Auction-based Market Research Articles

Personality traits and their impact on decision-making risks in auction-based market purchases

Personality traits and their impact on decision-making risks in auction-based market purchases

Toward efficient waste electric vehicle battery recycling via auction-based market trading mechanisms

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey–Clarke–Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application.

Aggregator free ancillary services e‐market for electric vehicles using smart contracts

The energy sector is undergoing an enormous transition with new market mechanisms affecting the market and the traders. In the smart grid environment, an auction-based market can be useful to ensure trading between electric vehicles (EVs) and distribution companies (DisComs). However, the participation of EVs in the energy market poses specific issues. The first one is the involvement of aggregator/ auctioneer in trading procedures, which affects the energy trading as well as privacy of the EV owners. Second one is the auction mechanism should fulfill all critical economic properties, strategic and non-strategic constraints. In order to address these issues, a novel aggregator free mechanism is proposed in this paper allowing EVs to securely participate in the ancillary services market. A smart contract deployed in Blockchain is used for market-clearing. This paper proposes two kinds of implementation of auction-based ancillary services market in the distribution system. Through an extensive study performed on a hypothetical group of 76 561 EVs and 13 DisComs, it is found that the performance of the proposed scheme is superior in decentralized implementation compared to centralized implementation.

A Two-Stage Auction Mechanism for Cloud Resource Allocation

With the recent growth in the size of cloud computing business, handling the interactions between customers and cloud providers has become more challenging. Auction theory has been proposed to model these interactions due to its simplicity and a good match with real-world scenarios. In this paper, we consider cloud of clouds networks (CCNs) with different types of servers along with customers with heterogeneous demands. For each CCN, a CCN manager is designated to handle the cloud resources. A comprehensive framework is introduced in which the process of resource gathering and allocation is addressed via two stages, where the first stage models the interactions between customers and CCN managers, and the second stage examines the interactions between CCN managers and private cloud providers (CPs). For the first stage, an options-based sequential auction (OBSA) is adapted to the examined market, which is capable of providing truthfulness as the dominant strategy and resolving the entrance time problem. An analytical foundation for OBSAs is presented and multiple performance metrics are derived. For the second stage, two parallel markets are assumed: flat-price and auction-based market. A theoretical framework for market analysis is provided and the bidding behavior of CCN managers is described.

Toward an Automated Auction Framework for Wireless Federated Learning Services Market

In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this article, we propose an auction-based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

Paradigm shifts

Abstract The author studies the evolution of the number of coexisting beliefs in a financial market. Crucially, he undertakes to do so in a framework where the paradigms, beliefs, and models driving agents behavior are left totally unspecified; i.e., the author does not make any parametric or non-parametric model assumptions. The overreaching aim of this exercise is to characterise the dynamic of the variety of beliefs in an auction-based financial market independently of any assumptions on agents behaviors. The resulting framework may be seen as an abstract agent-based model. In a computer experiment the authors exhibits a cycle between two states, so that either all agents act according to the same belief, or there is no leading belief; i.e., there is one dominating belief, or none. Further, the author finds that the frequency of this cycle is positively linked to the quality of the information available to the agents.

Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks

As an emerging decentralized secure data management platform, blockchain has gained much popularity recently. To maintain a canonical state of blockchain data record, proof-of-work based consensus protocols provide the nodes, referred to as miners, in the network with incentives for confirming new block of transactions through a process of "block mining" by solving a cryptographic puzzle. Under the circumstance of limited local computing resources, e.g., mobile devices, it is natural for rational miners, i.e., consensus nodes, to offload computational tasks for proof of work to the cloud/fog computing servers. Therefore, we focus on the trading between the cloud/fog computing service provider and miners, and propose an auction-based market model for efficient computing resource allocation. In particular, we consider a proof-of-work based blockchain network. Due to the competition among miners in the blockchain network, the allocative externalities are particularly taken into account when designing the auction mechanisms. Specifically, we consider two bidding schemes: the constant-demand scheme where each miner bids for a fixed quantity of resources, and the multi-demand scheme where the miners can submit their preferable demands and bids. For the constant-demand bidding scheme, we propose an auction mechanism that achieves optimal social welfare. In the multi-demand bidding scheme, the social welfare maximization problem is NP-hard. Therefore, we design an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we show that our proposed auction mechanisms with the two bidding schemes can efficiently maximize the social welfare of the blockchain network and provide effective strategies for the cloud/fog computing service provider.

