  Predicting human decision-making under risk and uncertainty represents a
quintessential challenge that spans economics, psychology, and related
disciplines. Despite decades of research effort, no model can be said to
accurately describe and predict human choice even for the most stylized tasks
like choice between lotteries. Here, we introduce BEAST Gradient Boosting
(BEAST-GB), a novel hybrid model that synergizes behavioral theories,
specifically the model BEAST, with machine learning techniques. First, we show
the effectiveness of BEAST-GB by describing CPC18, an open competition for
prediction of human decision making under risk and uncertainty, in which
BEAST-GB won. Second, we show that it achieves state-of-the-art performance on
the largest publicly available dataset of human risky choice, outperforming
purely data-driven neural networks, indicating the continued relevance of BEAST
theoretical insights in the presence of large data. Third, we demonstrate
BEAST-GB's superior predictive power in an ensemble of choice experiments in
which the BEAST model alone falters, underscoring the indispensable role of
machine learning in interpreting complex idiosyncratic behavioral data.
Finally, we show BEAST-GB also displays robust domain generalization
capabilities as it effectively predicts choice behavior in new experimental
contexts that it was not trained on. These results confirm the potency of
combining domain-specific theoretical frameworks with machine learning,
underscoring a methodological advance with broad implications for modeling
decisions in diverse environments.
