Capturing the Complexity of Human Strategic Decision-Making with Machine Learning
This work addresses the challenge of understanding complex human strategic behavior for behavioral scientists, offering a novel interpretable model that goes beyond incremental improvements by generating new explanations.
The authors tackled the problem of predicting human strategic decision-making in two-player matrix games by analyzing over 90,000 decisions across 2,400 games, showing that a deep neural network outperforms existing theories and revealing context-dependent factors like game complexity that explain deviations from rational behavior.
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.