Research Interest
Methodology: game theory, modelling and design of service systems, optimization, data mining using econometric and statistical tools, structural estimation.
Application: supply chain management, pricing and revenue management, platform economics, sustainable operations.
Publication (*stands for a student coauthor)
Journal Publications
"Navigating Traceability: How Pricing and Responsibility Sharing Affect Product Quality and Supply Chain Welfare" with Ruxian Wang and Xinyi Zhou*, Production and Operations Management, Forthcoming. [AI Summary Video]
Honorable mention, POMS-HK Best Student Paper Competition, 2023"The Value of Adding On-demand Service with Price- and Waiting-Sensitive Customers" with Xin Weng*, Fiona Fu*, and Li Xiao, IISE Transactions, Forthcoming.
"Efficient Frontier and Applications in Product Offering and Pricing" with Chenxu Ke and Ruxian Wang, 2025, Manufacturing & Service Operations Management. [AI Summary Video]
"Information Sharing and Financing Services on Online Retailing Platforms" with Xinru Hu* and Jianbin Li, 2025, Production and Operations Management. [Summary Video]
"Dual Sourcing: Creating and Utilizing Flexible Capacities with a Second Supply Source" with Zhe Liu and Awi Federgruen, 2022, Production and Operations Management. [AI Summary Video]
2nd Place, POMS College of Supply Chain Management Student Paper Competition, 2018.
2nd Place, POMS-HK Best Student Paper Competition, 2019"Synthesis and Generalization of Structural Results in Inventory Management: A Generalized Convexity Property" with Zhe Liu and Awi Federgruen, 2020, Mathematics of Operations Research. [AI Summary Video]
Finalist, George Nicholson Student Paper Competition, 2018"Assessing the Impact of Service Level when Customer Needs are Uncertain: An Empirical Investigation of Hospital Step-Down Units" with Carri W. Chan, Linda V. Green, and Gabriel Escobar, 2019, Management Science. [AI Summary Video]
"Framework Agreement in Procurement: An Auction Model and Design Recommendations" with Yonnata Gur and Gabriel Weintraub, 2018, Manufacturing & Service Operations Management. [AI Summary Video]
"Optimal and Asymptotically Optimal Policies for Assemble-to-Order N- and W- Systems" with Jing-sheng Song and Hanqing Zhang, 2016, Naval Research Logistics.
"Capacity Investment Decisions under Risk Aversion" with Xiaoming Yan, 2016, Naval Research Logistics.
"Preferences on Contractual Forms in Supply Chains" with Yaozhong Wu, 2015, European Journal of Operational Research.
Papers under review and revision
"The Power of Reactive Upgrades in Dynamic Resource Allocation with General Upgrading" with Zheng Cui and Daniel Zhuoyu Long, major revision.
2nd Prize, The 20th International Conference on Service Systems and Service Management, 2025"Service Deployment in the On-Demand Economy: Employees, Contractors, or Both? " with Xin Weng* and Li Xiao, major revision.
"Pricing and Information Provision in Service Platforms with Heterogeneous Customers" with Xin Weng* and Li Xiao, minor revision.
"Price Competition Based on Relative Prices" with Awi Federgruen, major revision.
"Fake It Till You Fund It: The Rise of Exploitative Practices in Online Crowdfunding Marketplaces" with Xinru Hu* and Jianbin Li, major revision.
Honorable mention, POMS-China Best Student Paper Competition, 2025
Selected working papers and work-in-progress
“Efficient Market Design under Projection Bias: Rent Placement and Platform Governance” [AI Summary Video]
Abstract: Marketplace auctions are typically designed as if bidders and the platform evaluate market states under a common probability model. We study market design when they do not. Conditional on efficient allocation and statewise individual rationality, probability disagreement creates a new design margin: the platform chooses both the amount and the location of incentive rent. A likelihood-ratio ranking identifies states where perceived rent is objectively cheap, but this pointwise logic is generally insufficient because rent assigned to one report also affects mimics' deviation payoffs. We establish compatibility conditions under which the cheapest state for an intended type is also least attractive to potential mimics, reducing the state-contingent problem to perceived rent levels. Projection bias satisfies these conditions and yields a unique efficient payment rule: truthful bidding is a subjective Bayesian equilibrium, winners facing different types pay their full values, and rent is concentrated on own-type matching states. Revenue decomposes exactly into rational mechanism optimization and a behavioral increment attributable to probability disagreement; the latter eventually vanishes as competition thickens. We further characterize implementation and governance boundaries arising from no-subsidy constraints, bid manipulation, endogenous allocation, repeated participation, and bid-band implementation. A calibration to 1,000 eBay U.S. Mint proof-set auctions disciplines magnitudes rather than identifies projection: at the four-bidder benchmark, projection contributes about $1.80 per item, while most of the gain over a second-price auction reflects ordinary mechanism optimization. The results identify when probability disagreement creates economically meaningful design value and when market structure or governance dissipates it."Channels as Data-Acquisition Technologies: Servitization, Pricing, and Privacy Governance" with Jiayi Zhu* and Lei Yang.
