Hierarchical maximum likelihood parameter estimation for cumulative prospect theory: Improving the reliability of individual risk parameter estimates

Individual risk preferences can be identified by using decision models with tuned parameters that maximally fit a set of risky choices made by a decision maker. A goal of this model fitting procedure is to isolate parameters that correspond to stable risk preferences. These preferences can be modeled as an individual difference, indicating a particular decision maker's tastes and willingness to tolerate risk. Using hierarchical statistical methods we show significant improvements in the reliability of individual risk preference parameters over other common estimation methods. This hierarchal procedure uses population level information (in addition to an individual's choices) to break ties (or near-ties) in the fit quality for sets of possible risk preference parameters. By breaking these statistical ``ties'' in a sensible way, researchers can avoid overfitting choice data and thus better measure individual differences in people's risk preferences.