Combinatorial double auction-based resource allocation mechanism in cloud computing market

The cloud computing environment may be considered as market for computing and storage resources. Providers rent their available resources in the form of Virtual Machines (VM) and charge the users accordingly. One of the challenges in this market is providing a mechanism for the allocation of resources and their pricing, such that the proper benefit of both users and providers are guaranteed. In this paper, a combinatorial double auction-based market is studied in which a broker performs the allocation of the providers’ VMs according to the users’ requests. The proposed allocation problem is formulated as an integer linear programming model aiming at maximizing the total profit of users and providers. It is proved that the proposed model satisfies the desirable properties including: truthfulness, fairness, economic efficiency and allocation efficiency. Furthermore, due to the high complexity of the proposed model, a heuristic resource allocation algorithm with a quasi linear time complexity is presented. The results of evaluations confirm the good agreement of the heuristic algorithm with the optimization model in terms of allocation performance. Moreover, simulation results using CloudSim indicate that, compared to the previous works in literature, the proposed algorithm increases the profit of providers and users and reduces the resource wastage.

Adaptive Auction Framework for Spectrum Market in Cognitive Radio Networks

For the recent decade, cognitive radio networks have received much attention as an alternative to the traditional static spectrum allocation policy since the licensed spectrum channels are not being used efficiently. The most critical issue of the cognitive radio networks is how to distribute the idle spectrum channels to the secondary users opportunistically. The auction-based market is desirable for the trade of idle spectrum channels since the secondary users can purchase a channel in timely manner and the licensed primary users can earn the additional profit while not using the channels. Among the auction algorithms proposed for the spectrum market, we focus on the TASG framework, which consists of two nested auction algorithms, because it enables the group-buying of spectrum channels for the secondary users with limited budgets, and possesses many positive properties such as budget-balance, individual rationality and truthfulness. However, the TASG framework is not very attractive to the market participants since the seller earns the small revenue and the buyer has the low utility. In this paper, we propose a new auction framework for the spectrum markets, called aDaptive and Economically robust Auction-based Leasing (DEAL), that keeps all the benefits of TASG while improving the utility (or revenue) of the participants. To this end, we develop an enhanced inner-auction algorithm, called the Global Auction algorithm in our DEAL framework, and adapt the involved parameters dynamically based on the previous bids from the potential buyers. Simulation results demonstrate that our framework significantly outperforms the previous TASG.

Carbon Auction Revenue and Market Power: An Experimental Analysis

State and regional governments in the U.S. and abroad are looking to market-based approaches to mitigating greenhouse gas emissions from the electric sector, and in the U.S. as a compliance approach to meeting the aggressive targets of the Environmental Protection Agency (EPA)’s Clean Power Plan. Auction-based approaches, like those used in the Northeast U.S. and California, are both recommended strategies under the Plan and attractive to state governments because they can generate significant revenue from the sale of emissions permits. However, given the nature of imperfect competition in existing electricity markets, particularly at the state and regional level, the issue of market power is a concern at the forefront. This paper provides the results from a controlled laboratory experiment of an auction-based emissions market in the electricity sector. The results show that government revenue from auctioning emissions permits is substantially lower when market concentration is only moderately increased. The results hold significant implications for states and other subnational governments that have high revenue expectations from the auctioning of emissions permits.

Towards a secure trading of aviation CO2 allowance

The growth of world air traffic has been accompanied by a significant increase of its environmental impact, including CO2 emissions, which has forced the European Union to include aviation in its Emission Trading Scheme (EU ETS). The EU ETS is a market-based mechanism that obliges airlines to buy or sell carbon permits, thus forcing them to share confidential information with their competitors in an auction-based market. Disclosure of confidential information will be one of the main barriers for the ETS adoption. In this contribution we describe the design and implementation of a Secure Multi-party Computation framework, capable of overcoming these barriers. The framework runs as an online, cloud-based computational service for performing CO2 trading securely without the need of sharing business information. Benefits and limitations of the proposed approach are discussed, as well as the challenges to be overcome towards an operational implementation.

