Combinatorial auctions for electronic business

Combinatorial auctions (CAs) have recently generated significant interest as an automated mechanism for buying and selling bundles of goods. They are proving to be extremely useful in numerous e-business applications such as e-selling, e-procurement, e-logistics, and B2B exchanges. In this article, we introduce combinatorial auctions and bring out important issues in the design of combinatorial auctions. We also highlight important contributions in current research in this area. This survey emphasizes combinatorial auctions as applied to electronic business situations

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

A Free Exchange e-Marketplace for Digital Services

The digital era is witnessing a remarkable evolution of digital services. While the prospects are countless, the e-marketplaces of digital services are encountering inherent game-theoretic and computational challenges that restrict the rational choices of bidders. Our work examines the limited bidding scope and the inefficiencies of present exchange e-marketplaces. To meet challenges, a free exchange e-marketplace is proposed that follows the free market economy. The free exchange model includes a new bidding language and a double auction mechanism. The rule-based bidding language enables the flexible expression of preferences and strategic conduct. The bidding message holds the attribute-valuations and bidding rules of the selected services. The free exchange deliberates on attributes and logical bidding rules for automatic deduction and formation of elicited services and bids that result in a more rapid self-managed multiple exchange trades. The double auction uses forward and reverse generalized second price auctions for the symmetric matching of multiple digital services of identical attributes and different quality levels. The proposed double auction uses tractable heuristics that secure exchange profitability, improve truthful bidding and deliver stable social efficiency. While the strongest properties of symmetric exchanges are unfeasible game-theoretically, the free exchange converges rapidly to the social efficiency, Nash truthful stability, and weak budget balance by multiple quality-levels cross-matching, constant learning and informs at repetitive thick trades. The empirical findings validate the soundness and viability of the free exchange

Distributed optimisation techniques for wireless networks

Alongside the ever increasing traffic demand, the fifth generation (5G) cellular network architecture is being proposed to provide better quality of service, increased data rate, decreased latency, and increased capacity. Without any doubt, the 5G cellular network will comprise of ultra-dense networks and multiple input multiple output technologies. This will make the current centralised solutions impractical due to increased complexity. Moreover, the amount of coordination information that needs to be transported over the backhaul links will be increased. Distributed or decentralised solutions are promising to provide better alternatives. This thesis proposes new distributed algorithms for wireless networks which aim to reduce the amount of system overheads in the backhaul links and the system complexity. The analysis of conflicts amongst transmitters, and resource allocation are conducted via the use of game theory, convex optimisation, and auction theory. Firstly, game-theoretic model is used to analyse a mixed quality of service (QoS) strategic non-cooperative game (SNG), for a two-user multiple-input single-output (MISO) interference channel. The players are considered to have different objectives. Following this, the mixed QoS SNG is extended to a multicell multiuser network in terms of signal-to-interference-and-noise ratio (SINR) requirement. In the multicell multiuser setting, each transmitter is assumed to be serving real time users (RTUs) and non-real time users (NRTUs), simultaneously. A novel mixed QoS SNG algorithm is proposed, with its operating point identified as the Nash equilibrium-mixed QoS (NE-mixed QoS). Nash, Kalai-Smorodinsky, and Egalitarian bargain solutions are then proposed to improve the performance of the NE-mixed QoS. The performance of the bargain solutions are observed to be comparable to the centralised solutions. Secondly, user offloading and user association problems are addressed for small cells using auction theory. The main base station wishes to offload some of its users to privately owned small cell access points. A novel bid-wait-auction (BWA) algorithm, which allows single-item bidding at each auction round, is designed to decompose the combinatorial mathematical nature of the problem. An analysis on the existence and uniqueness of the dominant strategy equilibrium is conducted. The BWA is then used to form the forward BWA (FBWA) and the backward BWA (BBWA). It is observed that the BBWA allows more users to be admitted as compared to the FBWA. Finally, simultaneous multiple-round ascending auction (SMRA), altered SMRA (ASMRA), sequential combinatorial auction with item bidding (SCAIB), and repetitive combinatorial auction with item bidding (RCAIB) algorithms are proposed to perform user offloading and user association for small cells. These algorithms are able to allow bundle bidding. It is then proven that, truthful bidding is individually rational and leads to Walrasian equilibrium. The performance of the proposed auction based algorithms is evaluated. It is observed that the proposed algorithms match the performance of the centralised solutions when the guest users have low target rates. The SCAIB algorithm is shown to be the most preferred as it provides high admission rate and competitive revenue to the bidders

