SCIENCE@home

Predicting human decisions with behavioural theories and machine learning

Ori Plonsky,Reut Apel,E. Ert,Moshe Tennenholtz,David D. Bourgin,Joshua C Peterson,Daniel Reichman,Thomas L. Griffiths,Stuart J. Russell,Even C Carter,J. F. Cavanagh,Ido Erev

Published 2019 in Nature Human Behaviour

ABSTRACT

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. Here we introduce BEAST gradient boosting (BEAST-GB), a hybrid model integrating behavioural 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 that BEAST-GB predicts more accurately than neural networks trained on extensive data and dozens of existing behavioural models. BEAST-GB also generalizes robustly across unseen experimental contexts, surpassing direct empirical generalization, and helps to refine and improve the behavioural theory itself. Our analyses highlight the potential of anchoring predictions on behavioural 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 behaviour. A new model merges behavioural science and machine learning to predict choice under risk and uncertainty. Tested on multiple large datasets, it outperforms top psychological and AI models, enabling accurate, interpretable forecasts of human decisions.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-59 of 59 references · Page 1 of 1

CITED BY

Showing 1-6 of 6 citing papers · Page 1 of 1