Nonparametric Strategy Test

We present a nonparametric statistical test for determining whether an agent is following a given mixed strategy in a repeated strategic-form game given samples of the agent's play. This involves two components: determining whether the agent's frequencies of pure strategies are sufficiently close to the target frequencies, and determining whether the pure strategies selected are independent between different game iterations. Our integrated test involves applying a chi-squared goodness of fit test for the first component and a generalized Wald-Wolfowitz runs test for the second component. The results from both tests are combined using Bonferroni correction to produce a complete test for a given significance level alpha. We applied the test to publicly available data of human rock-paper-scissors play. The data consists of 50 iterations of play for 500 human players. We test with a null hypothesis that the players are following a uniform random strategy independently at each game iteration. Using a significance level of alpha = 0.05, we conclude that 305 (61%) of the subjects are following the target strategy.

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Publication Details

Journal
Proceedings of the ... International Florida Artificial Intelligence Research Society Conference
Published
2025-05-14
DOI
https://doi.org/10.32473/flairs.38.1.138635
Primary Topic
Big Data and Business Intelligence
Type
article
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article

Nonparametric Strategy Test

Sam Ganzfried
Proceedings of the ... International Florida Artificial Intelligence Research Society Conference
Big Data and Business Intelligence
article

Nonparametric Strategy Test

Sam Ganzfried
article en

Abstract

We present a nonparametric statistical test for determining whether an agent is following a given mixed strategy in a repeated strategic-form game given samples of the agent's play. This involves two components: determining whether the agent's frequencies of pure strategies are sufficiently close to the target frequencies, and determining whether the pure strategies selected are independent between different game iterations. Our integrated test involves applying a chi-squared goodness of fit test for the first component and a generalized Wald-Wolfowitz runs test for the second component. The results from both tests are combined using Bonferroni correction to produce a complete test for a given significance level alpha. We applied the test to publicly available data of human rock-paper-scissors play. The data consists of 50 iterations of play for 500 human players. We test with a null hypothesis that the players are following a uniform random strategy independently at each game iteration. Using a significance level of alpha = 0.05, we conclude that 305 (61%) of the subjects are following the target strategy.

Proceedings of the ... International Florida Artificial Intelligence Research Society ConferenceVol. 38
Openalex Percentile: Top 64%
Big Data and Business Intelligence
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