publications
preprints
- In progressSizing before Testing: Incentives and the Value of Pre-Experiment InformationGuoxing He, Zhen Lian, and Feng Tian2026In preparation for submission
- Working PaperLearning Pay Strategies with Small Samples in Gig Economy PlatformsArthur Delarue, Zhen Lian, and Tony Qin2026Under reviewTalks:
- INFORMS Annual Meeting 2025
- RM&P Conference 2025
- MSOM Conference 2025 (by coauthor)
Gig economy platforms operate in dynamic and competitive labor markets in which workers can compare and choose among multiple job offers in real time. A central challenge for these platforms is to set worker compensation when individual workers’ reservation wages are heterogeneous, and when common market-wide factors such as competitor incentives shift workers’ willingness to accept offers. We study a profitmaximizing platform that must serve identical requests by sequentially making takeit-or-leave-it pay offers to a pool of workers, observing only acceptances and rejections. Each worker’s reservation wage consists of an individual component and a shared global factor that is initially unknown to the platform. We first characterize the optimal policy in a full-information benchmark where the global factor is known, and show that the problem can be solved efficiently via dynamic programming. The optimal policy leverages worker heterogeneity by initially offering pay below the myopic optimum, increasing pay following rejections and decreasing it following acceptances, and avoiding a set of dominated pay levels that are never optimal. We then analyze the realistic setting in which the global factor is uncertain and must be inferred from a small number of observations. While the exact belief-augmented solution is complex, we develop simple and interpretable heuristics with provable performance guarantees. In particular, we propose a direct-commit policy and a probe-and-commit policy that use little or no learning to adapt pay to market conditions. Our results provide actionable guidance for gig economy platforms seeking to set transparent, adaptive worker compensation in competitive spot labor markets.
- Working PaperTransparency, Control, and Pay in the Gig Economy: A Game-theoretic PerspectiveZhen Lian, Feng Tian, and Feifan Zhang2026Major revision at Management ScienceTalks:
- MSOM Conference 2025 (by coauthor)
- INFORMS Annual Meeting 2024
- INFORMS RM&P Section Conference 2024
- Marketplace Innovation Workshop 2024
- INFORMS Annual Meeting 2023
The transparency and control of earnings are major concerns for gig economy workers across platforms such as ride-hailing and food delivery. While workers advocate for greater transparency, platforms selectively disclose information, shaping workers’ decision-making and earnings. Recently, the Federal Trade Commission (FTC) has highlighted lack of transparency as a key issue, and platforms have responded by introducing upfront pay quotes that provide per-trip compensation details for workers. Using a game-theoretic model, we analyze the strategic interactions between platforms and workers, incorporating tools from information design to examine how different transparency policies—specifically, flat commission rates versus upfront quote—shape equilibrium outcomes. We find that greater transparency can paradoxically increase platform control, as it allows platforms to fine-tune pay structures in ways that ultimately reduce worker autonomy. Moreover, while full information benefits the platform when it has flexibility in commission setting, it can backfire under commitment constraints, leading to lower profits than a no-information policy. Our findings highlight that transparency is not inherently beneficial for workers. Instead, its effects depend on how it interacts with pay policies. In particular, simple mechanisms, such as a fixed commission rate, can provide workers with more stability and bargaining power than per-trip transparency. These insights offer important guidance for policymakers and platform designers navigating the trade-offs of transparency in the gig economy.
