Peng Shi, the Robert R. Dockson Assistant Professor in Business Administration, was awarded the INFORMS Frederick W. Lanchester Prize for the “best contribution to operations research and the management sciences published in English in the past five years” at the fall INFORMS Annual Meeting.
The prize recognized a collection of two papers that focused on aspects of matching markets; Shi received the award as a part of a team of five scholars including Itai Ashlagi (Stanford University), Mark Braverman (Princeton University), Yash Kanoria (Columbia University), and Jacob Leshno (Columbia University).
“I am very honored to receive this prize with my co-authors, and I am very thankful for this recognition by the community,” Shi remarked when learning he was named one of the recipients for 2024.
The assistant professor of data sciences and operations is interested in developing quantitative methodologies for the betterment of society. His current research focuses on optimization in matching markets, with applications in school choice, public housing, and two-sided marketplaces. The latter caught the attention of INFORMS.
Shi, with Ashlagi, Braverman, Kanoria, co-authored the second paper of the prize, “Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations,” first published in Management Science in May 2020 (Volume 66, Issue 5).
This research studied how to reduce congestion in two-sided matching markets with private preferences. A “matching market” is any type of system that enables people to match with one another such as job candidates who match their qualifications with a company’s openings. This could entail an individual needing to send hundreds of applications, flooding employers’ inboxes with many thousands of resumes to screen from a multitude of applicants.
“All of these applications take a toll on the two sides, as applying to many jobs takes time and effort, and reading thousands of resumes requires effort from the employer’s side,” Shi explained.
The “congestion” places stress on the marketplace to process all of the back-and-forth communication that could complicate reaching a mutually beneficial outcome.
“Due to the congestion, good candidates may not get hired in the places that fit them, and employers may not get the best candidates they can get,” Shi added.
Shi’s paper provides key recommendations on how to improve the efficiency of matching markets using information theory. Focused on guiding design decisions, the researchers determined how certain tools, such as “match recommendations” (the marketplace recommends a well-suited candidate) and “informative signals” (candidates and companies indicate what they are specifically seeking), should be constructed to optimize the matching process.
Building on the award-winning research, Shi published a subsequent paper in Management Science in March 2023 (Volume 69, Issue 3) titled, “Optimal Matchmaking Strategy in Two-Sided Marketplaces.” This research applied the same methodology to maximize “search and matching interface” strategies, allowing online marketplaces such as HomeAdvisor, Yelp, Google Local Businesses, and Thumbtack, among others, to match consumers to service providers effectively.
