Predicting human decisions with behavioural theories and machine learning | 9am PT, Tues Oct 14, 2025
Grigory Bronevetsky
 Modeling Talks |
Predicting human decisions with behavioural theories and machine learning Ori Plonsky, Technion  Tues, October 14, 2025 | 9am PT Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home 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. Bio: Ori Plonsky is an Assistant Professor in the Faculty of Data and Decision Sciences at the Technion-Israel Institute of Technology. His research spans behavioral decision-making, human learning, and the integration of data science with behavioral science, focusing on human choice prediction and computational modeling of behavior. Ori holds a PhD in Behavioral Sciences and has an engineering background, enhancing his interdisciplinary approach. His work has been published in journals such as Psychological Review, Nature Human Behavior, and PNAS. In 2022, he received the Hillel Einhorn New Investigator Award from the Society for Judgment and Decision Making. |