Published/Accepted papers

1. Algorithmic Precision and Human Decision: A Study of Interactive Optimization for School Schedules. Joint work with Arthur Delarue and Sébastien Martin. Management Science, Special Issue on Human-Algorithm Connection. 72(1):148-166.

   Management Science SSRN Working Paper Media Abstract

   • Semi-finalist for Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research
   • Accepted by the Twenty-Fifth ACM Conference on Economics and Computation (EC'24).
   • Accepted for presentation at MSOM Special Interest Group (SIG) 2023.
   • This Yale Insights article breaks down why adjusting school start times is surprisingly complicated and how our method helps schools find more workable schedules.

   Abstract: Motivated by a collaboration with the San Francisco Unified School District (SFUSD), this paper presents an interactive optimization framework for addressing complex pub- lic policy problems. These problems suffer from a chicken-and-egg dilemma, where policymakers understand the objectives and constraints but lack the ability to solve them (“the optimization problem”), while researchers possess the necessary algorithms but lack the necessary insights into the policy context (“the policy problem”). Our framework addresses this challenge by combining three key elements: (1) an efficient optimization algorithm that can solve the problem given certain known objectives, (2) a method for generating a large set of diverse, near-optimal solutions, and (3) an inter- face that facilitates exploration of the solution space. We illustrate the effectiveness of this framework by applying it to the problem of improving school schedules at SFUSD. The resulting schedule, implemented in August 2021, saved the district over $5 million and, to our knowledge, represents the first successful optimization-driven school start time change in the United States.
2. Capturing the Benefits of Autonomous Vehicles in Ride-Hailing: The Role of Dispatch Platforms and Market Structure. Joint work with Garrett van Ryzin. Management Science. 71(7):5491-5510.

   Management Science SSRN Working Paper Media Abstract

   • See this Yale Insights article for a plain-language overview of what’s driving robotaxi prices and how companies like Waymo, Tesla, Uber, and Lyft might work together in the future.

   Abstract: 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.
3. Optimal Growth in Two-sided Markets. Joint work with Garrett van Ryzin. Management Science, 67(11):6862-6879.

   Management Science SSRN Working Paper Talk at Simons Institute (by Garrett) Abstract

   Abstract: 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.

Papers under review/revision

4. Learning Pay Strategies with Small Samples in Gig Economy Platforms. Joint work with Arthur Delarue and Tony Qin. Under review.

   SSRN Abstract

   Abstract: 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 take-it-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.
5. Transparency, Control, and Pay in the Gig Economy: A Game-theoretic Perspective. Joint work with Feng Tian and Feifan Zhang. Under major revision at Management Science.

   SSRN Abstract

   Abstract: 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.
6. Labor Cost Free-Riding in the Gig Economy. Joint work with Sébastien Martin and Garrett van Ryzin. Under major revision at Management Science.

   SSRN Abstract

   • Honorable mention, RMP 2021 Jeff McGill Student Paper Award

   Abstract: 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.
7. Consumer Status Signaling, Wealth Inequality, and the Dupe Economy. Joint work with Li Chen and Shiqing Yao. Under review at at Production and Operations Management.

   SSRN Abstract

   Abstract: 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.