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Motivated by the prevalence of ride-sharing platforms, in “Spatial Pricing in Ride-Sharing Networks,” Bimpikis, Candogan, and Saban explore the impact of the demand pattern for rides across a network’s locations on a platform’s optimal pricing and compensation policy, profits, and consumer surplus. They explicitly account for the pricing problem’s spatial dimension and the fact that the drivers endogenously determine whether and where to provide service. Their first contribution is to develop a tractable model to study a platform operating on a network of locations that may differ in both the size of their potential demand and the destination preferences of riders. Second, they provide a characterization of the platform’s optimal policy and identify “balancedness” of the demand pattern as a property that captures the profit potential of a given network. Finally, they discuss the benefits and limitations of a number of alternative pricing and compensation schemes. We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Riders are heterogeneous in terms of their destination preferences and their willingness to pay for receiving service. Drivers decide whether and where to provide service so as to maximize their expected earnings given the platform’s pricing and compensation policy. Our findings highlight the impact of the demand pattern on the platform’s prices, profits, and the induced consumer surplus. In particular, we establish that profits and consumer surplus at the equilibrium corresponding to the platform’s optimal pricing and compensation policy are maximized when the demand pattern is “balanced” across the network’s locations. In addition, we show that they both increase monotonically with the balancedness of the demand pattern (as formalized by its structural properties). Furthermore, if the demand pattern is not balanced, the platform can benefit substantially from pricing rides differently depending on the location from which they originate. Finally, we consider a number of alternative pricing and compensation schemes that are commonly used in practice and explore their performance for the platform. The e-companion is available at https://doi.org/10.1287/opre.2018.1800 .

This paper studies multitier supply chain networks in the presence of disruption risk. Firms decide how much to source from their upstream suppliers so as to maximize their expected profits, and prices of intermediate goods are set so that markets clear. We provide an explicit characterization of (expected) equilibrium profits, which allows us to derive insights into how the network structure—that is, the number of firms in each tier, production costs, and disruption risk—affect firms’ profits. Furthermore, we establish that networks that maximize profits for firms that operate in different stages of the production process—that is, for upstream suppliers and downstream retailers—are structurally different. In particular, the latter have relatively less diversified downstream tiers and generate more variable output than the former. Finally, we consider supply chains that are formed endogenously. Specifically, we study a setting where firms decide whether to engage in production by considering their (expected) postentry profits. We argue that endogenous entry may lead to chains that are inefficient in terms of the number of firms that engage in production. This paper was accepted by Vishal Gaur, operations management.

Motivated by the proliferation of online platforms that collect and disseminate consumers’ experiences with alternative substitutable products/services, we investigate the problem of optimal information provision when the goal is to maximize aggregate consumer surplus. We develop a decentralized multiarmed bandit framework where a forward-looking principal (the platform designer) commits up front to a policy that dynamically discloses information regarding the history of outcomes to a series of short-lived rational agents (the consumers). We demonstrate that consumer surplus is nonmonotone in the accuracy of the designer’s information-provision policy. Because consumers are constantly in “exploitation” mode, policies that disclose accurate information on past outcomes suffer from inadequate “exploration.” We illustrate how the designer can (partially) alleviate this inefficiency by employing a policy that strategically obfuscates the information in the platform’s possession; interestingly, such a policy is beneficial despite the fact that consumers are aware of both the designer’s objective and the precise way by which information is being disclosed to them. More generally, we show that the optimal information-provision policy can be obtained as the solution of a large-scale linear program. Noting that such a solution is typically intractable, we use our structural findings to design an intuitive heuristic that underscores the value of information obfuscation in decentralized learning. We further highlight that obfuscation remains beneficial even if the designer can directly incentivize consumers to explore through monetary payments. This paper was accepted by Serguei Netessine, operations management.

Firms and institutions increasingly employ contests as a way to innovate. Participants learn from one another’s successes and failures while competing for the contest’s awards. In “Designing Dynamic Contests,” Bimpikis, Ehsani, and Mostagir provide a number of design guidelines that focus on the contest’s information-disclosure policy. Critically, their analysis highlights that, when agents compete and learn from one another dynamically, the success of the contest largely relies on when and what information the designer chooses to disclose about their relative progress toward the end goal. Participants race toward completing an innovation project and learn about its feasibility from their own efforts and their competitors’ gradual progress. Information about the status of competition can alleviate some of the uncertainty inherent in the contest, but it can also adversely affect effort provision from the laggards. This paper explores the problem of designing the award structure of a contest and its information disclosure policy in a dynamic framework and provides a number of guidelines for maximizing the designer’s expected payoff. In particular, we show that the probability of obtaining the innovation as well as the time it takes to complete the project are largely affected by when and what information the designer chooses to disclose. Furthermore, we establish that intermediate awards may be used by the designer to appropriately disseminate information about the status of competition. Interestingly, our proposed design matches several features observed in real-world innovation contests. The e-companion is available at https://doi.org/10.1287/opre.2018.1823 .

This paper studies the strategic interaction between a monopolistic seller of an information product and a set of potential buyers that compete in a downstream market. The setting is motivated by information markets in which (i) sellers have the ability to offer information products of different qualities and (ii) the information product provides potential buyers not only with more precise information about the fundamentals, but also with a coordination device that can be used in their strategic interactions with their competitors. Our results illustrate that the nature and intensity of competition among the information provider’s customers play first-order roles in determining the information provider’s optimal strategy. We show that when the customers view their actions as strategic complements, the provider finds it optimal to offer the most accurate information at the provider’s disposal to all potential customers. In contrast, when buyers view their actions as strategic substitutes, the provider maximizes the provider’s profits by either (i) restricting the overall supply of the information product or (ii) distorting its content by offering a product of inferior quality. We also establish that the provider’s incentive to restrict the supply or quality of information provided to the downstream market intensifies in the presence of information leakage. The online appendix is available at https://doi.org/10.1287/mnsc.2018.3068 . This paper was accepted by Gad Allon, operations management.

