Auction Approach Research Articles

A Two-Sided Price-Decoupled Pay-As-Bid Auction Approach for the Clearing of Day-Ahead Electricity Markets

In this paper we propose a possible alternative for conventional pay-as-clear type multiunit auctions commonly used for the clearing of day-ahead power exchanges, and analyse some of its characteristic features in comparison with conventional clearing. In the proposed framework, instead of the concept of the uniform market clearing price, we introduce limit prices separately for supply and demand bids, and in addition to the power balance constraint, we formulate constraints for the income balance of the market. The total traded quantity is used as the objective function of the formulation. The concept is demonstrated on a simple example and is compared to the conventional approach in small-scale market simulations.

D2D-Enabled Mobile User Edge Caching: A Multi-Winner Auction Approach

In device-to-device (D2D)-enabled caching cellular networks, the user terminals (UTs) collaboratively store and share a large volume of popular contents from the base station (BS) for traffic offloading and delivery delay reduction. In this article, the multi-winner auction based caching placement in D2D-enabled caching cellular networks is investigated for UT edge caching incentive and content caching redundancy reduction. Firstly, a multi-winner once auction for UT edge caching is modeled which auctions multiple contents for multiple UTs. Then the optimization problem for content caching revenue maximization is formulated. Specifically, the "cache conflict" restriction relationship among UTs is used as one of the constraints in the problem to reduce the content caching redundancy in a UT movement scenario. The problem is solved by semidefinite programming (SDP) relaxation to obtain an approximate optimal caching placement. Moreover, the payment strategy of the auction is developed as a Nash bargaining game for personal profit fairness among the UTs who win the auction for content caching. Subsequently, a multi-winner once auction based caching (MOAC) placement algorithm is proposed. In addition, due to the high complexity of MOAC, we further propose a heuristic multi-winner repeated auction based caching placement (MRAC) algorithm, which can greatly reduce the complexity with only tiny performance loss. Simulation results show that the proposed algorithms can reduce the traffic load and average content access delay effectively compared with the existing caching placement algorithms.

Pricing the cloud: a QoS-based auction approach

ABSTRACTCloud computing is an evolutionary technology that offers on-demand resources and elastic services through the Internet. Most providers adopt fixed-price mechanisms (e.g. pay-as-you-go). However, a few providers have recently employed auction-like approaches to price cloud services. Meanwhile, cloud consumers pay more attention to Quality of Service (QoS) such as availability, which measures how well a service is performed. This paper proposes a novel auction approach that can efficiently allocate resources according to customers’ QoS preferences. The QoS-based pricing can generate more revenue than a fixed-price strategy. This research lies at the intersection of cloud computing, economics, and information systems.

Household-level dynamics in residential location choice modelling with a latent auction method

A longstanding deficiency in the modelling of residential location choices is the common assumption of a single household decision-maker. This paper contributes to a growing literature on methods to capture intra-household dynamics in this decision process. A latent auction approach is employed to estimate a residential location choice model for the Greater Toronto Area. The present work extends the consideration of individual utility factors beyond simple commute time, to include frequency of automobile use, transit use, and the cost of parking at the destination. Results suggest a weakening differential between male and female roles in the residential location choice, as multi-worker households are increasing in response to increasing costs of living. The strength of the latent auction model is confirmed as a means of linking bid-rent with observed market prices. Several conclusions are drawn out of the model results, including a pattern of larger households preferring the larger and cheaper houses characteristic of suburban areas of the Greater Toronto Area.

