Forecasting economic decisions under risk: The predictive importance of choice-process data - Steffen Q. Mueller; Patrick Ring; Maria Schmidt (University of Hamburg, Faculty of Business, Economics and Social Sciences, Chair for Economic Policy, 2019)
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: C44; C45; C53; D87; D91; 330; Forecasting; lottery; risk; choice-process tracing; experiments; machine learning; decision theory