Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity

Abstract

How can behavioral science incorporate tools from machine learning (ML)? We propose that ML models can be used as upper bounds for the “explainable” variance in a given data set and thus serve as upper bounds for the potential power of a theory. We demonstrate this method in the domain of uncertainty. We ask over 600 individuals to make a total of 6000 choices with randomized parameters and compare standard economic models to ML models. In the domain of risk, a version of expected utility that allows for non-linear probability weighting (as in cumulative prospect theory) and individual-level parameters performs as well out-of-sample as ML techniques. By contrast, in the domain of ambiguity, two of the most widely studied models (a linear version of maximin preferences and second order expected utility) fail to compete with the ML methods. We open the “black boxes” of the ML methods and show that under risk we “rediscover” expected utility with probability weighting. However, in the case of ambiguity the form of ambiguity aversion implied by our ML models suggests that there is gain from theoretical work on a portable model of ambiguity aversion. Our results highlight ways in which behavioral scientists can incorporate ML techniques in their daily practice to gain genuinely new insights.