Auction Mechanism Design Research Articles

Performance-Based Pricing for Federated Learning via Auction

Many machine learning techniques rely on plenty of training data. However, data are often possessed unequally by different entities, with a large proportion of data being held by a small number of data-rich entities. It can be challenging to incentivize data-rich entities to help train models with others via federated learning (FL) if there are no additional benefits. This difficulty arises because these data-rich entities cannot enjoy the revenue increment generated from the improved performances on tasks controlled by data-limited entities. In this paper, we investigate pricing mechanisms through auctions for FL, focusing on auction scenarios with one data seller and some data-limited entities as buyers. The mechanisms aim to account for buyers' performance gains from the FL and provide equitable monetary compensation to the data seller. We first formulate the task as a performance-based auction mechanism design problem and offer a template that can accommodate multiple kinds of auctions with different desiderata. Utilizing this template, we instantiate different truthful strategies with different goals, including maximizing social welfare and maximizing the seller's profit in auctions. In addition, considering the randomness between the model test performance used in the auction and the actual performance in a production environment, we provide theoretical analyses to quantify the impact of the uncertainty on the social welfare or the seller's profit of auction mechanisms. We provide experimental results based on two datasets with synthetic buyers' valuation to illustrate the truthfulness, social welfare, and data sellers' profit.

Design of online combinatorial auction mechanism for urban land transfer.

With the wide application of the "Tender, Auction, and Listing" system for land in China, it is of paramount importance to establish a sound land auction mechanism so as to avoid unreasonable land allocation. However, existing studies only focus on developers who are merely concerned about land combinations and offline auctions with low efficiency and high land unsold rate. To further improve land auction mechanism, we modeled the problem of land allocation as an online combinatorial auction. On this basis, we defined a land correction coefficient and designed an optimal online combinatorial auction mechanism that enabled developers to submit multiple combinations of land. Our designed mechanism proved to satisfy both incentive compatibility (IC) and individual rationality (IR), which can prevent developers from lying in the auction while winning higher revenue, reducing land unsold rate, increasing developer turnover rate and promoting the stability of land resource allocation rate. Therefore, online combinatorial auction mechanism, both applicable and efficient, is a practical solution for improving land auctions in China.

Boundedly rational bidding decision for land auctions during the transformation of real estate markets

Real estate developers in China exhibit different boundedly rational behaviors when bidding in different land auction formats, resulting in deviation from fully rational bidding, and the transformation of China’s real estate industry has intensified this deviation. To address this problem, this study establishes a reference utility model and a subjective decision probability model under the framework of incomplete information game to characterize developers’ utility distortion and winning probability distortion in open-bid auctions and sealed-bid auctions for land, respectively. The results show that the deviation between bounded rational bidding and fully rational bidding increases as developer competition intensifies. The equilibrium bidding of cost-disadvantaged developers in the boundedly rational model deviates less from the standard model. Moreover, in sealed-bid auctions, the "hiding degree" of cost-advantaged developers is greater, showing size effects, while the bids in open-bid auctions are more complex and affected by market conditions and developers’ risk preferences. Thus, this study characterizes boundedly rational bidding in land auctions and interprets the deviation from fully rational bidding, which can provide a more real basis for land auction mechanism design.

TAFS: A Truthful Auction for IoT Application Offloading in Fog Computing Networks

Emerging as an alternative to cloud computing, fog computing is expected to provide low-latency, high-throughput, reliable services for ever-growing Internet of Things (IoT) applications, especially real-time applications with strict responsiveness requirements. By offloading time-critical and computation-intensive applications to proximal fog nodes (FNs), both application response time and network congestion can be markedly reduced. However, the FNs commonly suffer from limited resources compared to cloud computing nodes and, hence, may not serve all application users with guaranteed performance. The dynamic and heterogeneous nature of FNs also brings difficulty and overhead to fog computing resource management. These issues are addressed in the present study with the design of a double auction mechanism, namely, truthful auction for the fog system (TAFS), which provides incentives for FNs to satisfy as many application demands as possible with guaranteed performance. TAFS takes into account the latency tolerance of application users during the FN assignment and resource allocation to satisfy real-time requirements. We theoretically prove that TAFS satisfies several desired economic properties, including truthfulness, individual rationality, and budget balance. The performance of TAFS is evaluated through simulation experiments.

End-to-End Service Auction: A General Double Auction Mechanism for Edge Computing Services

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.

Truthful multi-attribute multi-unit double auctions for B2B e-commerce logistics service transactions

This study investigates truthful multi-attribute multi-unit double auction mechanism design problem for B2B e-commerce logistics service transactions (LSTs). The optimization problem of the auctioneer is modeled as an integer program to maximize social welfare where the utility of shippers is related to their monetary profits and non-price attributes of corresponding carriers. To handle the information asymmetry of price and non-price attributes, a Multi-Attribute Multi-unit Trade Reduction (MA-MTR) mechanism is proposed to resolve the problem in a decentralized setting. A family of Enhanced Multi-Attribute Multi-unit Trade Reduction (E-MA-MTR) mechanisms is then designed to achieve fair allocation and capture the auctioneer’s tradeoff between various operational objectives. A family of Improved Multi-Attribute Multi-unit Trade Reduction (I-MA-MTR) auction mechanisms with elaborate efficiency-loss-alleviation strategies is further proposed to ameliorate efficiency performance. Theoretical analysis manifests that all above mechanisms satisfy incentive compatibility (IC), balanced budget (BB), individual rationality (IR) and asymptotical efficiency (AsE). Numerical results further demonstrate the superiority of the MA-MTR mechanism over its single-attribute counterpart, verify the effectiveness of relevant strategies in the E-MA-MTR and I-MA-MTR mechanisms and shed light on the choice of alternative mechanisms.

