to predict others provides insight into how people establish cooperative behavior, and why it sometimes fails.