Formalizing Opponent Modeling with the Rock, Paper, Scissors Game

Author:

Brockbank ErikORCID,Vul Edward

Abstract

In simple dyadic games such as rock, paper, scissors (RPS), people exhibit peculiar sequential dependencies across repeated interactions with a stable opponent. These regularities seem to arise from a mutually adversarial process of trying to outwit their opponent. What underlies this process, and what are its limits? Here, we offer a novel framework for formally describing and quantifying human adversarial reasoning in the rock, paper, scissors game. We first show that this framework enables a precise characterization of the complexity of patterned behaviors that people exhibit themselves, and appear to exploit in others. This combination allows for a quantitative understanding of human opponent modeling abilities. We apply these tools to an experiment in which people played 300 rounds of RPS in stable dyads. We find that although people exhibit very complex move dependencies, they cannot exploit these dependencies in their opponents, indicating a fundamental limitation in people’s capacity for adversarial reasoning. Taken together, the results presented here show how the rock, paper, scissors game allows for precise formalization of human adaptive reasoning abilities.

Publisher

MDPI AG

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference74 articles.

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Rock-Paper-Scissors Image Classification Using Transfer Learning;2023 International Conference on Business Analytics for Technology and Security (ICBATS);2023-03-07

2. A Hidden Markov Model Based Intelligent Platform for Characterizing Behaviors;2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS);2022-11-17

3. Behavior Reasoning for Opponent Agents in Multi-Agent Learning Systems;IEEE Transactions on Emerging Topics in Computational Intelligence;2022-10

4. Assessing behavioural profiles following neutral, positive and negative feedback;PLOS ONE;2022-07-05

5. Transfer of Learned Opponent Models in Zero Sum Games;Computational Brain & Behavior;2022-05-31

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