Customer preferences may not be rational, and therefore we focus on quantifying the limit of rationality (LoR) in choice modelling applications. We define LoR as the “cost” of approximating the observed choice fractions from a collection of offer sets with those from the best fitting probability distribution over rankings. Computing LoR is intractable in the worst case. To tackle this challenge, we introduce two new concepts – rational separation and choice graph, using which we reduce the problem to solving a dynamic program on the choice graph and express the computational complexity in terms of structural properties of the graph. By exploiting the graph structure, we provide practical methods to compute LoR efficiently for a large class of applications. We apply our methods to real-world grocery sales data from the IRI Academic Dataset and identify product categories for which going beyond rational choice models is necessary to obtain acceptable performance. – Joint work with: Paat Rusmevichientong, USC Marshall.
Srikanth Jagabathula is Associate Professor of Information, Operations, and Management Sciences at the Leonard N. Stern School of Business at New York University. He is affiliated with the NYU Stern Center for Business Analytics and the NYU Center for Data Science. He received his PhD degree in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. He has received four “Best Paper” awards, a CAREER award from the National Science Foundation, and the IIT Bombay President of India Gold Medal. He works at the intersection of operations management, machine learning, and marketing with a focus on building methodologies for leveraging data to make effective business decisions.
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