Ido Erev (PhD: University of North Carolina, 1990, Cognitive/Quantitative Psychology).
Ido joined the Faculty of Industrial Engineering and Management in 1990 as a lecturer. He was promoted to full professor in 2004, and has held the “Women’s Division—ATS Academic Chair” since 2006. He was the head of the Behavioral Science Area, the head of the Technion section of the Max Wertheimer Minerva Center for Cognitive Research, and the head of the Technion group in the ICORE for Empirical Legal Studies of Decision Making .
Ido has been a visiting research associate in economics at the University of Pittsburgh; a Michael A. Gould fellow at Columbia Business School; a Marvin Bower Fellow at Harvard Business School; a fellow at the Israel Center of Advanced Studies; a visiting professor at Erasmus School of Economics; a visiting professor at the Interdisciplinary Center in Herzliya (IDC); and a research environment professor at Warwick Business School.
Ido and his co-workers focus on three related lines of research. The first centers on the observation of a large difference between decisions that are made based on a description of the incentive structure, and decisions that are made based on experience: people tend to exhibit oversensitivity to rare events in decisions from description, and the opposite bias in decisions from experience. This observation, initially documented by Barron & Erev (2003) and now known as the experience-description gap (Hertwig & Erev, 2009), is important because mainstream behavioral economic research (e.g., Kahneman & Tversky, 1979) focuses on decisions from description, while most of the efforts to apply it involve decisions from experience. Ido and his co-workers address this problem by the systematic study of decisions from experience.
A second line of research focuses on the difference between anomalies and forecasts. The leading models of choice behavior tend to focus on interesting anomalies. Since each model focuses on a few isolated anomalies, it is not easy to use these models to derive clear forecasts. That is, it Is not clear which of the leading models should be used to address a new choice problem. Ido and his co-workers try to address this problem by developing general models, and then organizing international choice prediction competitions in which they challenge other researchers to propose better models that can capture all the classical anomalies and allow ex ante predictions of behavior (see, for example: http://departments.agri.huji.ac.il/economics/teachers/ert_eyal/competition.htm ).
A third line of research centers on the practical implications of the basic research summarized above. The experience-description gap, and the models that best capture it, suggest that economic incentives are most effective when they ensure that socially desirable behavior maximizes payoff, and also minimizes the probability of regret. Ido and his co-authors use this observation to address distinct social and organizational problems. For example, they demonstrate that gentle rule enforcement methods are more effective in improving safety in industrial settings than traditional policies (see Erev & Roth, 2014).