Auction Theory Research Articles

Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising

Digital advertising constitutes one of the main revenue sources for online platforms. In recent years, some advertisers tend to adopt auto-bidding tools to facilitate advertising performance optimization, making the classical utility maximizer model in auction theory not fit well. Some recent studies proposed a new model, called value maximizer, for auto-bidding advertisers with return-on-investment (ROI) constraints. However, the model of either utility maximizer or value maximizer could only characterize partial advertisers in real-world advertising platforms. In a mixed environment where utility maximizers and value maximizers coexist, the truthful ad auction design would be challenging since bidders could manipulate both their values and affiliated classes, leading to a multi-parameter mechanism design problem. In this work, we address this issue by proposing a payment rule which combines the corresponding ones in classical VCG and GSP mechanisms in a novel way. Based on this payment rule, we propose a truthful auction mechanism with an approximation ratio of 2 on social welfare, which is close to the lower bound of at least 5/4 that we also prove. The designed auction mechanism is a generalization of VCG for utility maximizers and GSP for value maximizers.

Budget-feasible Sybil-proof mechanisms for crowdsensing

The rapid use of smartphones and devices leads to the development of crowdsensing (CS) systems where a large crowd of participants can take part in performing data collecting tasks in large-scale distributed networks. Participants/users in such systems are usually selfish and have private information, such as costs and identities. Budget-feasible mechanism design, as a sub-field of auction theory, is a useful paradigm for crowdsensing, which naturally formulates the procurement scenario with buyers' budgets being considered and allows the users to bid their private costs. Although the bidding behavior is well-regulated, budget-feasible mechanisms are still vulnerable to the Sybil attack where users may generate multiple fake identities to manipulate the system. Thus, it is vital to provide Sybil-proof budget-feasible mechanisms for crowdsensing. In this paper, we design a budget-feasible incentive mechanism which can guarantee truthfulness and deter Sybil attack. We prove that the proposed mechanism achieves individual rationality, truthfulness, budget feasibility, and Sybil-proofness. Extensive simulation results further validate the efficiency of the proposed mechanism.

A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems

This article deals with an artificial intelligence (AI) framework to support Internet-of-everything (IoE) applications over sixth-generation wireless (6G) networks. An integrated IoE-Edge Intelligence ecosystem is designed to effectively face the problems of Virtual Machines (VMs) placement based on their popularity, computation offloading optimization, and system reliability improvement predicting compute nodes faults. The main objective of the article is to increase performance in terms of minimization of worst end-to-end (e2e) delay, percentage of requests in outage, and the enhancement of reliability. The article focuses on the following main issues: (i) proposal of a channel-aware federated learning (FL) approach to forecast the popularity of the VMs required by IoE devices; (ii) use of an AI-based channel conditions forecasting module at the benefits of the FL process; (iii) development of a suitable VMs placement on the basis of their popularity and of an efficient tasks allocation technique based on a modified version of the auction theory (AT) and a proper matching game; (iv) enhancement of the system reliability by an echo-state-network (ESN), located on each computation node and running in the background to predict failures and anticipate tasks migration. Numerical results validate the effectiveness of the proposed strategy for IoE applications over 6G networks.

Blockchain-based collaborative edge computing: efficiency, incentive and trust

The rise of 5G technology has driven the development of edge computing. Computation offloading is the key and challenging point in edge computing, which investigates offloading resource-intensive computing tasks from the user side to the cloud or edge side for processing. More consideration needs to be given to load balancing, user variability, and the heterogeneity of edge facilities in relevant research. In addition, most of the research around edge collaboration also revolves around cloud-side collaboration, which pays relatively little attention to the collaboration process between edge nodes, and the incentive and trust issues of the collaboration process need to be addressed. In this paper, we consider the impact of the user demand variability and the edge facility heterogeneity, then propose a method based on Vickrey-Clarke-Groves (VCG) auction theory to accommodate the edge demand response (EDR) process where the number of users and service facilities do not match. The method makes users’ bidding rules satisfy the Nash equilibrium and weakly dominant strategy, which can improve the load balancing of edge nodes, has positive significance in improving the edge resource utilization and reducing the system energy consumption. In particular, combined with blockchain, we further optimize the incentive and trust mechanism of edge collaboration and consider three scenarios: no collaboration, internal collaboration, and incentive collaboration. We also consider the impact of the user task’s transmission distance on the quality of experience (QoE). In addition, we illustrate the possible forking attack of blockchain in collaborative edge computing and propose a solution. We test the performance of the proposed algorithm on a real-world dataset, and the experimental results verify the algorithm’s effectiveness and the edge collaboration’s necessity.