On Location Privacy-Preserving Online Double Auction for Electric Vehicles in Microgrids

In this paper, we address the issue of demand response (DR) in microgrids via vehicle-to-vehicle technology in the smart grid with consideration for location privacy protection supported by Internet of Vehicles. To enable effective DR, the online double auction is a viable approach to support energy trading between electric vehicles (EVs) that have surplus or insufficient energy, while the utility of each participant can be considered. Nonetheless, there are three primary challenges in designing such an online double auction approach. First, as EVs are allowed to enter the market at any time, the auctioneer should make the best decision without further information about bids and asks. Second, as EVs are allowed to enter the market in different places, the auctioneer should perform routing optimization for EV charging after determining the winner. Third, there is a risk of leakage in the location of EVs that needs to be protected. To tackle these issues, we present a new truthful online double auction scheme, which features multiunit energy trading among EVs, routing optimization for EV charging, and location privacy protection. We conduct a theoretical analysis and demonstrate that our online double auction scheme is capable of achieving several important economic properties as well as the privacy guarantee (i.e., k -anonymity). Our experimental results show that the proposed scheme can achieve good performance with respect to social welfare, satisfaction ratio, total profit of EV owners, peak load shifting, state of charge, driving distance satisfaction, and computing time, and can further ensure location privacy protection.

Text vs. logo: Does eco-label format influence consumers’ visual attention and willingness-to-pay for fruit plants? An experimental auction approach

Eco-labels identify products that are produced in an environmentally friendly way. They utilize many formats combining graphics/logos and text to attract environmentally conscious consumers. Different eco-label formats may vary in their effectiveness in influencing consumers’ preferences for products. This manuscript explores the relationship between eco-labels’ format and consumer preferences, willingness-to-pay, and visual attention. Participants’ visual attention was recorded while they submitted bids in an experimental auction for fruit-producing plants with three different eco-labels (i.e. industry-specific, Non-GMO, and heirloom) presented as either text or using a logo. Results from random effects Tobit models indicate that eco-label format does influence consumers’ visual attention and product valuation. Logos capture relatively more visual attention than text eco-labels. Furthermore, additional visual attention to the logos increases respondents’ bids, while visual attention to the text decreases their bids. Implications for relevant industry stakeholders are discussed.

Design of auction‐based approach for market clearing in peer‐to‐peer market platform

This study designs a market platform for peer-to-peer (P2P) energy trading in transactive energy (TE) systems, where prosumers and consumers actively participate in the market as seller or buyer to trade energy. An auction-based approach is used for market clearing in the proposed platform and a review of different types of auctions is performed. The appropriate auction approach for market clearing in the proposed platform is designed. The proposed auction mechanism is implemented in three steps namely determination, allocation and payment. This study identifies important P2P market clearing performance indices, which are used to compare and contrast the designed auction with different types of auction mechanisms. Comparative studies demonstrate the efficacy of the proposed auction mechanism for market clearing in the P2P platform.

Simultaneous service selection for multiple composite service requests: A combinatorial auction approach

Emerging developments in service-based systems have contributed to a range of proposals for the rapid development of complex applications by composing existing web services through standards-based interfaces. With the availability of a large number of such services offering identical functionality at varying levels of price and other Quality-of-Service (QoS) parameters, selecting the best web services for composition is critical– the problem referred to as composite service selection (CSS). Extant CSS approaches have tended to offer solutions to the problem for a single composite service. We extend CSS to address the setting involving multiple, simultaneous composite service requests. We propose a service selection mechanism based on combinatorial auctions to solve this problem by simultaneously matching the web service offers and requests and performing extensive evaluations in four market sectors with varying economic sizes and complexity of requests. The results indicate that the proposed simultaneous mechanism achieves a higher success rate in allocating services to requesters compared to sequential matching mechanisms. While the average cost difference in procuring composite services under the two mechanisms is not statistically significant, the simultaneous mechanism achieves more stable and homogenous costs over time.

Common Values, Unobserved Heterogeneity, and Endogenous Entry in U.S. Offshore Oil Lease Auctions

In a environment, some market participants have private information relevant to others' assessments of their own valuations or costs. Economic theory shows that this type of informational asymmetry can have important implications for market performance and market design. Yet even for the classic example of an oil lease auction, formal evidence on the presence and strength of common values has been limited by the problem of auction-level unobserved heterogeneity that is likely to affect both participation in an auction and bidders' willingness to pay. Here we develop an empirical approach for first-price sealed bid auctions with affiliated values, unobserved heterogeneity, and endogenous bidder entry. We show that important features of the model are nonparametrically identified and apply a semiparametric estimation approach to data from U.S. offshore oil and gas lease auctions. Our empirical results show that common values, affiliated private information, and unobserved heterogeneity - three distinct phenomena with different implications for policy and empirical work - are all present. Failing to account for unobserved heterogeneity obscures the empirical evidence of common values. We examine the implications of our estimates for the classic revenue ranking of sealed bid auction designs, and for the interaction between affiliation, the winner's curse, and the number of bidders in determining the aggressiveness of bidding and seller revenue.