Approximately Truthful Mechanisms for Radio Spectrum Allocation

In wireless networks, a recent trend is to make spectrum access dynamic for efficient utilization of spectrum. In such a scenario, the spectrum is periodically allocated to wireless users using an auction-based market mechanism. A critical property required for designing such a mechanism is truthfulness, which could avoid market manipulation. Such mechanism design typically involves solving NP-hard problems; hence, approximation algorithms are always resorted to in real systems. However, recent results have suggested that it is impossible to implement reasonable approximations without losing robustness to manipulation. In this paper, we solve the problem in a novel perspective by relaxing the constraints of ensuring strong truthfulness. We discuss the concepts of approximate truthfulness and provide approximately truthful mechanisms to improve efficiency (in terms of social welfare and spectrum utilization). We first develop a computationally efficient mechanism that achieves truthful in expectation. This mechanism is based on the assumption that bidders are risk neutral. Following that, we break the assumption by proposing a hard-to-manipulate auction (HMA), which makes it hard to manipulate the auction for profit gains. Our extensive simulation results show that our mechanisms can achieve significant improvement over the state-of-the-art mechanisms.

A Combinatorial Auction-based collaborative cloud services platform

In this paper, we introduce a combinatorial auction-based market model to enable Cloud Service Providers (CSPs) to satisfy complex user requirements collaboratively, where the CSPs are connected in a social network and communication costs among them cannot be ignored. However, in many situations CSPs may lie about their private information in order to maximize their earnings. Therefore, we combine the combinatorial auction with the VCG-auction mechanism to ensure that CSPs do not lie in auction. Based on above market model, we construct a collaborative cloud platform, which is divided into three layers: The user-layer receives requests from end-users, the auction-layer matches the requests with the cloud services provided by the Cloud Service Provider (CSP), and the CSP-layer forms a coalition to improve serving ability to satisfy complex requirements of users. In fact, the aim of the coalition formation is to find suitable partners for a particular CSP, and, we propose two heuristic algorithms for the coalition formation. The Breadth Traversal Algorithm (BTA) and Revised Ant Colony Algorithm (RACA) are proposed to form a coalition when bidding for a single cloud service in the auction. The experimental results show that RACA outperforms the BTA in bid price and our methods work well compared to the existing auction-based method in terms of economic efficiency. Other experiments were conducted to evaluate the impact of the communication cost on coalition formation and to assess the impact of iteration times for the optimal bidding price.

Truthful Spectrum Auctions With Approximate Social-Welfare or Revenue

In cellular networks, a recent trend in research is to make spectrum access dynamic in the spatial and temporal dimensions for the sake of efficient utilization of spectrum. In one such model, the spectrum is divided into channels and periodically allocated to competing base stations using an auction-based market mechanism. An “efficient” auction mechanism is essential to the success of such a dynamic spectrum access model. A key objective in designing an auction mechanism is “truthfulness.” Combining this objective with an optimization of some social choice function (such as the social-welfare or the generated revenue) is highly desirable. In this paper, we design polynomial-time spectrum auction mechanisms that are truthful and yield an allocation with $O(1)$ -approximate social-welfare or revenue. Our mechanisms generalize to general interference models. To the best of our knowledge, ours is the first work to design polynomial-time truthful spectrum auction mechanisms with a constant-factor approximation of either the expected revenue or the social-welfare. We demonstrate the performance of our designed mechanism through simulations.

E-Game: A platform for developing auction-based market simulations

Trading in electronic markets has been the focus of intense research over the last few years within Computer Science and Economics. This paper discusses the need for tools to support the design and implementation of electronic market simulations or games. Such games emulate real life problems and can be used in order to conduct research on market infrastructure, negotiation protocols and strategic behaviour. To this end, we present the e-Game platform which was developed to support the design, implementation and execution of market simulations involving auctions. How the development of market games is aided is demonstrated with an example game.

An auction-based market equilibrium algorithm for a production model

We present an auction-based algorithm for computing market equilibrium prices in a production model, in which producers have a single linear production constraint, and consumers have linear utility functions. We provide algorithms for both the Fisher and Arrow–Debreu versions of the problem.