Behavioral decision research highlights interesting choice anomalies, and proposes elegant cognitive models that can explain these phenomena. Yet, it is often easier to predict behavior with theory-free machine learning tools than with the leading cognitive models. One reason for the difficulty in deriving general predictions using cognitive models is that different models are often proposed to explain different phenomena. It is then unclear which model to use to address a new task. The current talk reviews recent research and describes a new choice prediction competition project that tries to address this problem.
Based on research with Ori Plonsky, Reut Apel, Eyal Ert and Moshe Tennenholtz.
Ido Erev is the President Elect of the European Association for Decision Making, Professor of Behavioral Science at the Technion, and Research Environment Professor in Warwick Business School. His work focuses on the impact of economics incentives on choice behavior. It suggests that the initial reaction to a description of the incentive structure reflects overweighting of rare events, but experience reverses this bias.
The AI Seminar is generously supported by Apple.