TITLE: The Limit of Rationality in Choice Modeling
Choice-based demand models based on the random utility maximization (RUM) principle are routinely used in academic literature and industry practice. However, the RUM principle may be violated in practice because customer preferences may not be rational. This raises the following empirical questions: (a) Given a dataset consisting of offer sets and individual choices, are the observed choice probabilities consistent with the RUM principle? (b) If not, what is the degree of inconsistency?
We formulate the problem of quantifying the limit of rationality (LoR) in choice modeling applications. Computing LoR is intractable in the worst case, but we identify the source of complexity through new concepts of rational separation and choice graph. By exploiting the graph structure, we provide practical methods to compute LoR efficiently for a large class of applications. Applying our methods to real-world grocery sales data, we identify product categories for which going beyond rational choice models is necessary to obtain acceptable performance.
Joint work with Srikanth Jagabathula (NYU)
BIO: Paat Rusmevichientong is a Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. His research interests focus on stochastic optimization problems with applications to market systems, supply chain, and revenue management. For more information, see www-bcf.usc.edu/~rusmevic