Abstract: Firms increasingly obtain operational data through continuing service relationships such as leasing, subscriptions, and managed-service contracts. We study how this data-acquisition role changes selling-versus-leasing channel design and intertemporal pricing, and how privacy governance changes the resulting service channel. We develop a two-period model in which leasing creates a continuing relationship that generates data. These data improve subsequent service, while identifiable data can additionally generate commercialization revenue. Data value gives the leasing relationship a shadow acquisition value: the firm may lower the introductory rent to enlarge the data-producing relationship base and recover this subsidy through improved future service, continuation pricing, or data commercialization. This channel-data-service feedback can overturn durability-based channel prescriptions. We distinguish iterative products, for which access is naturally aligned with intertemporal value creation, from stable products, for which shifting customers from ownership to leasing can destroy continuation value. We further distinguish data-scope restrictions, which change the return to acquired data, from disclosure requirements, which make privacy consequences salient before channel choice and thereby change formation of the data-producing base. Service-channel architecture, introductory pricing, AI capability, and data governance should be designed jointly. Privacy policy should distinguish restrictions on data use from disclosure: both can protect consumers, but they intervene at different points in the channel-data-service feedback and therefore have different service and welfare consequences."From Optimization to Satisficing: Robust Screening under Distributional Ambiguity" with Shumin Ma and Daniel Zhuoyu Long.
Abstract: This study investigates a robust screening problem under distributional ambiguity, where a seller is uncertain about a buyer's true valuation distribution, knowing only that it lies near a reference distribution measured by the Wasserstein metric. Traditional robust optimization (RO) approaches prioritize maximizing worst-case revenue within predefined ambiguity sets, often yielding seller-centric outcomes and reliance on precise set specifications. We propose a robust satisficing (RS) framework aimed at attaining a specified revenue target by minimizing the worst-case shortfall across all potential distributions. Our approach offers a tractable formulation and detailed characterization of optimal mechanisms using randomized pricing strategies. We also assess the out-of-sample efficacy of a simple posted pricing mechanism, finding it particularly effective with lower targets and positively skewed valuations, where smaller valuations have high probability mass. Comparing RO with RS, we find that RS consistently enhances buyer surplus when the reference distribution has an increasing hazard rate and increases out-of-sample seller revenue with positively skewed true valuations. Our analysis indicates that a target-driven RS framework enhances buyer surplus and fairness by offering more opportunities to lower-valuation buyers, potentially boosting overall revenue in scenarios with demand skewed toward these valuations. This approach offers a practical and viable modeling alternative to conventional RO methods, effectively overcoming the challenges of ambiguity set calibration while ensuring broader equitable access for diverse buyers."When Ethics Meet Algorithms: Corporate Social Responsibility in Personalized Pricing and Network Effects".
Abstract: As big data and advanced analytics become more prevalent, companies are increasingly adopting personalized pricing strategies. While these strategies can boost profitability, they also raise concerns about fairness and consumer privacy, often leading to reduced price transparency. This paper explores how corporate social responsibility (CSR) initiatives can reconcile the tension between profit-driven personalized pricing and consumer welfare in digital markets characterized by network effects and price (un)observability. We show that, without CSR, price discrimination can diminish consumer surplus and firm profits due to coordination failures among consumers stemming from network externalities and price opacity. Our findings reveal that CSR initiatives -- especially those that factor consumer surplus into pricing -- create a self-regulating mechanism that: (1) aligns consumer incentives, (2) enhances firm profits beyond standard discriminatory pricing, and (3) leads to Pareto improvements by increasing surplus for all market participants. We validate our findings across various contexts, including different network structures and sequential purchasing, illustrating their generalizability and robustness. These results underscore the vital role of CSR in fostering efficiency and sustainability in personalized pricing. Our findings offer managers a strategic framework for ethical pricing that strengthens competitive positioning in digital markets and provide valuable guidance for policymakers, suggesting that CSR incentives can effectively address concerns related to algorithmic fairness."Price Fairness in Networked Markets: Regulatory Approaches to Third-Degree Price Discrimination".
Abstract: The rise of extensive customer data and advancements in big data analytics have popularized discriminatory pricing strategies in digital marketplaces. While these strategies can benefit firms by exploiting customer heterogeneity, they raise significant concerns about unfairness, inequity, and social injustice. There is a consensus that regulatory interventions are necessary to address these issues. This paper examines the impacts of price fairness regulations within the context of third-degree price discrimination and network effects, focusing on price, demand, firm profit, consumer surplus, and social welfare. Utilizing a game-theoretical model, we analyze the economic effects of implementing price fairness regulations. Our findings indicate that when consumer valuations are highly heterogeneous and network effects are present, such regulations can lead to a Pareto efficient outcome, enhancing market demand, firm profit, and consumer surplus. Conversely, when valuations are similar, fairness regulations may reduce market demand, negatively affecting both firms and consumers, even in heterogeneous network conditions. We further validate our findings by exploring various scenarios with different network structures and information dynamics. Our research highlights an intriguing interaction between heterogeneous network effects and valuations, identifying critical factors that influence the effectiveness of price fairness regulations. These insights offer valuable guidance for policymakers and managers in developing fair pricing practices that align business interests with consumer welfare."Optimizing Assemble-to-Order Systems: Decomposition Heuristics and Scalable Algorithms" with Shuyu Chen*, Jeannette Song and Hanqin Zhang.
"Competitive Option Offerings Under Supply Disruptions" with Xiaotong Liu*, Max Shen, and Li Xiao.
"Medicare Reform: Estimation of Impacts of Premium Support Systems" with Awi Federgruen.
"Oligopolistic Competition in Online Marketplaces: Equilibrium Analysis and System Coordination" with Guillermo Gallego and Xinyi Zhou*.