A discrete choice model implementing gist-based categorization of alternatives, with applications to patient preferences for cancer screening and treatment

The rational microeconomic decision model is hard-coded into usual econometric specifications such as the Multinomial Logit and Probit models, inter alia. There is a very tight link between utility maximization and the apparatus of welfare theory that underlies economic policy analysis, which creates a tension around the possibility of representing other decision rules. We propose a less restrictive model of choice, built on the concept of gist-based categorization judgments that are assumed to precede (thus, condition) the maximization-driven selection process in decision making. This categorization facilitates decision making by allowing adoption of certain simpler decision rules under appropriate conditions, the drivers of which are endogenously determined. We demonstrate that the proposed model provides better fit than traditional choice models, using cancer screening and treatment choice data from two discrete choice experiments. In addition, we show that the model provides a deeper, more nuanced and insightful perspective on (healthcare) decision making.