- Working PaperLabor Cost Free-Riding in the Gig EconomyZhen Lian, Sebastien Martin, and Garrett van Ryzin2026Major revision at Management ScienceAwards:
- Honorable mention, RMP 2021 Jeff McGill Student Paper Award
Talks:- 2024: Stanford OIT seminar
- 2023: University of Toronto, Rotman School of Business Seminar
- 2022: Lyft Rideshare Labs, Boston University, Purdue University, Yale SOM, UC Berkeley, UNC Chapel Hill, Johns Hopkins University, Northwestern Kellogg, NYU Stern, MIT Sloan, Rice University
- 2021: Boston College, Wharton, INFORMS Annual Meeting, Cornell OTIM Workshop, Cornell ORIE PhD Colloquium, CMU YinzOR 2021, EC’21 (Poster), Columbia PhD Brown Bag, INFORMS RM&P Section Conference, MSOM Conference
We propose a theory of gig economies in which workers participate in a shared labor pool utilized by multiple firms. Since firms share the same pool of workers, they face a trade-off in setting pay rates; high pay rates are necessary to maintain a large worker pool and thus reduce the likelihood of lost demand, but they also lower a firm’s profit margin. We prove that larger firms pay more than smaller firms in the resulting pay equilibrium. These diseconomies of scale are strong too; firms smaller than a critical size pay the minimal rate possible (the workers’ reservation wage), while all firms larger than the critical size earn the same total profit regardless of size. This scale disadvantage in labor costs contradicts the conventional wisdom that gig companies enjoy strong network effects and suggests that small firms have significant incentives to join an existing gig economy, implying gig markets are highly contestable. Yet we also show that the formation of a gig economy requires the existence of a large firm, in the sense that an equilibrium without any firms participating only exists when no single firm has enough demand to form a gig economy on its own. The findings are consistent with stylized facts about the evolution of gig markets such as ride sharing.
- Working PaperConsumer Status Signaling, Wealth Inequality, and the Dupe EconomyLi Chen, Zhen Lian, and Shiqing Yao2023Under review at Production and Operations ManagementTalks:
- INFORMS Annual Meeting 2019
- POMS Conference 2019
The growing popularity of "dupes"—lower-priced knockoffs (or duplicates) that consumers knowingly purchase—has reshaped how luxury consumption is used for status signaling. By allowing consumers to mimic the visible consumption of luxury goods without paying the full price premium, dupes challenge the traditional role of luxury products in conveying social status and raise new managerial and regulatory concerns about the emerging dupe economy. Motivated by these industry dynamics, we consider a market entry-deterrence game between an incumbent status product firm (the firm) and a dupe entrant. The market demand is endogenously determined by a consumer status signaling subgame. We show that in the absence of dupe entry market intervention, consumer status signaling can improve the firm’s profit up to a saturation point, and high wealth inequality benefits the firm. We further show that these "free-market" insights may reverse under strong market intervention that penalizes a potential dupe entry, where both status signaling and wealth inequality may make the firm worse off. Finally, we characterize the welfare-maximizing market intervention and show it can restore the positive value of status signaling but may also come with a high social cost of enforcement.
publications
- Management ScienceAlgorithmic precision and human decision: A study of interactive optimization for school schedulesArthur Delarue, Zhen Lian, and Sebastien MartinManagement Science, 2026Awards:
- Semi-finalist for Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research
Talks:- Transportation And Logistics Early-Career North-American (TALENT) Workshop 2025
- Operations Seminar, Zicklin School of Business, Baruch College 2025
- Yale SOM Faculty Seminar 2024
- ACM Conference on Economics and Computation 2024 (EC’24)
- Cornell Johnson Summer Symposium 2024
- MSOM Conference Sustainability Special Interest Group (SIG) 2023
- The University of Hong Kong (HKU) Operations Seminar 2023
- The Chinese University of Hong Kong in Shenzhen (CUHK-Shenzhen) Operations Seminar 2023
Media coverage:In collaboration with the San Francisco Unified School District (SFUSD), this paper introduces an interactive optimization framework to tackle complex school scheduling challenges. The choice of school start and end times is an optimization challenge, as schedules influence the district’s transportation system, and limiting the associated costs is a computationally difficult combinatorial problem. However, it is also a policy challenge, as transportation costs are far from the only consequence of school schedule changes. Policymakers need time and knowledge to balance these considerations and reach a consensus carefully; past implementations have failed because of policy issues, despite state-of-the-art optimization approaches. We first motivate our approach with a microfoundation model of the interplay between policymakers and researchers, arguing that limiting their dependency is key. Building on these insights, we propose a framework that includes (1) a fast algorithm capable of solving the school schedule problem that compares favorably to the literature and (2) an interactive optimization approach that leverages this speed to allow policymakers to explore a variety of solutions in a transparent and efficient way, facilitating the policy decision-making process. The framework led to the first optimization-driven school start time changes in the United States, updating the schedule of all 133 schools in SFUSD in 2021, with annual transportation savings exceeding $5 million. A comprehensive survey of approximately 27,000 parents and staff in 2022 provides evidence of the approach’s effectiveness.
