Case-based Reasoning and Dynamic Choice Modeling

Priya Thomas and Todd Guilfoos


Estimating discrete choices under uncertainty typically rely on assumptions of the Expected Utility Theory. We build on the dynamic choice modeling literature by using a non-linear case-based reasoning approach that is based on cognitive processes and forms expectations by comparing the similarity between past problems and the current problem faced by a decision maker. This study provides a proof of concept of a behavioral model of angler location choice applied to recreational fishers’ location choice behavior in Connecticut. We find the case-based decision model does well in explaining the observed data and provides value in explaining dynamic value of attributes.