Using Methods from Machine Learning to Evaluate Behavioral Models of Choice Under Risk and Ambiguity

28 Pages Posted: 14 Jan 2015 Last revised: 9 Jan 2017

See all articles by Alexander Peysakhovich

Alexander Peysakhovich

Yale University - Human Cooperation Lab

Jeffrey Naecker

affiliation not provided to SSRN; Google

Date Written: January 12, 2015


How can behavioral scientists 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 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 our ML methods essentially “rediscover” expected utility with probability weighting. However, in the case of ambiguity we show that 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.

Keywords: behavioral economics, risk, ambiguity, microeconomics, machine learning, experimental economics

Suggested Citation

Peysakhovich, Alexander and Naecker, Jeffrey and Naecker, Jeffrey, Using Methods from Machine Learning to Evaluate Behavioral Models of Choice Under Risk and Ambiguity (January 12, 2015). Journal of Economic Behavior and Organization, Forthcoming, Available at SSRN: or

Alexander Peysakhovich (Contact Author)

Yale University - Human Cooperation Lab ( email )

New Haven, CT
United States

Jeffrey Naecker

Google ( email )

affiliation not provided to SSRN

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