Abstract
Strategic decision-making is a crucial component of human interaction. Here we conduct a large-scale study of strategic decision-making in the context of initial play in two-player matrix games, analysing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on this dataset predicts human choices with greater accuracy than leading theories of strategic behaviour, revealing systematic variation unexplained by existing models. By modifying this network, we develop an interpretable behavioural model that uncovers key insights: individuals’ abilities to respond optimally and reason about others’ actions are highly context dependent, influenced by the complexity of the game matrices. Our findings illustrate the potential of machine learning as a tool for generating new theoretical insights into complex human behaviours.
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Data availability
The datasets generated and/or analysed are available at https://osf.io/xrvaw.
Code availability
The code used to generate the results is available at https://osf.io/xrvaw.
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Acknowledgements
This work and related results were made possible with the support of the NOMIS Foundation. We thank N. Chater and S. Li for helpful discussions.
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J.-Q.Z.: conceptualization, methodology, investigation, formal analysis, data curation, software, visualization, writing (original draft), and writing (reviewing and editing); J.C.P.: methodology, formal analysis, software, visualization, and writing (reviewing and editing); B.E.: conceptualization, methodology, formal analysis, supervision, validation, and writing (reviewing and editing); T.L.G.: conceptualization, methodology, formal analysis, supervision, validation, writing (reviewing and editing), resources, and funding acquisition.
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Zhu, JQ., Peterson, J.C., Enke, B. et al. Capturing the complexity of human strategic decision-making with machine learning. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02230-5
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DOI: https://doi.org/10.1038/s41562-025-02230-5
