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 sufficient...

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Main Author: Sam Ganzfried
Format: Article
Language:English
Published: LibraryPress@UF 2025-05-01
Series:Proceedings of the International Florida Artificial Intelligence Research Society Conference
Subjects:
Online Access:https://journals.flvc.org/FLAIRS/article/view/138635
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author Sam Ganzfried
author_facet Sam Ganzfried
author_sort Sam Ganzfried
collection DOAJ
description 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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issn 2334-0754
2334-0762
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series Proceedings of the International Florida Artificial Intelligence Research Society Conference
spelling doaj-art-e38ee23e0ff44f0b8cebbb55d2bc674f2025-08-20T03:49:41ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.138635Nonparametric Strategy TestSam Ganzfried0Ganzfried Research 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. https://journals.flvc.org/FLAIRS/article/view/138635game theorynonparametric statistics
spellingShingle Sam Ganzfried
Nonparametric Strategy Test
Proceedings of the International Florida Artificial Intelligence Research Society Conference
game theory
nonparametric statistics
title Nonparametric Strategy Test
title_full Nonparametric Strategy Test
title_fullStr Nonparametric Strategy Test
title_full_unstemmed Nonparametric Strategy Test
title_short Nonparametric Strategy Test
title_sort nonparametric strategy test
topic game theory
nonparametric statistics
url https://journals.flvc.org/FLAIRS/article/view/138635
work_keys_str_mv AT samganzfried nonparametricstrategytest