Events & Seminars

This paper considers integrated information-sharing and financing services for a retail platform on which sellers sell products. Online marketplaces such as Amazon and Tmall have been expanding services to boost the growth of their ecosystems. One is information service on sharing privately gathered massive consumer data that is not available to their sellers, and the other is financing service, e.g., Amazon Lending and Ant Financial, aiming to provide accessible and cheap financial support for small and medium sized sellers. We develop a game-theoretical model to examine the impacts of financing on the platform’s information-sharing strategy when sellers are financially constrained. The platform charges a commission fee for each transaction and determines how to share the privately observed demand information which may be contingent on seller’s financing choice. The platform and bank then simultaneously set the loan interest rates, followed by the seller’s selection of loan provider and production quantity. We characterize the equilibrium finance-operations and the optimal information-sharing strategy for the platform. We find that although the seller selects loan provider based on relative financing cost under exogenously given interest rates, she always selects the platform financing under equilibrium. The platform should always share information, but may make it contingent on his financing service which causes double-marginalization in the capital market and hurts supply chain efficiency. We show that such inefficiency could be resolved by charging a fixed-payment for the information-sharing service which leads to a ‘win-win’ outcome. We also examine the impacts of the capital market composition and extend these analytical results and managerial insights to general settings to ensure the robustness. Our findings could provide useful guidance for platform practitioners to design integrated services on the financing and information provision.

This paper studies a simultaneous-search problem in which a player observes the outcomes sequentially, and must pay reservation fees to maintain eligibility for recalling the earlier offers. We use postgraduate program applications to illustrate the key ingredients of this family of problems. We develop a parsimonious model with two categories of schools: reach schools, which the player feels very happy upon joining, but the chance of getting into one is low; and safety schools, which are a safer choice but not as exciting. The player first decides on the application portfolio, and then the outcomes from the schools applied to arrive randomly over time. We start with the extreme case wherein the safety schools always admit the player. We show that it suffices to focus on the last safety school, which allows us to conveniently represent the player's value function by a product form of the probability of entering the last safety period and the expected payoff from then on.

We show that the player's payoff after applications is increasing and discrete concave in the number of safety schools. We also develop a recursive dynamic programming algorithm when admissions to safety schools are no longer guaranteed. We demonstrate instances in which the player applies to more safety schools when either the reservation fee gets higher or the admission probability drops lower, and articulate how these arise from the portfolio optimization consideration. This has strong managerial implications for service providers in devising their reservation fees and admission rates, especially for institutions that are not universally favored by prospective applicants.

Keywords: simultaneous search, dynamic programming, stochastic models, reservation fees

In many practical settings, learning algorithms can take a substantial amount of time to converge, thereby raising the need to understand the role of discounting in learning. We illustrate the impact of discounting on the performance of learning algorithms by examining two classic and representative
dynamic-pricing and learning problems studied in Broder and Rusmevichientong (2012) [BR] and Keskin and Zeevi (2014) [KZ]. In both settings, a seller sells a product with unlimited inventory over T periods. The seller initially does not know the parameters of the general choice model in BR (resp., the linear demand curve in KZ). Given a discount factor ρ, the retailer's objective is to determine a pricing policy to maximize the expected discounted revenue over T periods. In both settings, we establish lower bounds on the regret under any policy and show limiting bounds of Ω(√(1/(1-ρ))) and Ω(√T) when T → ∞ and ρ → 1, respectively. In the model of BR with discounting, we propose an asymptotically tight learning policy and show that the regret under our policy as well that under the MLE-CYCLE policy in BR is O(√(1/(1-ρ))) (resp., O(√T)) when T → ∞ (resp., ρ → 1). In the model of KZ with discounting, we present sufficient conditions for a learning policy to guarantee asymptotic optimality, and show that the regret under any policy satisfying these conditions is O(log(1/(1-ρ))√(1/(1-ρ))) (resp., O(log⁡T √T)) when T → ∞ (resp., ρ → 1). We show that three different policies - namely, the two variants of the greedy Iterated-Least-Squares policy in KZ and a different policy that we propose - achieve this upper bound on the regret. We numerically examine the behavior of the regret under our policies as well as those in BR and KZ in the presence of discounting. We also analyze a setting in which the discount factor per period is a function of the number of decision periods in the planning horizon.

We consider service competition between two platforms, who are assumed to be farsighted, i.e., they consider the chains of reactions following their initial deviation. We first investigate the one- sided competition where the supply-side capacities of two platforms are fixed and then proceed to the two-sided competition where the two platforms are competing on both the supply and demand sides. We aim to derive farsightedly stable outcomes referred as the von Neumann-Morgenstern farsighted stable set (vNM FSS), a problem boiling down to finding the Pareto efficient strategies which indirectly dominate other strategies. To that end, we construct auxiliary decision problems for each platform where they make price decisions for the customers and wage decisions for the workers, subject to a subgame workers-customers equilibrium. We obtain each platform’s price and wage decisions by analyzing the Karush-Kuhn-Tucker conditions. We show that, in sharp contrast to the “winner-take-all” outcome predicted by the Nash equilibrium (myopic) solution concept, both platforms can survive competition under the farsightedly stable outcomes. We also find that, in contrast to the myopic solution which may leave either customers or workers a positive surplus, farsightedness behaviour of platforms fully extracts the surplus from both customers and workers. Our analysis reveals that, in the one-sided competition, myopic stable outcome (i.e., Nash equilibrium) is consistent with the farsighted stable outcome in most of cases. However, in the two-sided competition, they are totally different. We also demonstrate that even though platforms are farsighted, the stable outcome cannot yield the monopolistic profit for the two platforms.

