Ori Plonsky
Associate Professor, Faculty of Data and Decision Sciences, Technion
Visiting Professor, Rotman School of Management, University of Toronto
I study how people learn and make decisions from experience, and how behavioral theory and machine learning can be combined to understand and predict human choice.
Research themes
Decisions from experience
How people learn and make decisions through repeated experience, particularly in uncertain and risky environments.
Predicting human choice
Using prediction as a tool for developing and evaluating behavioral theories, and for designing interventions that work reliably in practice.
Behavioral science × machine learning
Combining behavioral theory with machine learning methods to improve both the prediction and the understanding of human decision making.
Selected research
PSYCHOLOGICAL REVIEW · 2015
Reliance on small samples, the wavy recency effect, and similarity-based learning
A foundational account of how people learn from experience, explaining several puzzling patterns of choice through reliance on small samples and similarity-based learning.
NATURE HUMAN BEHAVIOUR · 2025
Predicting human decisions with behavioral theories and machine learning
A large-scale test of how behavioral theories and machine learning can complement one another to predict both initial and repeated human decisions (AKA the BEAST-GB paper).
JOURNAL OF APPLIED PSYCHOLOGY · 2023
Motivational drivers for serial position effects: Evidence from high-stakes legal decisions
Evidence from high-stakes legal decisions showing systematic serial-position effects and examining the motivational processes behind them. This line of work was recognized with the 2021 Hillel Einhorn New Investigator Award.
Currently
During the 2026–27 academic year, I am a Visiting Professor in the Marketing Area at the Rotman School of Management, University of Toronto. I’m especially happy to connect with researchers working on related questions while I’m in Toronto.