- Management ScienceCapturing the benefits of autonomous vehicles in ride hailing: The role of market configurationZhen Lian and Garrett van RyzinManagement Science, 2025Talks:
- RM&P Conference 2021
- MSOM Conference 2021
- Marketplace Innovation Workshop 2021
- POMS Conference 2021
- INFORMS Annual Meeting 2020
- Cornell OTIM Workshop 2020
- INFORMS Annual Meeting 2019
Media coverage:-
Will Self-Driving Cars Lower Ride-Hailing Prices?, Yale Insights
We develop an economic model of autonomous vehicle (AV) ride-hailing markets in which uncertain aggregate demand is served with a combination of a fixed fleet of AVs and an unlimited potential supply of human drivers (HVs). We analyze market outcomes under two dispatch platform structures (common platform vs. independent platforms) and two levels of supply competition (monopoly AV vs. competitive AV). A key result of our analysis is that the lower cost of AVs does not necessarily translate into lower prices; the price impact of AVs is ambiguous and depends critically on both the dispatch platform structure and the level of AV supply competition. In the extreme case, we show if AVs and HVs operate on independent dispatch platforms and there is a monopoly AV supplier, then prices are even higher than in a pure HV market. When AVs are introduced on a common dispatch platform, we show that whether the equilibrium price is reduced depends on the level of AV competition. If AVs are owned by a monopoly firm, then the equilibrium price is the same as in a pure HV market. In fact, the only market structure that leads to unambiguously lower prices in all demand scenarios is when AVs and HVs operate on a common dispatch platform and the AV supply is competitive. Our results illustrate the critical role dispatch platform and market structure play in realizing potential welfare gains from AVs.
- Case StudyEnabling X-to-the-Xth: The Operational Secret Behind Uber’s Explosive GrowthDaniel Guetta, Zhen Lian, and Garrett van Ryzin2023Springer, Columbia Caseworks
- Management ScienceOptimal growth in two-sided marketsZhen Lian and Garrett van RyzinManagement Science, 2021Talks:
- RM&P Conference 2019
- NYU Stern Sharing Economy Seminar 2019
- Cornell Johnson OTIM Workshop 2019
- INFORMS Annual Meeting 2018
We develop a theoretical model of optimal growth in two-sided markets. The model posits that market output (number of transactions) is a function of the stock of supply and demand. This market output is modeled using a homogeneous production function, which can have increasing or decreasing returns to scale. The supply and demand stock levels follow a growth model in which the rate of growth at each point in time is a function of both the surplus each side of the market receives and the attrition of supply and demand (supply and demand lifetimes). The surplus can be apportioned between the two sides of the market by changing the price paid to sellers and the price charged to buyers, which we assume the platform controls. Through these price levers, the platform can pay subsidies to one or both sides of the market. We investigate the behavior of optimal market growth, including the point at which the market becomes self-sustaining and the long-run optimal size of the market. We characterize the optimal balance between supply and demand as the market size grows and determine optimal subsidy policies that maximize discounted total profit. For the case of both increasing and decreasing returns without price constraints, we show the optimal policy is to grow using an impulse of subsidy spending (a subsidy shock) to move the market immediately to its optimal long-run size. This result is consistent with the “race to growth” observed in many two-sided markets like ride-sharing.
- LangmuirGeneral approach to construct photoresponsive self-assembly in a light-inert amphiphilic systemQiang Zhao, Zhen Lian, Xuedong Gao, and 2 more authorsLangmuir, 2016