UC Berkeley

Modern marketplaces and societal systems are undergoing substantial transformations due to automated data collection and algorithmic decision-making. In response, my research focuses on developing methodologies and algorithms for sequential and data-driven decision-making, with a particular emphasis on modeling and analyzing human behavior within systems. To achieve this goal, I employ an eclectic set of techniques from statistics, optimization, and machine learning, while drawing on insights from economic theories and marketing literature. My dissertation contributes to the field of sequential and data-driven decision-making by addressing important real-world problems and developing novel methodologies and algorithms that bridge theory and practice.

Reference effects are fundamental consumer behavior models with roots from the renowned prospect theory in economics and have been widely observed in reality. Due to the existence of reference price effects by consumers, retailers need to be aware of the sequential effects of current prices on future demand when designing their dynamic pricing policies. In the first part of the dissertation, we build a random coefficient logit demand model that allows arbitrary joint distributions of valuations, responsiveness to prices, and responsiveness to reference prices among consumers to fully express consumer heterogeneity. We develop a nonparametric estimation method to learn heterogeneous consumer reference effects from transaction data, and we apply a modified policy iteration algorithm to find the optimal pricing policies. We verify the effectiveness of our approach through numerical experiments on large-scale individual-level transaction data from the online retailer JD.com.

Mixture of regression models are useful for regression analysis in heterogeneous populations, and have been applied in diverse fields including biology, economics, engineering, epidemiology, marketing, and transportation. In the second part of the dissertation, we study the nonparametric maximum likelihood estimator (NPMLE) for fitting these models, and notably our approach does not require prior specification of the number of mixture components. We establish existence of the NPMLE and prove finite-sample parametric Hellinger error bounds for the predicted density functions. We also provide an effective procedure for computing the NPMLE without ad-hoc discretization and prove a theoretical convergence rate under certain assumptions. This work provides the theoretical foundation for the nonparametric estimation method used in the first part of the dissertation for estimating consumer heterogeneity.

Aside from online platforms like retailing, another type of platform that is receiving increasing attention comes from sharing services such as ride-hailing and vehicle sharing. On-demand and one-way vehicle sharing (e.g., car sharing, bike sharing, scooter sharing) services are emerging and environment-friendly transportation options that promise consumers more flexibility and convenience. Despite the benefits of vehicle sharing, such services still suffer from many operational difficulties and struggle to thrive. One major challenge in operating such vehicle sharing systems is matching the supply and the demand in real time across multiple locations. In the third part of the dissertation, we consider how to dynamically reposition vehicles in order to minimize the total costs of repositioning and lost sales in the long run. The repositioning problem is critical in successful management of on-demand one-way vehicle sharing services, and it is challenging both analytically and computationally. The optimal repositioning policy under a general $n$-location network is inaccessible without knowing the optimal value function. Inspired by the base-stock reorder policy in inventory control, we propose a class of base-stock repositioning policies and prove that they are asymptotically optimal in certain limiting regimes.