Optimal Bidder Selection in Clearing House Default Auctions

Default auctions at central counterparties (or ‘CCPs’) are critically important to financial stability. However, due to their unique features and challenges, standard auction theory results do not immediately apply. This paper presents a model for CCP default auctions that incorporates the CCP’s non-standard objective of maximizing success above a threshold rather than revenue, the key question of who participates in the auction and the potential for information leakage affecting private portfolio valuations. We show that an entry fee, by appropriately inducingmembers to participate or not, can maximize the probability the auction succeeds. The result is novel, both in auction theory and as a mechanism for CCP auction design.

A model of collusion formation and measurement in the open ascending bid auctions in the agricultural markets

PurposeThis paper gives a model of collusion formation and a method of measuring the degree of it among the traders/bidders in the agricultural commodity markets in India. The important assumption is that the bidding is repetitive with a set of common bidders. The theory has been derived based on the behavior of the wholesale market of agricultural commodities in India. The paper is based on full information in the collusion formation. The paper first derives the theoretical structure of the bidders' behavior and thereafter derives a measure of collusion formation with the help of real-life data.Design/methodology/approachThe paper used the standard theory of optimization and the theory of auction and probability statistics.FindingsThis is a complete information model of cartel formation. The bidding is repetitive and continues forever in discrete time. Hence bidders behavior is observable. Using the proposed method, if the APMC measures for each market and publishes on a periodic basis, say weekly basis, then it will be easier to break the collusion in the market where relative collision is present. For example, if a farmer has three options to sell in three different markets, then the published data would help them to select the market where the degree of collusion is relatively lower. Moreover, the undesirable loss can be avoided based on the right choice of market. As a result, transaction costs will be optima.Originality/valueThe paper first derives the theoretical structure of the bidders' behavior and thereafter derives a measure of collusion formation with the help of real-life data.

Toward User-Centric Resource Allocation for 6G: An Economic Perspective

This article presents an economics-inspired solution for realizing user-centric resource allocation for 6G. Particularly, the user-centric criterion is proposed based on users subjective values, i.e., quality of experience (QoE) on services, thereby satisfying the most desired demands of all users. Furthermore, the price is utilized as the indicator of users values, and the total value of information conveyed to users is prioritized so that the user-centric resource allocation is economically efficient. In addition, a Cybertwin-assisted core network is introduced as a practical solution for implementing user-centric resource allocation. A resource market with appropriate market rules is developed from auction theory. It is demonstrated through simulations that our proposed user-centric resource allocation solution can convey more personalized value to users.

Online Hybrid Dutch Auction with both private and common value components and counteracting overpayments

Auctions have been categorized exclusively as common value (CV) or private value (PV). Due to different cost structures of buyers and demand uncertainty, few studies would dispute that most real-world auctions have both elements. The well-designed online Hybrid Dutch Auction (HDA), with a transparent e-platform, can tackle collusion, overpayment, entry deterrence, and price discovery in public asset marketplaces.For the first time, this paper models a hybrid auction with both PV and CV components. We nest two auction theory paradigms for the HDA with a two-dimensional signal model and report a strategic analysis of equilibrium bidding behavior. The findings reveal that: (1) increased competition can mitigate the negative effect of uncertainty about the CV on revenue and bidding behavior; (2). Dynamic inconsistency benefits both parties. (3) Interestingly, the correct answer to the dilemma between attracting more buyers and gathering more information depends on the precision of the seller’s disclosed information.

