Maritime transportation accounts for 90% of global trade, but ballasting—vessels traveling without cargo—imposes substantial economic and environmental costs. This paper examines the oil transportation industry, where approximately half of all miles traveled are sailed empty. While some ballasting is necessary due to inherent supply-demand imbalances in oil markets, our analysis demonstrates that market structure, specifically the fragmentation of vessel ownership, is also a primary driver, accounting for 10-20% of the total empty miles traveled depending on the market segment. In addition, we show that consolidating vessels into small shipping pools—sets of vessels operated under unified management—can reduce ballasting-related carbon emissions by up to 15%. This market-driven approach, which is gaining industry adoption, maintains competitive dynamics, given the limited scale of consolidation, while significantly improving efficiency. The gains arise from enhanced coordination within larger pools and expanded port coverage, reducing unnecessary vessel repositioning. More broadly, our findings quantitatively demonstrate that organizational changes alone—specifically, the consolidation of vessel operations—can generate significant environmental improvements by reducing empty miles. This provides a practical path toward sustainability that can complement and amplify the benefits of technological innovation.

We develop a dynamic game-theoretic model of a two-sided platform that allows for heterogeneity and endogenous behavior on both sides of the market. We focus on illustrating the potential benefits of optimal information provision in terms of managing supply-side decisions, including supplier entry/exit and pricing. Our analysis identifies the mechanisms through which information design may increase platform revenues. Finally, we highlight the role of information design as a substitute for commission subsidies during cold start. Overall, our numerical experiments suggest that, by influencing the providers’ decisions, optimal information provision can lead to a substantial increase in platform revenues.

We explore the welfare implications of data-tracking technologies that enable firms to collect consumer data and use it for price discrimination. The model we develop centers around two features: competition between firms and consumers’ level of sophistication. We find that the absence of data tracking may lead to a decrease in consumer surplus, even when consumers are myopic. Importantly, this result relies critically on competition: consumer surplus may be higher when data-tracking technologies are used only when multiple firms offer substitutable products.

This paper establishes that two-sided platforms have an incentive to strategically disclose (coarse) information about demand to the supply side as this can considerably boost their profits. However, this practice may also adversely affect the welfare of consumers. By optimally designing its information disclosure policy, a platform can influence the entry and pricing decisions of its potential suppliers. On the other hand, consumers may end up being worse off as they have access to fewer trading options and/or face higher prices compared to when the platform refrains from sharing any demand information to its potential suppliers.

We investigate the effectiveness of a number of policy instruments, i.e., taxes and subsidies, in terms of their impact on the adoption of innovative production methods, producers’ profits, consumer surplus, and return on government expenditure. Our findings indicate that using only taxes encourages experimentation with new production methods but decreases social welfare. Utilizing only subsidies outperforms policies that involve both taxes and subsidies in achieving higher social welfare but the converse is true in achieving a higher experimentation rate. We illustrate the applicability of our insights by conducting a numerical study using data on conventional and organic egg production in Denmark.

We explore marketplace design in the context of a B2B platform specializing in liquidation auctions and establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% by inducing a higher level of bidder participation. Also, we estimate a structural model that endogenizes bidders’ dynamic behavior and find that appropriately designing a recommendation system yields an additional revenue increase by reducing supply-side cannibalization and altering the composition of participating bidders.

This paper studies multi-tier supply chain networks in the presence of disruption risk. Firms decide how to source their inputs from 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 equilibrium prices and profits, which allows us to derive insights into how the network structure, i.e., the number of firms in each tier, production costs, and disruption risk, affect firms’ profits. Also, we consider supply chains that are formed endogenously and argue that endogenous entry leads to chains that are inefficient in terms of the number of firms that engage in production.

We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Drivers decide whether and where to provide service so as to maximize their expected earnings. We characterize the platform’s optimal pricing policy in an intuitive way using a set of dual variables capturing the value of a driver in a given location. Our findings also highlight the impact of the demand pattern on the platform’s prices, profits, and the induced consumer surplus, i.e., we establish that profits and consumer surplus at equilibrium are increase monotonically with the balancedness of the demand pattern across the network’s locations.

The paper considers 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 in Cournot and decide how to allocate their output to the markets they are connected to. We provide a characterization of the production quantities at the unique equilibrium of the resulting game for any given network. We 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, e.g., due to a firm expanding into a new market or two firms merging, on firms’ profits and consumer surplus.

