11/7/24 | 4:15pm | E51-149
Jiayu (Kamessi) Zhao
Winner of 2024 ORC Best Student Paper Competition
MIT
Abstract: Flexibility is a cornerstone of operations management, crucial to hedge stochasticity in product demands, service requirements, and resource allocation. In two-sided platforms, flexibility is also two-sided and can be viewed as the compatibility of agents on one side with agents on the other side. Platform actions often influence the flexibility on either the demand or the supply side. But how should flexibility be jointly allocated across different sides? Whereas the literature has traditionally focused on only one side at a time, our work initiates the study of two-sided flexibility in matching platforms. We propose a parsimonious matching model in random graphs and identify the flexibility allocation that optimizes the expected size of a maximum matching. Our findings reveal that flexibility allocation is a first-order issue: for a given flexibility budget, the resulting matching size can vary greatly depending on how the budget is allocated. Moreover, even in the simple and symmetric settings we study, the quest for the optimal allocation is complicated. In particular, easy and costly mistakes can be made if the flexibility decisions on the demand and supply side are optimized independently (e.g., by two different teams in the company), rather than jointly. To guide the search for optimal flexibility allocation, we uncover two effects — flexibility cannibalization and flexibility asymmetry — that govern when the optimal design places the flexibility budget only on one side or equally on both sides. In doing so we identify the study of two-sided flexibility as a significant aspect of platform efficiency.
Bio: Kamessi Zhao is a final year PhD student at MIT Operations Research Center, where she is advised by Prof. Daniel Freund. Prior to PhD, she graduated from Columbia University in 2020 with a B.S. degree in Operations Research. Her research focuses on how two-sided platforms can incentivize flexibility on both market sides to enhance operational efficiency. Her recent works study (i) the interactions among flexibility incentives on different sides of platforms, and (ii) the implications of flexible operations for market design, e.g., developing incentive-compatible methods to deploy autonomous vehicles through gig economy platforms. In tackling these challenges, her works combine tools from game theory, stochastic decision-making, and online algorithms. In the summers of 2023 and 2024, she served as an applied scientist on the Uber Eats Realtime Pricing Team.