How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach

The deepening of electricity reform results in increasingly frequent auctions and the surge of generators, making it difficult to analyze generators’ behaviors. With the difficulties to find analytical market equilibriums, approximate equilibriums were obtained instead in previous studies by market simulations, where in some cases the results are strictly bound to the initial estimations and the results are chaotic. In this paper, a multi-unit power bidding model is proposed to reveal the bidding mechanism under clearing pricing rules by employing an auction approach, for which initial estimations are non-essential. Normalized bidding price is introduced to construct generators’ price-related bidding strategy. Nash equilibriums are derived depending on the marginal cost and the winning probability which are computed from bidding quantity, transmission cost and demand distribution. Furthermore, we propose a comparative analysis to explore the impact of uncertain elastic demand on the performance of the electricity market. The result indicates that, there exists market power among generators, which lead to social welfare decreases even under competitive conditions but elastic demand is an effective way to restrain generators’ market power. The feasibility of the models is verified by a case study. Our work provides decision support for generators and a direction for improving market efficiency.

An auction-based rescue task allocation approach for heterogeneous multi-robot system

Nowadays, robots are faced with real-time, dynamic, complex and confrontational working environment. It is significant to analyze task allocation in multi-robot systems. In this paper, a dynamic auction approach for differentiated tasks under cost rigidities (DAACR) is proposed, which can obtain optimal results in the task allocation of rescue robots. To verify the feasibility of the proposed approach, we investigate the optimality of the DAACR and compare it with other task allocation approaches based on the Hungarian algorithm. The results show that robots using this algorithm can adapt to a variety of complicated work environments, accomplish more tasks in limited time, reduce the delay of task allocation, and improve the overall utility of multi-robot systems.

An Auction Approach for Cost and Schedule Management of IT Outsourcing Project

Cost and schedule management are important issues in an information technology outsourcing project. Generally, a client does not know much about market quotation and ability of vendors. Thus, an efficient approach should select a suitable vendor for the project with balanced cost and schedule. In this study, an improved standard English auction (ISEA) is designed, in which the multi-attribute situation is considered, and multi-attribute utility theory (MAUT) is introduced. A new auction protocol and a utility increment function are proposed, respectively. Finally, numerical examples are designed to demonstrate the auction process and its operation. Some insights are found: The ISEA is competent to describe the cost and schedule management process in an auction approach. The optimal incremental utility for client and vendor are found, and the proposed auction protocol is a mechanism for the client and vendor to obtain win–win results. The completely consistent results can be obtained from different size cases, which indicates that the validity of the designed auction mechanism and the effect of the utility increase function on the negotiation results.

How to restrain participants opt out in shared parking market? A fair recurrent double auction approach

We propose a fair recurrent double VCG (FRD-VCG) auction mechanism to approach the emerging shared parking management problem. In a given shared parking environment with a parking management platform and a double-side perspective, the proposed mechanism considers how to restrain the potential participants (parking slot demanders and slot suppliers) opt out, which is based on the participants’ priority attributes and are calculated with respect to historic auction records provided by the parking platform. Participants’ fairness bids are then generated combining their priority attributes and their submitted bids (bid price and parking time) with the support of a novel evaluation function, which integrates priority attributes, bid price and parking time into an output value. The parking slot allocation rule and transaction payment rule are further designed to dealing with these issues include winner determination and price setting, respectively. Simulations show advantages of the proposed FRD-VCG mechanism, i.e., comparing with the double VCG (D-VCG) mechanism for the shared parking management problem where priority attributes and evaluation function are not considered, the proposed FRD-VCG mechanism has the potential to persuade participants to remain in the market whilst it improves the market’s retention rate, the parking slot’s utilization rate and the participants’ utilities.