This paper studies the interaction between a seller of an information product and potential buyers that compete in a downstream market. Our results illustrate that the nature and intensity of competition are key in determining the optimal strategy. We show that when customers’ actions are strategic complements, the provider finds it optimal to offer the most accurate information to all potential customers. In contrast, when customers’ actions are strategic substitutes, the provider maximizes her profits by either (i) restricting the supply of the information product, or (ii) distorting its content. We also establish that the provider’s incentive to restrict the supply information intensifies in the presence of information leakage.

We consider the design of service systems that process tasks with types that are ex ante unknown, and employ servers with different skill sets. We show that the performance loss due to the uncertainty can be significant and that the system’s stability region is dependent on the rate at which information about tasks’ types is generated. Furthermore, we consider endogenizing the servers’ capabilities and explore the problem of jointly optimizing over training and staffing levels and the resource allocation policy. We find that among optimal designs there always exists one with a hierarchical structure, where all tasks are initially routed to the least skilled servers and then progressively move to more skilled ones, if necessary.

Participants race towards 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. 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. We establish that intermediate awards may be used by the designer to disseminate information about the status of competition. Our proposed design matches several features observed in real-world innovation contests.

Crowd-sourced content in the form of online product reviews or recommendations is an integral feature of most Internet-based service platforms and marketplaces. The service platform or marketplace “explores” the set of available options through its customers’ decisions, while they “exploit” the information they obtain from the platform about past experiences to determine whether and what to purchase. Unlike the extensive work on the trade-off between exploration and exploitation in the context of multi-armed bandits, the canonical framework we discuss in this chapter involves a principal that explores a set of options through the actions of self-interested agents.

This paper studies sourcing decisions of firms in a multi-tier 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. 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.

We investigate the problem of optimal information provision when the goal is to maximize aggregate consumer surplus. We illustrate how a designer can (partially) alleviate the inefficiency involved in information gathering by employing a policy that strategically obfuscates past data and show that the optimal information-provision policy can be obtained as the solution of a large-scale linear program.

We present empirical evidence that monitoring products online is associated with successfully obtaining discounts. Further, we develop a structural model of consumers’ dynamic monitoring to find substantial heterogeneity in consumers’ opportunity costs for an online visit ranging from 2 to 25$. A 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.

We introduce a framework for developing adaptive, personalized treatments for chronic diseases for which medication is effective for only a subset of patients. Our model is based on a continuous-time, multi-armed bandit setting where drug effectiveness is assessed by aggregating information from several channels: by continuously monitoring the state of the patient, but also by (not) observing the occurrence of particular infrequent health events, such as relapses or disease flare-ups. We illustrate the effectiveness of the methodology by developing a set of efficient treatment policies for multiple sclerosis, which we then use to benchmark several existing treatment guidelines.

This paper examines a game-theoretic model of competition between firms which 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.

We show that a celebrated result in inventory management, i.e., that the expected cost savings from centralized inventory management scale with the square root of the number of locations, depends on the “light-tailed” nature of the demand uncertainty. In particular, we establish that the benefit from pooling relative to the decentralized case, in terms of both expected cost and safety stock, is equal to $n^{\frac{(\alpha–1)}{\alpha}}$ for a class of heavy-tailed demand distributions, whose power-law asymptotic decay rate is determined by the parameter $\alpha \in (1, 2)$.

We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. We define asymptotic learning as the fraction of agents taking the correct action converging to one as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the induced communication network most agents are a short distance away from “information hubs,” which receive and distribute a large amount of information.

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. We characterize the monopolist’s optimal personalized pricing policy and show that it is directly linked to an individual’s Bonacich centrality.

This paper studies a simple model of experimentation and innovation. Our analysis suggests that patents improve the allocation of resources by encouraging rapid experimentation and efficient ex post transfer of knowledge. Symmetric equilibria involve delayed and staggered experimentation, whereas the optimal allocation never involves delays and may involve simultaneous experimentation. Appropriately designed patents implement the optimal allocation. Finally, we discuss the case when signals differ and are private information.

We study the efficiency of oligopoly equilibria in a model where firms compete over capacities and prices. We show that efficiency in the worst oligopoly equilibria can be arbitrarily low. However, if the best oligopoly equilibrium is selected (among multiple equilibria), the worst-case efficiency loss is $$2(\sqrt{N}-1)/(N-1)$$ with $N$ firms, and this bound is tight. We also suggest a simple way of implementing the best oligopoly equilibrium.