Operational research and simulation methods for autonomous ride-sourcing

Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems. The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method. The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage. The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces

Essays in auction theory

Auction theory is a branch of game theory that considers human behavior in auction markets and the ensuing market outcomes. It is also successfully used as a tool to design real-life auctions. This thesis contains five essays addressing a variety of topics within the realm of auction theory. The first essay gives an easily accessible overview of the most important insights of auction theory. The second essay, motivated by the UMTS-auctions that took place in Europe, studies auctions in which, in contrast to standard auction theory, losing bidders benefit from a high price paid by the winner(s). Under this assumption, the first-price sealed-bid auction and the second-price sealed-bid auction are no longer revenue equivalent. The third essay analyzes how well different kinds of auctions are able to raise money for charity. It turns out that standard winner-pay auctions are inept fund-raising mechanisms because of the positive externality bidders forgo if they top another’s high bid. As this problem does not occur in all-pay auctions, where bidders pay irrespective of whether they win or lose, all-pay auctions are more effective in raising money. The fourth essay studies a particular auction type, a so-called simultaneous pooled auction with multiple bids and preference lists, that has been used for example in the Netherlands and Ireland to auction available spectrum. The results in this essay show that this type of auction does not satisfy elementary desirable properties such as the existence of an efficient equilibrium. The fifth essay argues that inefficient auction outcomes due to strong negative (informational) externalities (created by post-auction interactions) can be avoided by asking bidders prior to the auction to submit any publicly observable payment they would like to make.

Modèles et algorithmes pour les enchères combinatoires

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

On incentive issues in practical auction design

Algorithmic mechanism design studies the allocation of resources to selfish agents, who might behave strategically to maximize their own utilities. This thesis studies these incentive issues arsing from four different settings, that are motivated by real- life applications. We model the settings and problems by appropriately extending or generalizing classical economic models. After that we systematically analyze the auction design problems by using methods from both economic theory and computer science. The first problem is the auction design problem for selling online rich media ad- vertisement. In this market, multiple advertisers compete for a set of slots that are arranged in a line, such as a banner on a website. Each buyer desires a particular num- ber of consecutive slots and has a private per-click valuation while each slot is associated with a quality factor. Our goal is to maximize the auctioneer’s expected revenue given buyers’ consecutive demand. This is motivated by modeling buyers who may require these to display a large size ad. Three major pricing mechanisms, the Bayesian pric- ing model, the maximum revenue market equilibrium model and an envy-free solution model are studied in this setting. The second setting is for fund-raising scenarios, where a revenue target is usually specified. We are interested in designing truthful auctions that maximize the probability to achieve this revenue target, rather than in maximizing the expected revenue. We study this topic from the perspective of Bayesian auction design in digital good auctions. We present an algorithm to find the optimal truthful auction for two buyers with independent valuations and show the problem is NP-hard when the number of buyers is arbitrary or the distributions are correlated. We also investigate simple auctions in this setting and provide approximately optimal solutions. Third, we study double auction market design where the trading broker wants to maximize its total revenue by buying low from the sellers and selling high to the buyers in a Bayesian setting. For single-parameter setting, we develop a maximum mechanism for the market maker to maximize its own revenue. For the more general case where each seller’s product may be different, we consider a number of various settings in terms of constraints on supplies and demands. For each of them, we develop a polynomial time computable truthful mechanism for the market maker to achieve a revenue at least a constant factor times the revenue of any other truthful mechanism. Finally, we study the inefficiency of mixed equilibria of all-pay auctions in three different environments – combinatorial, multi-unit and single-item auctions. First, we consider item-bidding combinatorial auctions where m all-pay auctions run in parallel, one for each good. For fractionally subadditive valuations, we strengthen the upper bound by proving some structural properties of mixed Nash equilibria. Next, we design an all-pay mechanism with a randomized allocation rule for the multi-unit auction, which admits a unique, approximately efficient, pure Nash equilibrium. Finally, we analyze single-item all-pay auctions motivated by their connection to crowdsourcing contests and show tight bounds on the PoA of social welfare, revenue and maximum bid