A Choice Prediction Competition: Choices From Experience and From Description

DSpace/Manakin Repository

A Choice Prediction Competition: Choices From Experience and From Description

Citable link to this page


Title: A Choice Prediction Competition: Choices From Experience and From Description
Author: Roth, Alvin E.; Erev, Ido; Ert, Eyal; Haruvy, Ernan; Herzog, Stefan; Hau, Robin; Hertwig, Ralph; Stewart, Terrence; West, Robert; Lebiere, Christian

Note: Order does not necessarily reflect citation order of authors.

Citation: Erev, Ido, Eyal Ert, Alvin E. Roth, Ernan Haruvy, Stefan Herzog, Robin Hau, Ralph Hertwig, Terrence Stewart, Robert West, and Christian Lebiere. 2009. A choice prediction competition: choices from experience and from description. Journal of Behavioral Decision Making 23(1): 15-47.
Full Text & Related Files:
Abstract: Erev, Ert, and Roth organized three choice prediction competitions focused on three related choice tasks: one shot decisions from description (decisions under risk), one shot decisions from experience, and repeated decisions from experience. Each competition was based on two experimental datasets: An estimation dataset, and a competition dataset. The studies that generated the two datasets used the same methods and subject pool, and examined decision problems randomly selected from the same distribution. After collecting the experimental data to be used for estimation, the organizers posted them on the Web, together with their fit with several baseline models, and challenged other researchers to compete to predict the results of the second (competition) set of experimental sessions. Fourteen teams responded to the challenge: the last seven authors of this paper are members of the winning teams. The results highlight the robustness of the difference between decisions from description and decisions from experience. The best predictions of decisions from descriptions were obtained with a stochastic variant of prospect theory assuming that the sensitivity to the weighted values decreases with the distance between the cumulative payoff functions. The best predictions of decisions from experience were obtained with models that assume reliance on small samples. Merits and limitations of the competition method are discussed.
Published Version: doi:10.1002/bdm.683
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:5343169
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search