ArticleNature human behaviour2025
Predicting human decisions with behavioural theories and machine learning.
Article in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- From Self-Worth to Sustainable Choice: An Efficacy-Translation Account of Green Consumption.Behavioral sciences (Basel, Switzerland) · 2026Article
- Large language models accurately identify decision reasons in verbal reports.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Feedback-induced attitudinal changes in risk preferences.Nature communications · 2026Article
- Beyond binary: a machine-learning classification of childhood COVID-19 vaccination intentions using behavioural data.Population health metrics · 2025Article
- Sequence-to-sequence models with attention mechanistically map to the architecture of human memory search.Communications psychology · 2025Article
- Behavior engineering using quantitative reinforcement learning models.Nature communications · 2025Article
- Modelling dataset bias in machine-learned theories of economic decision-making.Nature human behaviour · 2024Article
- Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.PLoS computational biology · 2024Article
- Nonverbal behavioral patterns predict social rejection elicited aggression.Biological psychology · 2023Article
- Lying on the Dissection Table: Anatomizing Faked Responses.Behavior research methods · 2022Article
- Humans as intuitive classifiers.Frontiers in psychology · 2022Article
- Empirica: a virtual lab for high-throughput macro-level experiments.Behavior research methods · 2021Article
- On the Value of Alert Systems and Gentle Rule Enforcement in Addressing Pandemics.Frontiers in psychology · 2020Article
- Personalizing Human-Agent Interaction Through Cognitive Models.Frontiers in psychology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
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.
Indexed as
Identifiers
40691307What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.