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Adaptive Reasoning in Rock-Paper-Scissors

Authors
Erik Brockbank
University of California, San Diego ~ Psychology
Edward Vul
University of California, San Diego, United States of America
Abstract

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.

Tags

Keywords

adaptation
learning
decision making
conflict
cooperation

Topics

Decision Making
Learning
Discussion
New
Bot move after a tie Last updated 2 months ago

Very nice talk. Just a short question about the last slide before the Summary. What was the next move of the bot after a tie in those win-stay-loose strategies? Thanks and best Sebastian

Sebastian Gluth 0 comments
theoretical exploitations? Last updated 2 months ago

I like this work - do you have a paper in the works and maybe sharing data? I am a little confused by one of the plots though. The plot you are showing ~7 min in - this is mostly theoretical outcomes? If so they appear to have much higher potential differentials than observed. Does that mean they are not likely to be exploits? Or are the humans pos...

Dr. Leslie Blaha 0 comments
What is causing the delay before learning? Last updated 2 months ago

I'm very interested in the delay of ~200 trials before participants begin learning the bot's moderately complex strategies. How strange to see learning curves be flat over such a long range before a more traditional trajectory begins! The suddenness reminded me a bit of experiences of insight (a.k.a., Aha! moments) in the problem solving literatu...

Dr. Beth Baribault 0 comments