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The Value of "Who" and "What" When Predicting Choice Under Risk
We investigate the predictive value-add of auxiliary covariates in a choice under risk setting. We start with a data set representative of the Dutch population, and simulate different levels of data availability by selectively removing demographic covariates, subject identifiers, or both. We use expected utility theory (EUT) as a benchmark model and evaluate its out-of-sample prediction performance against machine learning (ML) models. We show that identifying information is more valuable than demographic data, although both show significant improvement over choice data alone. EUT is competitive with ML models, in particular outperforming them on subjects whose choices are consistent with (monotonic) utility maximization. There is little heterogeneity across demographic groups. Overall, our results demonstrate the predictive power of simple identifying information while emphasizing the continued relevance of EUT amid advances in ML and AI.