Optimal Mechanism Design with Flexible Consumers and Costly Supply

The problem of designing a profit-maximizing, Bayesian incentive compatible and individually rational mechanism with flexible consumers and costly heterogeneous supply is considered. In our setup, each consumer is associated with a flexibility set that describes the subset of goods the consumer is equally interested in. Each consumer wants to consume one good from its flexibility set. The flexibility set of a consumer and the utility it gets from consuming a good from its flexibility set are its private information. We adopt the flexibility model of [1] and focus on the case of nested flexibility sets -- each consumer's flexibility set can be one of k nested sets. Examples of settings with this inherent nested structure are provided. On the supply side, we assume that the seller has an initial stock of free supply but it can purchase more goods for each of the nested sets at fixed exogenous prices. We characterize the allocation and purchase rules for a profit-maximizing, Bayesian incentive compatible and individually rational mechanism as the solution to an integer program. The optimal payment function is pinned down by the optimal allocation rule in the form of an integral equation. We show that the nestedness of flexibility sets can be exploited to obtain a simple description of the optimal allocations, purchases and payments in terms of thresholds that can be computed through a straightforward iterative procedure.Comment: 8 pages. arXiv admin note: text overlap with arXiv:1607.0252

Analyzing and Predicting Verification of Data-Aware Process Models – a Case Study with Spectrum Auctions

Verification techniques play an essential role in detecting undesirable behaviors in many applications like spectrum auctions. By verifying an auction design, one can detect the least favorable outcomes, e.g., the lowest revenue of an auctioneer. However, verification may be infeasible in practice, given the vast size of the state space on the one hand and the large number of properties to be verified on the other hand. To overcome this challenge, we leverage machine-learning techniques. In particular, we create a dataset by verifying properties of a spectrum auction first. Second, we use this dataset to analyze and predict outcomes of the auction and characteristics of the verification procedure. To evaluate the usefulness of machine learning in the given scenario, we consider prediction quality and feature importance. In our experiments, we observe that prediction models can capture relationships in our dataset well, though one needs to be careful to obtain a representative and sufficiently large training dataset. While the focus of this article is on a specific verification scenario, our analysis approach is general and can be adapted to other domains

Auction-Based Efficient Online Incentive Mechanism Designs in Wireless Networks

Recently, wide use of mobile devices and applications, such as YouTube and Twitter, has facilitated every aspect of our daily lives. Meanwhile, it has also posed great challenges to enable resource-demanding users to successfully access networks. Thus, in order to enlarge network capacity and fully make use of vacant resources, new communication architectures emerge, such as D2D communications, edge computing, and crowdsourcing, all of which ask for involvement of end mobile users in assisting transmission, computation, or network management. However, end mobile users are not always willing to actively provide such sharing services if no reimbursements are provided as they need to consume their own computation and communication resources. Besides, since mobile users are not always stationary, they can opt-in and opt-out the network for their own convenience. Thus, an important practical characteristic of wireless networks, i.e., the mobility of mobile users cannot be ignored, which means that the demands of mobile users span over a period of time. As one of promising solutions, the online incentive mechanism design has been introduced in wireless networks in order to motivate the participation of more mobile users under a dynamic environment. In this thesis, with the analyses of each stakeholder's economic payoffs in wireless networks, the auction-based online incentive mechanisms are proposed to achieve resource allocations, participant selections, and payment determinations in two wireless networks, i.e., Crowdsensing and mobile edge computing. In particular, i) an online incentive mechanism is designed to guarantee Quality of Information of each arriving task in mobile crowdsensing networks, followed by an enhanced online strategy which could further improves the competitive ratio; ii) an online incentive mechanism jointly considering communication and computation resource allocations in collaborative edge computing networks is proposed based on the primal-dual theory; iii) to deal with the nonlinear issue in edge computing networks, an nonlinear online incentive mechanism under energy budget constraints of mobile users is designed based on the Maximal-in-Distributional Range framework; and iv) inspired by the recent development of deep learning techniques, a deep incentive mechanism with the budget balance of each mobile user is proposed to maximize the net revenue of service providers by leveraging the multi-task machine learning model. Both theoretical analyses and numerical results demonstrate the effectiveness of the designed mechanisms

Improvements to transmission expansion planning and implementation :treating uncertainty in commercial operation dates and increasing aunction efficiency

Three proposals contributing to the electricity transmission expansion planning and implementation process are presented in this thesis. The first proposal refers to the use of combinatorial and simultaneous descending auctions to treat the exposure problem and increase the efficiency of multi-item transmission auctions. A simulation framework to quantify potential benefits of using these auctions protocols, for transmission companies and grid users, is proposed. The second proposal refers to an expansion planning methodology that explicitly accounts for uncertainties in facility implementation times while determining the capacity additions and their optimal implementation schedule. In the third proposal, principal-agent theoretic concepts are applied to develop a methodology for the optimal design of winner-selection and risksharing mechanisms, with the goal of managing uncertainties in implementation times of transmission facilities, when competitive processes are used to select the agents to which concessions to implement and operate these facilities are awarded. Classical optimization approaches, notably mixed-integer linear programming, are used in the mathematical formulations that underlie the simulation and analyses carried out for all three proposals; and qualitative conclusions aiming at aiding planners and regulators are drawn from the quantitative results of case studies.Esta tese apresenta três contribuições ao planejamento e implantação da expansão da transmissão. Primeiro, propõe-se usar leilões combinatórios e leilões descendentes simultâneos para tratar o problema da exposição em leilões multi-itens de concessões de transmissão, aumentando a eficiência destes leilões, e apresenta-se um arcabouço de simulação para quantificar os benefícios potencias do uso de tais protocolos. Segundo, propõe-se uma metodologia de planejamento da expansão que considera explicitamente incertezas em tempos de implantação de instalações da transmissão ao determinar as adições de capacidade e as datas de início de implantação de ativos. Terceiro, aplica-se conceitos da teoria do agente-principal para propor uma abordagem para otimizar o desenho de mecanismos de seleção do vencedor e de partilha de riscos, de modo a gerir incertezas em tempos de implantação de ativos, no contexto em que mecanismos competitivos são utilizados para selecionar os agentes a que contratos de transmissão implantação são concedidos. Para todas as três propostas, utiliza-se abordagens de otimização clássica, notadamente programação inteira linear mista, para a formulação matemática que subsidia simulações e análises; e retira-se dos resultados numéricos de estudos de casos conclusões qualitativas que subsidiem planejadores e reguladores

Large-scale Multi-item Auctions : Evidence from Multimedia-supported Experiments

This book presents two experimental studies that deal with the comparison of multi-item auction designs for two specific applications: the sale of 2.6 GHz radio spectrum rights in Europe, and the sale of emissions permits in Australia. In order to tackle the complexity of these experiments, a cognitively based toolkit is proposed, including modularized video instructions, comprehension tests, a learning platform, a graphical one-screen user interface, and comprehension-based group matching