A new approach to inferring hierarchical models of consumer choice is described. A classification algorithm is used to estimate decision trees at an individual level without requiring prior assumptions about tree form. Derived models are analyzed within a modeling system that summarizes the diversity of decision rules in a sample as well as their implications for aggregate market shares. An application to the analysis of panel data and a comparison with disaggregate logit analysis are reported.
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