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The authors describe their implementation of a market-based approach to reallocating fishing shares in New South Wales, Australia. Their solution addressed several nonstandard requirements, including the possibility of all-or-nothing offers, fair prices, and an endogenously determined subsidy.

Combinatorial exchanges that allow for package offers to address nonconvexities in demand or supply typically employ linear and anonymous prices because they are simple, tractable, and fair. Despite their prevalence, linear anonymous prices do not necessarily correspond to Walrasian competitive equilibrium prices in such settings, and their impact is not well understood. This paper is the first to analyze the effect of different pricing rules on the efficiency of combinatorial exchanges, using both analytic methods and numerical experiments. Our analysis is motivated by a combinatorial fishery-rights exchange designed to reform the fishing industry in New South Wales (NSW), Australia. We find that when linearity and anonymity are required for only one side of the market, the average efficiency loss is negligible. In contrast, with a single linear price vector for both sides, the efficiency loss is substantial, especially when the market is small. In a formal model, we show that efficiency losses decrease when the number of buyers grows or the size of the submitted packages decreases. Besides the reform of the NSW fishing industry, our results have important implications for other cap-and-trade programs as well as other industries where demand or cost complementarities play a role.

We present our work with a global online fashion retailer, Zalando, as an example of how a global retailer can utilize massive amount of data to optimize price discount decisions over a large number of products in multiple countries on a weekly basis. Given demand forecasts under a collection of discrete prices, Zalando’s objective is to set discount levels to maximize total profit over the entire selling horizon while taking into account both local and global business constraints. Local constraints refer to single product level requirements, where Zalando needs to balance sales across different countries and over different weeks while adhering to a first-come-first serve policy. That is, as long as product inventory exists, a customer is served independent of the customer’s origin country or time of arrival. Global constraints refer to specific targets set by management for different product categories and each country. We address these challenges by applying a three-step process. In the first step, we cluster products into groups that behave similarly and solve the aggregated problem in a way that allows us to decouple the problem into a problem for each product category. Each product category includes thousands of individual products (SKUs) and the various markets where products are sold, each of which with its own target sales and margins. In the second step, we decompose this problem using Lagrangian relaxation into a problem for each product (SKU) and provide an efficient way to identify the Lagrange multipliers. Finally, in the last step, we optimize decisions for individual products and also address local business constraints. For this new approach, which was implemented as part of Zalando's price discount decision process, we provide results from offline tests and field experiments to demonstrate its benefit.

We describe the design and implementation of a combinatorial exchange for trading catch shares in New South Wales, Australia. The exchange ended a decades-long political debate by providing a market-based response to a major policy problem faced by fisheries worldwide: the reallocation of catch shares in cap-and-trade programs designed to prevent overfishing. The exchange was conducted over the Internet to lower participation costs and allowed for all-or-nothing orders to avoid fragmented share portfolios. A subsidy was distributed endogenously to facilitate the transfer of shares from inactive to active fishers. Finally, prices were linear and anonymous to ensure that sellers of identical packages received the same payments. These features were crucial to mitigate economic distortions from introducing catch shares and to gain broad acceptance of the program. However, they led to computationally challenging allocation and pricing problems. The exchange operated from May to July 2017 and effectively reallocated shares from inactive fishers to those who needed them most: 86% of active fishers' bids were matched and their share deficits were reduced by 95% in high-priority share classes. Similar reallocation problems arise in fisheries with catch-share systems worldwide as well as in other cap-and-trade systems for resource rights, e.g., water and pollution rights. The implemented exchange illustrates how computational optimization and market design can provide policy tools, able to solve complex policy problems considered intractable only a few years ago.

International audience; We study the interactions among Internet providers in vehicular networks which offer access to commuters via road side units (RSUs). Namely, we propose a game-theoretical framework to model the competition on prices between vehicular Internet providers to capture the largest amount of users, thus selfishly maximizing the revenues. The equilibria of the aforementioned game are characterized under different mobile traffic conditions, RSU capabilities and users requirements and expectations. In particular, we also consider in the analysis the case where mobile users modify the price they accept to pay for the access as the likeliness of finding an access solution decreases. Our game-theoretical analysis gives insights on the outcomes of the competition between vehicular Internet providers, further highlighting some counter-intuitive behaviors; as an example, comparing with the case when users have constant price valuation over time, having users inclined to increasing their "acceptable" price may force vehicle Internet providers to charge lower prices due to competition.

International audience; Vehicular networks, besides supporting safety- oriented applications, are nowadays expected to provide effec- tive communication infrastructure also for supporting leisure- oriented application including content sharing, gaming and In- ternet access on the move. This work focuses on Vehicle to Infras- tructure (V2I) scenarios, where multiple content providers own a physical infrastructure of Road Side Units (RSUs) which they use to sell contents to moving vehicles. Content provider/RSU owners compete by adapting their pricing strategies with the selfish objective to maximize their own revenues. We study the economics of the price competition between the providers by resorting to game theoretic tools. Namely, we formalize a simultaneous price game among the operators further studying the existence of Nash equilibria and their related quality in terms of Price of Anarchy and Price of Stability. The proposed game model is finally used to assess the impact onto the game equilibra of several practical factors including the vehicles' willingness to pay, the traffic densities, and the configuration of the physical networks of RSUs.

International audience; During the last years the problem of network selection in wireless heterogeneous systems has attracted a lot of attention. Expecting that next generation mobile devices will allow connections to different types of networks, it is interesting to investigate the outcome of selfish behavior in that context. It may then be necessary to introduce some mechanisms to drive users choices to some desirable directions - quality of service (QoS) optimization, energy consumption minimization, network revenue maximization -. In this paper, we define and investigate a system where users decide which network to connect to based on some ratings of networks, possibly computed from feedback sent by other users. We then apply that model to investigate the pricing decisions made by network owners, in two different settings: a compe- tition among several revenue-oriented operators, or a revenue- maximizing monopoly. The outcomes of those settings are com- pared, in terms of network usage and energy consumption.