Facilitated sequence assembly using densely labeled optical DNA barcodes: A combinatorial auction approach.

The output from whole genome sequencing is a set of contigs, i.e. short non-overlapping DNA sequences (sizes 1-100 kilobasepairs). Piecing the contigs together is an especially difficult task for previously unsequenced DNA, and may not be feasible due to factors such as the lack of sufficient coverage or larger repetitive regions which generate gaps in the final sequence. Here we propose a new method for scaffolding such contigs. The proposed method uses densely labeled optical DNA barcodes from competitive binding experiments as scaffolds. On these scaffolds we position theoretical barcodes which are calculated from the contig sequences. This allows us to construct longer DNA sequences from the contig sequences. This proof-of-principle study extends previous studies which use sparsely labeled DNA barcodes for scaffolding purposes. Our method applies a probabilistic approach that allows us to discard “foreign” contigs from mixed samples with contigs from different types of DNA. We satisfy the contig non-overlap constraint by formulating the contig placement challenge as a combinatorial auction problem. Our exact algorithm for solving this problem reduces computational costs compared to previous methods in the combinatorial auction field. We demonstrate the usefulness of the proposed scaffolding method both for synthetic contigs and for contigs obtained using Illumina sequencing for a mixed sample with plasmid and chromosomal DNA.

Challenges in managing freshwater fishery resource through Lebak Lebung Auction approach: a case study in Pangkalan Lampam District Ogan Komering Ilir Regency

Responsible management of fishery resources has been a tradition of Ogan Komering Ilir (OKI) of South Sumatera for generations. It was recorded that since 1630 the Dutch Government had made auction policy for managing water territory in OKI Regency as an effort to preserve nature and to avoid the conflict of inland water ownership. Currently, the community-based management model has been adopted by local governments into formal regulations, known as Regional Regulation on Auction of Flood Water Swamp and Rivers (Lelang Lebak Lebung dan Sungai or L3S). This paper describes the success factors and the threats for the failure of the L3S management model in OKI Regency, based on a case study in Pangkalan Lampam District. The study showed that the management mechanism through the L3S system had been well instituted and become a well-established management practice. The management model is in line with the principle of co-management and the approach has become critical success factor in L3S management. However, ecological, economic and social aspects influence the sustainability of such fishery management model. Besides, L3S management model faces limited data and information related to fish stocks, which result in difficulties in determining the total allowable catch.

Coordinating on Who Dopes: An All Pay Auction Approach to Model Doping in Sport

We consider a two stage model of doping in sport in which athletes choose how much to dope and then how much effort to exert. Payoffs are determined by an all-pay auction where the bid or score depends on ability, doping and effort. In a very general setting we show that, in equilibrium, those with an ability level close to a clearly defined margin have most incentive to dope. Thus, it could be that the most able and least able athletes refrain from doping while those 'in the middle' dope. One can obtain, however, multiple equilibria. This results in a non-trivial coordination problem, analogous to that in market entry games, of who will dope.

Common Values, Unobserved Heterogeneity, and Endogenous Entry in U.S. Offshore Oil Lease Auctions

An oil lease auction is the classic example motivating a common values model. However, formal testing for common values has been hindered by unobserved auction-level heterogeneity, which is likely to affect both participation in an auction and bidders' willingness to pay. We develop and apply an empirical approach for first-price sealed bid auctions with affiliated values, unobserved heterogeneity, and endogenous bidder entry. The approach also accommodates spatial dependence and sample selection. Following Haile, Hong and Shum (2003), we specify a reduced form for bidder entry outcomes and rely on an instrument for entry. However, we relax their control function requirements and demonstrate that our specification is generated by a fully specified game motivated by our application. We show that important features of the model are nonparametrically identified and propose a semiparametric estimation approach designed to scale well to the moderate sample sizes typically encountered in practice. Our empirical results show that common values, affiliated private information, and unobserved heterogeneity - three distinct phenomena with different implications for policy and empirical work - are all present and important in U.S. offshore oil and gas lease auctions. We find that ignoring unobserved heterogeneity in the empirical model obscures the presence of common values. We also examine the interaction between affiliation, the winner's curse, and the number of bidders in determining the aggressiveness of bidding and seller revenue.

