Monthly Friday Seminar - Ido Erev (Technion - Israel Institute of Technology)

Professeur invité ISEM. Titre de la présentation "From Anomalies to Forecasts: Toward a Descriptive Model of Decisions under Risk, under Ambiguity, and from Experience"
Quand ? Le 01-04-2016,
de 10:00 à 12:00
Où ? ISEM en salle de visioconférence (SCI)
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Ido Erev

(William Davidson Faculty for Industrial Engineering and Management, Haifa, Israël)


Vendredi 1er avril 2016 


Résumé : Experimental studies of choice behavior document distinct, and sometimes contradictory, deviations from maximization in different settings and experimental paradigms. Specifically, different behavioral phenomena emerge in decisions under risk and decisions under ambiguity, in decisions from description and decisions from experience, and in choices between binary gambles and choices between multi-outcome gambles. Previous research addresses these distinctions by proposing different models that assume different processes and rely on different theoretical approaches to capture the different anomalies. This paper evaluates an alternative solution by developing a general model that captures the coexistence and relative importance of the contradicting tendencies shown to emerge in different settings. Three steps were taken to reduce the risk of overfitting the data. First, we replicated 14 classical anomalies in one experimental paradigm. Next, we studied 60 problems randomly selected from a space that includes all problems examined in the replication study. Finally, to exclude arbitrary selection of feasible models, an open choice prediction competition was organized. The organizers (the first three co-authors) presented their favorite model and challenged other researchers to develop better models. Models were evaluated based on their predictions of 60 new problems. The results suggest that the classical “pre-feedback” phenomena are replicable, but that feedback eliminates most of them, and instigates the choice of the prospect that minimizes the probability of regret. The models that best capture the results assume: (a) high sensitivity to the best estimates of the expected values, (b) the use of several feedback-dependent heuristics, and (c) reliance on small samples.


En savoir plus sur l'invité : Site personnel, Google Scholar