Decentralized two-sided matching markets serve millions of people every year across the world with the rise of the sharing economy. Prominent examples include the accommodation platform Airbnb and ride-sharing platforms such as DiDi, Grab and BlaBlaCar. These markets have several features: (1) participants enter and leave the market over time, (2) participants on one side publicize their willingness to be matched and wait for the other side to choose, and (3) participants have heterogeneous preferences for partners. Such a market may not operate efficiently due to participants’ limited information and search frictions. This paper studies a decentralized carpool matching market by using data from a Chinese ride-sharing platform to estimate a model of search and matching between drivers and passengers. It measures the passengers’ valuation of trips, the drivers’ preferences and their search length, and the drivers’ and the passengers’ waiting costs. It assesses whether centralized algorithms that require different information sets can improve match rates and quality. An interesting finding is that a greedy algorithm can decrease the participants’ waiting costs, increase match rates and the platform’s revenue, but reduce the drivers’ surplus.

Joint work with Tracy Xiao Liu, Tsinghua University, and Chenyu Yang, University of Maryland

Stochastic dynamic programming has been widely applied to model operations management problems. For these models, the single-period profit/cost function is fundamental to determining an optimal control policy. In practice, many of these profit/cost functions are quite complex and make the optimal policies too complicated for managers to implement. To address the challenge, we introduce a concept named weak K-convexity, which generalizes many variations of convexity in the literature, and establish some preservation results of weak K-convexity that can be used in dynamic programming setting. These findings lead to well-structured heuristic policies with worst-case performance bounds for a class of periodic-review inventory control or joint pricing and inventory control problems. Numerical studies show that our heuristic policies perform strongly.

In this paper, we study hybrid manufacturing/remanufacturing inventory systems that produce a single product to satisfy random demands over a finite planning horizon. In each period, the firm receives random demand and random product returns. A serviceable product can be manufactured from raw materials or remanufactured from a returned product. Both operations face random capacities modeled as positively dependent random variables. The firm's objective is to minimize the expected total discounted cost over the planning horizon. We partially characterize the firm's optimal policy for the general model and completely characterize it for the models with deterministic manufacturing or remanufacturing capacity, by two increasing functions with slopes at most one. For the special case with unlimited manufacturing capacity, we further characterize the optimal policy and obtain additional insights. In particular, we connect this model with an auxiliary dual-sourcing inventory model and show that they have the same optimal policy under certain conditions. Finally, we conduct a numerical study to derive further insights into the effects of random capacities. Among others, we find that ignoring randomness of the manufacturing capacity often incurs significant cost to the firm while the cost of ignoring capacity correlation is negligible. This is joint work with Suting Liu.

In healthcare service systems, the workload level can substantially impact service time and quality. We investigate this relationship in the context of cardiac surgery. Using 5,600 cardiac surgeries in a large hospital, we quantify how individual surgeon’s daily workload (number of cases performed in a day) affects surgery duration and patient outcomes. To handle the endogeneity issue, we construct novel instrument variables using hospital operational factors. We find surgeon’s high daily workload leads to longer OR times and post-surgery length-of-stay. We develop a scheduling model that incorporates the estimated effects and show that it can lead to substantial improvement.

In this paper, we develop several game theoretic models to study the selling format and seller service strategies of an online retailer. In our analysis, we consider two common selling formats, agency selling and reselling, and two common seller services, paid advertising and free information sharing services. When the online retailer offers advertising service, there is a cost sharing flexibility effect that creates more incentive for a seller to buy advertising under agency selling than reselling. Moreover, a higher cost of advertising effort mitigates the double marginalization effect of wholesale price and increases supply chain efficiency under reselling. When the online retailer does not offer advertising service, the cost sharing flexibility effect does not exist and the wholesale price no longer depends on the cost of advertising effort. When the online retailer offers both advertising and information sharing services, we fully characterize the equilibrium selling format, advertising fee, wholesale price, advertising effort and retail price. The seller prefers agency selling to reselling if the commission rate is low, whereas the reverse is true for the platform. Either firm prefers agency selling to reselling if the cost of advertising effort is low, or either the demand signal accuracy or demand uncertainty is high. We also consider the cases when the online retailer either offers advertising service but not information sharing service, or does not offer any of these two services. When more seller services are offered, the parameter space in which the supply chain profit is higher under agency selling than under reselling becomes larger. Moreover, the parameter space in which either firm prefers or both firms prefer agency selling to reselling becomes larger.