Recent advances in information technology have allowed firms to gather vast amounts of data regarding consumers’ preferences and the structure and intensity of their social interactions. This paper examines a game-theoretic model of competition between firms that can target their marketing budgets to individuals embedded in a social network. We provide a sharp characterization of the optimal targeted advertising strategies and highlight their dependence on the underlying social network structure. Furthermore, we provide conditions under which it is optimal for the firms to asymmetrically target a subset of the individuals and establish a lower bound on the ratio of their payoffs in these asymmetric equilibria. Finally, we find that at equilibrium firms invest inefficiently high in targeted advertising and the extent of the inefficiency is increasing in the centralities of the agents they target. Taken together, these findings shed light on the effect of the network structure on the outcome of marketing competition between the firms.

The paper considers a model of competition among firms that produce a homogeneous good in a networked environment. A bipartite graph determines which subset of markets a firm can supply to. Firms compete à la Cournot and decide how to allocate their production output to the markets they are directly connected to. We provide a characterization of the production quantities at the unique equilibrium of the resulting game for any given network. Our results identify a novel connection between the equilibrium outcome and supply paths in the underlying network structure. We then proceed to study the impact of changes in the competition structure, for example, due to a firm expanding into a new market or two firms merging, on firms’ profits and consumer surplus. The modeling framework we propose can be used in assessing whether expanding in a new market is profitable for a firm, identifying opportunities for collaboration, for example, a joint venture between competing firms, and guiding regulatory action in the context of market design and antitrust analysis. The online appendix is available at https://doi.org/10.1287/mnsc.2018.3061 . This paper was accepted by Gad Allon, operations management.

Online retail reduces the costs of obtaining information about a product’s price and availability and of flexibly timing a purchase. Consequently, consumers can strategically time their purchases, weighing the costs of monitoring and the risk of inventory depletion against prospectively lower prices. At the same time, firms can observe and exploit their customers’ monitoring behavior. Using a data set tracking customers of a North American specialty retail brand, we present empirical evidence that monitoring products online is associated with successfully obtaining discounts. We develop a structural model of consumers’ dynamic monitoring to find substantial heterogeneity, with consumers’ opportunity costs for an online visit ranging from $2 to $25 in inverse relation to their price elasticities. Our estimation results have important implications for retail operations. The randomized markdown policy benefits retailers by combining price commitment with the exploitation of heterogeneity in consumers’ monitoring costs. We estimate that the retailer’s profit under randomized markdowns is 81% higher than from subgame-perfect, state-contingent pricing, because the retailer need not limit its inventory to credibly limit markdowns, which permits its jointly optimal inventory stock to expand by 133%. The welfare gain from these larger inventories splits nearly equally into retailer profit and consumer surplus. We also discuss targeting customers with price promotions using their online histories and the implications of reducing consumers’ monitoring costs. The electronic companion is available at https://doi.org/10.1287/mnsc.2016.2661 . This paper was accepted by Serguei Netessine, operations management.

We study the optimal pricing strategies of a monopolist selling a divisible good (service) to consumers who are embedded in a social network. A key feature of our model is that consumers experience a (positive) local network effect . In particular, each consumer's usage level depends directly on the usage of her neighbors in the social network structure. Thus, the monopolist's optimal pricing strategy may involve offering discounts to certain agents who have a central position in the underlying network. Our results can be summarized as follows. First, we consider a setting where the monopolist can offer individualized prices and derive a characterization of the optimal price for each consumer as a function of her network position. In particular, we show that it is optimal for the monopolist to charge each agent a price that consists of three components: (i) a nominal term that is independent of the network structure, (ii) a discount term proportional to the influence that this agent exerts over the rest of the social network (quantified by the agent's Bonacich centrality ), and (iii) a markup term proportional to the influence that the network exerts on the agent. In the second part of the paper, we discuss the optimal strategy of a monopolist who can only choose a single uniform price for the good and derive an algorithm polynomial in the number of agents to compute such a price. Third, we assume that the monopolist can offer the good in two prices, full and discounted, and we study the problem of determining which set of consumers should be given the discount. We show that the problem is NP-hard; however, we provide an explicit characterization of the set of agents who should be offered the discounted price. Next, we describe an approximation algorithm for finding the optimal set of agents. We show that if the profit is nonnegative under any feasible price allocation, the algorithm guarantees at least 88% of the optimal profit. Finally, we highlight the value of network information by comparing the profits of a monopolist who does not take into account the network effects when choosing her pricing policy to those of a monopolist who uses this information optimally.

This paper studies sourcing decisions of firms in a multitier supply chain when procurement is subject to disruption risk. We argue that features of the production process that are commonly encountered in practice (including differential production technologies and financial constraints) may result in the formation of inefficient supply chains, owing to the misalignment of the sourcing incentives of firms at different tiers. We provide a characterization of the conditions under which upstream suppliers adopt sourcing strategies that are suboptimal from the perspective of firms further downstream. Our analysis highlights that a focus on optimizing procurement decisions in each tier of the supply chain in isolation may not be sufficient for mitigating risks at an aggregate level. Rather, we argue that a holistic view of the entire supply network is necessary to properly assess and secure against disruptive events. Importantly, the misalignment we identify does not originate from cost or reliability asymmetries. Rather, firms’ sourcing decisions are driven by the interplay of the firms’ risk considerations with nonconvexities in the production process. This implies that bilateral contracts that could involve under-delivery penalties may be insufficient to align incentives. The e-companion is available at https://doi.org/10.1287/opre.2017.1708 .