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Forecasting economic decisions under risk: The predictive importance of choice-process data

Steffen Mueller, Patrick Ring () and Maria Schmidt
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Patrick Ring: Social and Behavioral Approaches to Global Problems, Kiel Institute for the World Economy
Maria Schmidt: Department of Psychology, Kiel University

No 66, Working Papers from Chair for Economic Policy, University of Hamburg

Abstract: We investigate various statistical methods for forecasting risky choices and identify important decision predictors. Subjects (n=44) are presented a series of 50/50 gambles that each involves a potential gain and a potential loss, and subjects can choose to either accept or reject a displayed lottery. From this data, we use information on 8800 individual lottery gambles and specify four predictor-sets that include different combinations of input categories: lottery design, socioeconomic characteristics, past gambling behavior, eye-movements, and various psychophysiological measures that are recorded during the first three seconds of lottery-information processing. The results of our forecasting experiment show that choice-process data can effectively be used to forecast risky gambling decisions; however, we find large differences among models’ forecasting capabilities with respect to subjects, predictor-sets, and lottery payoff structures.

Keywords: Forecasting; lottery; risk; choice-process tracing; experiments; machine learning; decision theory (search for similar items in EconPapers)
JEL-codes: C44 C45 C53 D87 D91 (search for similar items in EconPapers)
Pages: 76 pages
Date: 2019-01-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-exp and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published in Hamburg Contemporary Economic Discussions, Issue 66, 2019

Downloads: (external link) First Version, 2019 (application/pdf)

Related works:
Working Paper: Excited and aroused: The predictive importance of simple choice process metrics (2020) Downloads
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