Strategic Choices and Routing Within Service Networks: Modeling and Estimation Using Machine Learning

Abstract

Customer decision-making is difficult to study empirically when choice sets are combinatorially complex. For example, in service networks such as physical marketplaces, shopping centers, and amusement parks, customers can choose from combinatorially many network paths that reflect their motivations to visit the stations they like while dynamically avoiding congestion (e.g., by delaying visiting a temporarily crowded store or attraction). Existing methods suffer serious issues when estimating the preferences regarding consumption and waiting that drive customers' network paths. We address this problem by leveraging seminal developments from discrete optimization and machine learning. We prove that customers need only make a small number of "local" choice comparisons in order to confirm that their choices are optimal; moreover, we need only analyze such comparisons in order to maximally learn customer preferences from data reporting their network paths. Because neural networks excel at identifying such hidden low-dimensional structure, we leverage them to build estimators that discriminate between, hence identify, customer types from their choices of paths. By consistently and tractably estimating customer preferences, our empirical methods enable deeper analyses of service network or marketplace design, capacity management, and customer targeting.

Keywords: Adversarial estimation; Consumer choice; Empirical operations management, Machine learning, Marketplace design, Neural networks, Queueing, Service networks, Structural estimation