A two-phase combinatorial double auction and negotiation mechanism for socialized joint reserve mode in emergency preparedness

Natural disasters seriously threaten the safety of human life and property. In order to provide sufficient emergency supplies instantly after disasters, it is necessary to reserve them in advance. The socialized joint reserve mode (SJRM) for emergency supplies has increasingly become a meaningful way to improve emergency preparedness capacity. Aiming to establish a market-oriented trading method of reserve services and allocate them efficiently, this research designs a two-phase combinatorial double auction and negotiation mechanism. The specific objective of this mechanism is to determine the transaction relationship, quantity, and price. In the first phase, the government, humanitarian relief organizations (HROs), enterprises and supermarkets constitute reserve service transaction's demand and supply sides. Their benchmark bidding strategy is given, and winners are determined by maximizing social surplus. In the second phase, the winners hold negotiations to discuss the supply priority of emergency materials and determine the trade quantity and price of the future. The negotiation contains two situations: oversupply and short supply. The decision-making results of the buyer and the seller are given respectively. A numerical example and sensitivity analysis are proposed to show the process and results of the auction and negotiation. The research shows that the mechanism designed in this paper can achieve the cooperation of multiple buyers and sellers in multiple periods. It proves that applying auction theory to emergency management practice has good feasibility and applicability.

An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning

In the fog computing paradigm, if the computing resources of an end device are insufficient, the user’s tasks can be offloaded to nearby devices or the central cloud. In addition, due to the limited energy of mobile devices, optimal offloading is crucial. The method presented in this paper is based on the auction theory, which has been used in recent studies to optimize computation offloading. We propose a bid prediction mechanism using Q-learning. Nodes participating in the auction announce a bid value to the auctioneer entity, and the node with the highest bid value is the auction winner. Then, only the winning node has the right to offload the tasks on its upstream (parent) node. The main idea behind Q-learning is that it is stateless and only considers the current state to perform an action. The evaluation results show that the bid values predicted by the Q-learning method are near-optimal. On average, the proposed method consumes less energy than traditional and state-of-the-art techniques. Also, it reduces the execution time of tasks and leads to less consumption of network resources.

Auction Pricing Mechanism of Construction Land Quota Based on Preference

In order to study the impact of the location preference of construction land on the auction pricing strategy of construction land quota, this study constructs a first-price auction and two-price auction model for bidders participating in the construction land quota and construction land auction based on the sequential auction theory. Therefore, the role of preference is discussed, and finally the conclusion is verified by an example. The results show that under the two auction modes, preference has a positive impact on the bidding strategy of construction land quota and farmers’ expected income. Compared with sequential first-price auction, the bidding strategy of sequential second-price auction is more competitive, and the quota equilibrium bidding price and farmers’ income obtained are higher.

Achieving the objectives of renewable energy policy – Insights from renewable energy auction design in Europe

Many countries choose policy objectives around their renewable energy expansion targets, such as efficiency and effectiveness. Due to the different nature and impact of those objectives, not all can be achieved simultaneously, so prioritising objectives is advisable. In this paper, we theoretically analyse the relationship between objectives and show that most European countries with renewable energy auctions in place have defined incoherent policy strategies in their respective renewable energy legislation. Based on these strategies, we analyse which objectives lead to the choice of which auction design elements conducting a qualitative comparative analysis. Considering 269 auction rounds from 20 European countries in the period 2011–2020, not all resulting choices of auction designs are in line with auction theory and existing literature’s findings on the relationship between objectives and auction design. Nevertheless, we show that on a country-level basis, most considered countries choose congruent auction designs, which either support all their (coherent) stated objectives or prioritise one or several of their incoherent objectives. For those countries with an incongruent auction design, we give recommendations on how to adapt their auction design depending on their exact choice of policy objectives.

SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions

As a newly emerging distributed machine learning technology, federated learning has unique advantages in the era of big data. We explore how to motivate participants to experience auctions more actively and safely. It is also essential to ensure that the final participant who wins the right to participate can guarantee relatively high-quality data or computational performance. Therefore, a secure, necessary and effective mechanism is needed through strict theoretical proof and experimental verification. The traditional auction theory is mainly oriented to price, not giving quality issues as much consideration. Hence, it is challenging to discover the optimal mechanism and solve the privacy problem when considering multi-dimensional auctions. Therefore, we (1) propose a multi-dimensional information security mechanism, (2) propose an optimal mechanism that satisfies the Pareto optimality and incentive compatibility named the SecMDGM and (3) verify that for the aggregation model based on vertical data, this mechanism can improve the performance by 2.73 times compared to that of random selection. These are all important, and they complement each other instead of being independent or in tandem. Due to security issues, it can be ensured that the optimal multi-dimensional auction has practical significance and can be used in verification experiments.

Multi-round auction-based resource allocation for edge computing: Maximizing social welfare

5G NR-Lite technology still suffers from inadequate response to low latency, high speed and mass connectivity scenarios in high mobility conditions. Building on 4G and 5G, 6G networks will work to more fully enable the Internet of Things (IoT) paradigm, working to provide secure wireless computing for the digital world. In order to ensure the resource utilization of edge computing, it is usually necessary to allocate resources among mobile devices. Many scholars have applied auction theory to resource allocation in mobile edge networks and achieved specific results in recent years. However, most existing studies unilaterally consider the utility of users or edge servers, and few jointly consider social welfare and user selection priorities, which cannot maximize the overall utility of mobile edge networks. This paper proposes a multi-round auction algorithm that pursues maximizing social welfare in a multi-user–multi-server network. The algorithm combines combinatorial auction with double auction, allowing users and servers to adjust their bids and asking prices in each auction round. To enhance the generalizability of the study, the algorithm takes full advantage of the flexibility of the auction mechanism and considers the users’ demand for different servers, the variability of bidding strategies, and the variability of the total resources of edge servers so that the users can obtain resources with higher priority and improve social welfare. To ensure that social welfare is maximized, we adopt the Vickrey–Clarke–Groves (VCG) payment mechanism and consider the effect of network externalities. Extensive comparative experiments show that the algorithm can effectively improve social welfare and maximize the overall benefits of the mobile edge network.

A Review of Auction Fever in Auction Theory

This paper summarizes the phenomenon of auction heat in auction theory, and mainly analyzes two different effects of auction heat: adversary effect and pseudo-endowment effect. In the pseudo-endowment effect, we have to understand that giving up something is much more painful than not getting the same thing. Thus, when an item is part of a person's endowment when the bidder forgoes the opportunity, it is considered a loss, and forgoing the opportunity to obtain the same item is considered a foregone gain. In the adversary effect, it creates a sense of competition and a desire to win. In addition, this paper also introduces the process of auction fever and discusses the positive or negative effects of auction fever on people in real life.

Joint Video Packet Assignment, Power Control and User Scheduling Over Cognitive Multi-Homing Heterogeneous NOMA Networks

Non-orthogonal multiple access (NOMA)-based cognitive heterogeneous multi-homing networks is a very important scenario in the future wireless networks. In this work, we formulate a joint video packet assignment, power control and user scheduling problem as a mixed integer non-linear programming (MINLP) to maximize the total video transmission quality for cognitive multi-homing heterogeneous NOMA networks, which is subject to the maximum accessed number of secondary users at each subchannel, video encoding characteristics, maximum interference power constraint and total available power constraint. For the joint video packet assignment, power control and user scheduling problem, we divide it into a video packet assignment subproblem, a power control subproblem and a secondary user scheduling subproblem for cognitive multi-homing heterogeneous NOMA networks. Firstly, the secondary user scheduling algorithm is proposed using the greedy method. Then, we utilize successive convex approximation (SCA) method to transform the power control subproblem into a convex programming problem, and an approximated optimal power control algorithm is proposed with the dual decomposition method. Finally, a heuristic video packet assignment algorithm is designed, which utilizes the auction theory. Numerical simulation results demonstrate that the proposed algorithms not only improve the video transmission quality, but also enhance the total throughput of cognitive multi-homing heterogeneous NOMA networks.

Network Audio Data and Music Composition Teaching Based on Heterogeneous Cellular Network.

