Capturing the complexity of human strategic decision-making with machine learning

Authors: JQ Zhu, JC Peterson, B Enke, TL Griffiths

Published: 2025

Publication: Nature Human Behaviour, 2025 - nature.com

This study used deep neural networks to analyze human strategic decision-making, predicting choices more accurately than existing theories and uncovering the context-dependent nature of reasoning and decision-making in complex games.

Methods: Deep neural networks trained on data from procedurally generated matrix games with over 2,400 variations; models were modified for interpretability.

Key Findings: Human choices and reasoning in initial play of two-player matrix games, focusing on strategic decision-making and response to game complexity.

Limitations: Findings are limited to initial play in matrix games and may not generalize to all forms of human strategic decision-making.

Institution: Princeton University, Boston University, Harvard University

Research Area: Strategic decision-making, Machine learning, Computational Cognitive Science

Discipline: Artificial Intelligence

Sample Size: 90000 participants

Citations: 16

DOI: https://doi.org/10.1038/s41562-025-02230-5