# Capturing the Complexity of Human Strategic Decision-Making with Machine Learning

arXiv: https://arxiv.org/abs/2408.07865 · HTML: https://mlsanity.com/paper/2408.07865/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Predicting Human Behavior in Unrepeated, Simultaneous-Move Games](https://mlsanity.com/paper/1306.0918/) (2013)
- [Measuring the Completeness of Theories](https://mlsanity.com/paper/1910.07022/) (2019)

## Built on by (most cited first, 14 of 14)

- [GLEE: A Unified Framework and Benchmark for Language-based Economic Environments](https://mlsanity.com/paper/2410.05254/) (2024)
- [The Double-Edged Sword of Behavioral Responses in Strategic Classification: Theory and User Studies](https://mlsanity.com/paper/2410.18066/) (2024)
- [Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions](https://mlsanity.com/paper/2505.11614/) (2025)
- [Post-training makes large language models less human-like](https://mlsanity.com/paper/2605.07632/) (2026)
- [Alignment Makes Language Models Normative, Not Descriptive](https://mlsanity.com/paper/2603.17218/) (2026)
- [Discovering Differences in Strategic Behavior Between Humans and LLMs](https://mlsanity.com/paper/2602.10324/) (2026)
- [CSP: A Simulator For Multi-Agent Ranking Competitions](https://mlsanity.com/paper/2502.11197/) (2025)
- [ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games](https://mlsanity.com/paper/2503.05925/) (2025)
- [Eliciting Trustworthiness Priors of Large Language Models via Economic Games](https://mlsanity.com/paper/2602.00769/) (2026)
- [Understanding Human-like Solutions in Combinatorial Optimization via Learning and Search](https://mlsanity.com/paper/2607.23854/) (2026)
- [Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design](https://mlsanity.com/paper/2607.28894/) (2026)
- [Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning](https://mlsanity.com/paper/2608.12372/) (2026)
- [The Internal Anatomy of Strategic Choice in Large Language Models](https://mlsanity.com/paper/2609.07478/) (2026)
- [CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition](https://mlsanity.com/paper/2609.21259/) (2026)

## Abstract (arXiv)

Understanding how people behave in strategic settings--where they make decisions based on their expectations about the behavior of others--is a long-standing problem in the behavioral sciences. We conduct the largest study to date of strategic decision-making in the context of initial play in two-player matrix games, analyzing 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 these data predicts people's choices better than leading theories of strategic behavior, indicating that there is systematic variation that is not explained by those theories. We then modify the network to produce a new, interpretable behavioral model, revealing what the original network learned about people: their ability to optimally respond and their capacity to reason about others are dependent on the complexity of individual games. This context-dependence is critical in explaining deviations from the rational Nash equilibrium, response times, and uncertainty in strategic decisions. More broadly, our results demonstrate how machine learning can be applied beyond prediction to further help generate novel explanations of complex human behavior.