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Estimating discrete choices under uncertainty typically rely on assumptions of expected utility theory. We build on the dynamic choice modeling literature by using a nonlinear case-based reasoning approach 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 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 the dynamic value of attributes. [ABSTRACT FROM AUTHOR]