# Predicting human decisions with behavioral theories and machine learning

arXiv: https://arxiv.org/abs/1904.06866 · HTML: https://mlsanity.com/paper/1904.06866/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search](https://mlsanity.com/paper/2506.17424/) (2025)
- [Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data](https://mlsanity.com/paper/2602.20152/) (2026)
- [LLMs Should Not Yet Be Credited with Decision Explanation](https://mlsanity.com/paper/2605.01164/) (2026)

## Abstract (arXiv)

Predicting human decisions under risk and uncertainty remains a fundamental challenge across disciplines. Existing models often struggle even in highly stylized tasks like choice between lotteries. We introduce BEAST Gradient Boosting (BEAST-GB), a hybrid model integrating behavioral theory (BEAST) with machine learning. We first present CPC18, a competition for predicting risky choice, in which BEAST-GB won. Then, using two large datasets, we demonstrate BEAST-GB predicts more accurately than neural networks trained on extensive data and dozens of existing behavioral models. BEAST-GB also generalizes robustly across unseen experimental contexts, surpassing direct empirical generalization, and helps refine and improve the behavioral theory itself. Our analyses highlight the potential of anchoring predictions on behavioral theory even in data-rich settings and even when the theory alone falters. Our results underscore how integrating machine learning with theoretical frameworks, especially those-like BEAST-designed for prediction, can improve our ability to predict and understand human behavior.