Virtualized resource sharing in cloud radio access networks: An auction approach

As mobile terminals proliferate and mobile Internet traffic explodes, demand for efficient and high capacity cellular access escalates. Two fundamental techniques are being implemented to help meet such a demand. The first is virtualization based, centralized cloud processing. Baseband signals are sampled and transmitted through front-haul links to a mobile cloud, for processing by mobile base station instances deployed in an on-demand fashion. User and channel information are aggregated to the cloud, facilitating optimized decision making. The second is the separation of infrastructure ownership from service provisioning in cellular networks. The “Tower” company now specializes in deployment and maintenance of the cloud-radio access network (C-RAN) infrastructure. Mobile operators focus instead on their sole business of wireless service provisioning. Mobile operators lease C-RAN resources that include spectrum resources at remote radio heads, front-haul bandwidth and mobile base station instances. This work proposes a natural auction approach for inter-operator resource sharing, where each operator bids a capacitated sub-network of the C-RAN. Drawing from the theories of Maximal-in-Range auctions and efficient graph algorithms, we design and test a C-RAN resource auction that is truthful, polynomial-time computable, and achieves close-to-optimal social welfare.

Crowdsourcing Sensing to Smartphones: A Randomized Auction Approach

Crowdsourcing to mobile users has emerged as a compelling paradigm for collecting sensing data over a vast area for various monitoring applications. It is of paramount importance for such crowdsourcing paradigm to provide effective incentive mechanisms. State-of-the-art auction mechanisms for crowdsourcing to mobile users are typically deterministic in the sense that for a given sensing job from a crowdsourcer, only a small set of smartphones are selected to perform sensing tasks and the rest are not selected. One apparent disadvantage of such deterministic auction mechanisms is that the diversity with respect to the sensing job is reduced. As a consequence, the quality of the collected sensing data is also decreased. This is due to failure to exploit the intrinsic advantage of the large set of diverse mobile users in a mobile crowdsourcing network. In this paper, we propose a randomized combinatorial auction mechanism for the social cost minimization problem , which is proven to be NP-hard. We design an approximate task allocation algorithm that is near optimal with polynomial-time complexity and use it as a building block to construct the whole randomized auction mechanism. Compared with deterministic auction mechanisms, the proposed randomized auction mechanism increases the diversity in contributing users for a given sensing job. We carry out both solid theoretical analysis and extensive numerical studies and show that our randomized auction mechanism achieves approximate truthfulness, individual rationality, and high computational efficiency.

Negotiable Auction Based on Mixed Graph: A Novel Spectrum Sharing Framework

Auction-based spectrum sharing is a promising solution to improve the spectrum utilization in 5G networks. Along with the spatial reuse, we observe that the ability to adjust the coverage of a spectrum bidder can provide room to itself for further negotiation while auctioning. In this paper, we propose a novel economic tool, size-negotiable auction mechanism (SNAM), which provides a hybrid solution between auction and negotiation for multi-buyers sharing spectrum chunks from a common database. Unlike existing auction-based spectrum sharing models, each bidder of the SNAM submits its bid for using the spectrum per unit space and a set of coverage ranges over which the bidder is willing to pay for the spectrum. The auctioneer then coordinates the interference areas (or coverage negotiation) to ensure no two winners interfere with each other while aiming to maximize the auction’s total coverage area or revenue. In this scenario, the undirected graph used by existing auction mechanisms fails to model the interference among bidders. Instead, we construct a mixed interference graph and prove that SNAM’s auctioning on the mixed graph is truthful and individually rational. Simulation results show that, compared with existing auction approaches, the proposed SNAM dramatically improves the spatial efficiency, hence leads to significantly higher seller revenue and buyer satisfaction under various setups. Thanks to its low complexity and low overhead, SNAM can target fine timescale trading (in minutes or hours) with a large number of bidders and requested coverages.