With the rapid development of services such as Industry 4.0 and Internet of Vehicles, it is difficult for traditional cellular networks to meet the needs of network users for quantification, diversification, and greenness in the future. Various cellular networks expand multiple micro-cell nodes and relay nodes under macro-cells to form a multilayer network architecture. Based on this, in the process of data transmission, the links have been repeatedly reduced, and at the same time, the terminal power consumption has been reduced and the running system has been improved. This article will use the ratio of the capacity, energy consumption, and resource allocation of different cellular networks as the main means to optimize the cost. Using graph theory, auction theory, and multipurpose optimization algorithms, we have conducted in-depth research topics on upstream and downstream wireless resource allocation, network relay deployment and transmission scheduling, MMW large-scale multi-antenna transmission technology, and base station energy management. A series of optimization schemes and algorithms are proposed. This dissertation is based on the research of educational system design theory in the field of educational technology so as to carry out the research of music education system design theory suitable for the nature of music subjects and learning and education characteristics. Based on the necessity and importance of music education system design theory, the research framework of music education system design theory is constructed in advance. The voice data acquisition system collects voice data through a network grabber and real-time recording and uses signal processing and pattern recognition technology to automatically classify the collected voice data into three categories: voice, environmental sound, and music. After establishing the audio data deployment strategy, simulation method, and architecture design based on heterogeneous cellular network, this paper designs the corresponding music composition teaching system, mainly including score editing, viewing, and content display of the composition teaching system, and the final test shows that the system designed in this paper can be effectively used in various music school teaching combined with heterogeneous cellular networks.

General equilibrium methodology applied to the design, implementation and performance evaluation of large, multi-market and multi-unit policy constrained auctions

The paper reports on the methodology, experiments, design and outcome of a large auction with multiple, interdependent markets constructed from principles of general equilibrium as opposed to game theoretic auction theory. The auction distributed 18,788 entitlements to operate electronic gaming machines in 176 interconnected markets to 363 potential buyers representing gaming establishments subject to multiple policy constraints on the allocation. The multi-round auction, conducted in one day, produced over $600M in revenue. All policy constraints were satisfied. Revealed dynamics of interim allocations and new statistical tests provide evidence of multiple market convergence hypothesized by classical principles and theories of general equilibrium. Results support the use of computer supported, “tâtonnement–like” market adjustments as a reliable empirical processes and not as purely theoretical constructs.

Merging Sequence Optimization Based on Reverse Auction Theory and Merging Strategy with Active Trajectory Adjustment of Heterogeneous Vehicles

This paper investigates the optimized merging sequence (MS) and on-ramp merging strategy in mixed traffic with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). To this end, a cooperative merging sequence optimization method is first proposed based on the reverse auction. In the method, a hybrid optimized computing structure is proposed to provide the foundation for connected and human-driving vehicles (CHVs) and connected and automated vehicles (CAVs) to obtain the MS more efficiently. And a cooperative merging strategy based on all cooperative merging vehicles under mixed traffic conditions is proposed. In particular, the downstream vehicles in the strategy can change their original velocities to actively participate in the cooperative merging process according to the merging requirements of on-ramp vehicles. And the vehicles in this strategy are all subject to state constraints to avoid the adverse effects of cooperative merging behavior on the following traffic on the main road. Results of numerical experiments illustrate that the merging sequence optimization method can reduce the time to obtain the optimal MS, and the increased computational efficiency is affected by CAV penetration. In addition, in mixed traffic conditions, the cooperative merging strategy can reduce fuel consumption and the time required for merging.

(Mal-)practices of Auctioneering on the English Property Market During the 19th Century

Abstract In the 19th century, auctions became a widespread form of transaction for real estate in England. Contemporaries viewed auctioning as an effective method for the transaction of land, in terms of price determination and transparency. Contrary to these theoretical assumptions, the article shows that in practice it was not economic efficiency but group interests that played a decisive role in the establishment of the auction mechanism. The article provides an analysis of the formal and informal rules of the auction trade for real estate and argues that 19th century real estate auctions did not always lead to optimal results in terms of valuation and allocation because power structures played a greater role in them than auction theory assumes.