Decision Intelligence for Two-sided Marketplaces
Two-sided marketplaces have emerged as viable business models in many real-world applications, such as ridesharing, retail, vacation rental, and food delivery. In particular, we have entered a paradigm of network with multiple distinct types of participants representing the supply and demand of a specific commodity or service.
This tutorial aims to impart a deep understanding of decision intelligence in two-sided marketplaces, with a particular emphasis on reinforcement learning and sequential decision-making for long-term optimization. Attendees will explore the design and management of such marketplaces, with case studies in ridesharing, and learn to tackle their unique challenges. They will gain practical skills in supply-demand forecasting, dynamic pricing, online matching, growth strategies and A/B testing, applicable to real-world situations. By the end of the tutorial, they should be proficient in applying these techniques and evaluating these systems, a valuable asset for AAAI community members interested in marketplace dynamics and decision intelligence.
Resources
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📢 The recordings are ready in Youtube and Blibli!
Schedule and Recordings
| Topic | Duration | Speaker | Recordings |
|---|---|---|---|
| Foundations of Two-Sided Marketplaces | 35 min | Hongtu Zhu | YouTube Bilibili |
| Optimizing Policies for Strategic Decision-Making | 75 min | Tony Qin | YouTube Bilibili |
| A/B Testing: Policy Evaluation and Experimental Design | 75 min | Chengchun Shi | YouTube Bilibili |
| Leveraging LLMs and Digital Twins for Marketplaces | 20 min | Tony Qin | YouTube Bilibili |
Tutorial Organizers
Zhiwei (Tony) Qin is Co-founder and Chief Scientist of Eva AI (foreva.ai). Previously, he was Principal Scientist at Lyft Rideshare Labs and Director of the Decision Intelligence group at DiDi AI Labs, spearheading the development of reinforcement learning (RL) for rideshare marketplace optimization. Tony received his Ph.D. in Operations Research from Columbia University. He is Associate Editor of the ACM Journal on Autonomous Transportation Systems. He has served as Area Chair/Senior PC of KDD, AAAI, and ECML-PKDD, and a referee of top journals. He is an INFORMS Senior Member, a Franz Edelman Award Finalist and Laureate in 2023, and received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019. He is the lead author of the book``Reinforcement Learning in the Ridesharing Marketplace `` published by Springer.
Chengchun Shi is an Associate Professor in the Department of Statistics at LSE. He works at the interface of RL and statistics, with applications to ride-sharing and healthcare. His work brings to light the relevance and significance of statistical learning in RL, and demonstrates the usefulness of RL as a framework for policy evaluation and A/B testing in two-sided marketplaces. Chengchun has published approximately 70 papers, with half of them accepted in prestigious statistical journals (JRSSB, JASA, AoS) and top machine learning venues (NeurIPS, ICML, KDD, JMLR). His outstanding contributions have been recognized with esteemed awards such as the Institute of Mathematical Statistics Tweedie Award, the Royal Statistical Society Research Prize and ICSA Outstanding Young Research Award. He is serving as the associate editors of JRSSB, JASA and Journal of Nonparametric Statistics. He also serves as a reviewer for a range of machine learning conferences, including NeurIPS, ICML, ICLR, AAAI, AISTATS, and KDD.
Hongtu Zhu is a tenured professor of biostatistics, statistics, computer science, and genetics at University of North Carolina at Chapel Hill. He was DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and was Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He has been a Fellow of American Statistical Association and Institute of Mathematical Statistics since 2011 and Fellow of Institute of Electrical and Electronic Engineers since 2025. He received an established investigator award from Cancer Prevention Research Institute of Texas in 2016 and received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019. He has published more than 350+ papers in top journals including Nature, Science, Cell, Nature Genetics, JMLR, PNAS, AOS, JASA, and JRSSB, as well as 55+ conference papers in top conferences including NeurIPS, ICML, ICLR, AAAI, KDD, ICDM, MICCAI, and IPMI. He is the Editor in Chief of Journal of American Statistical Association, Application and Case Study and Coordinating editor.