Auction mechanism design of the Chinese national carbon market for carbon neutralization

The carbon market auction mechanism is an important policy tool for carbon pricing and a key mechanism that supports carbon emission neutralization, especially for China. A few systematic studies exist on China’s carbon market auction mechanism. This article focuses on the five auction mechanisms in Chinese pilot emission trading schemes (ETS), reviews the structures and bidding situation of the five-pilot auction mechanism, extracts the similarities, and analyzes their different features, such as auction mode, bidding scale, participants, pricing mode, auction frequency, and so on. This study conducts an in-depth analysis of the carbon allowance auction mechanism in the Guangdong pilot ETS of China, including its development and the evolution of the key elements, its operational effects, and related disputes. Finally, this study puts forward the trend forecast and suggestions for the Chinese allowance auction mechanism, such as the time window of launching national allowance auctions, the most likely auction mode, carbon pricing, and bidding revenue management. Carbon pricing by auction is the most powerful policy tool for addressing carbon emissions reduction and implementing the Glasgow Climate Pact.

Mechanism Design for Perturbation Stable Combinatorial Auctions

Motivated by recent research on combinatorial markets with endowed valuations by (Babaioff et al., EC 2018) and (Ezra et al., EC 2020), we introduce a notion of perturbation stability in Combinatorial Auctions (CAs) and study the extent to which stability helps in social welfare maximization and mechanism design. A CA is γ-stable if the optimal solution is resilient to inflation, by a factor of γ ≥ 1, of any bidder’s valuation for any single item. On the positive side, we show how to compute efficiently an optimal allocation for 2-stable subadditive valuations and that a Walrasian equilibrium exists for 2-stable submodular valuations. Moreover, we show that a Parallel 2nd Price Auction (P2A) followed by a demand query for each bidder is truthful for general subadditive valuations and results in the optimal allocation for 2-stable submodular valuations. To highlight the challenges behind optimization and mechanism design for stable CAs, we show that a Walrasian equilibrium may not exist for γ-stable XOS valuations for any γ, that a polynomial-time approximation scheme does not exist for (2 − ε)-stable submodular valuations, and that any DSIC mechanism that computes the optimal allocation for stable CAs and does not use demand queries must use exponentially many value queries. We conclude with analyzing the Price of Anarchy of P2A and Parallel 1st Price Auctions (P1A) for CAs with stable submodular and XOS valuations. Our results indicate that the quality of equilibria of simple non-truthful auctions improves only for γ-stable instances with γ ≥ 3.

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform.

Federated learning has demonstrated strong capabilities in terms of addressing concerns related to data islands and privacy protection. However, in real application scenarios, participants in federated learning have difficulty matching. For example, two companies distributed in different regions do not know that the other party also needs federated learning in the case of information asymmetry. Therefore, it is difficult to build alliances. To enable suppliers and consumers to find one or more federated learning objects that are relatively satisfactory in a short time, this paper considers the idea of establishing a federated learning advertising platform, where data transactions need to consider privacy protection. A sponsored search auction mechanism design method is introduced to solve the problem of ranking the presentation order of participant advertisements. Due to the potential malicious bidding problem, which occurs when using the classic sponsored search auction mechanism under the federated learning scenario, this paper proposes a novel federated sponsored search auction mechanism based on the Myerson theorem, improving upon the ranking index used in the classic sponsored search auction mechanism. A large number of experimental results on a simulation data set show that our proposed method can fairly select and rank the data providers participating in the bidding. Compared with other benchmark mechanisms, the malicious bidding rate is significantly decreased. In the long run, the proposed mechanism can encourage more data providers to participate in the federated learning platform, thus continuously promoting the establishment of a federated learning ecosystem.

From Resource Auction to Service Auction: An Auction Paradigm Shift in Wireless Networks

In 5G and beyond, the newly emerging services, such as edge computing/intelligence services, may demand the provision of heterogeneous communications, computing, and storage (CCS) resources on and across network entities multihop apart. In such cases, traditional resource-oriented auction schemes, where buyers place bids on resources, may not be effective in providing end-to-end (E2E) quality-of-service (QoS) guarantees. To overcome these limitations, in this article, we coin the concept of E2E service auction where the auction commodities are E2E services rather than certain resource. Under this framework, buyers simply bid for services with E2E QoS requirements without having to know the inner working (which resources are behind). To guarantee E2E QoS for winning bids while ensuring essential economic properties, E2E service auction requires addressing the joint problem of network optimization and auction design with both economical and QoS constraints. To substantiate the mechanism design, we illustrate how to devise E2E service auctions for edge computing systems under various scenarios. We also identify the research opportunities on E2E service auction mechanism design for other critical use cases, including edge intelligence.

Auction theory and a note on game mechanisms

Abstract This paper will review important topics on the subject of auction theory and mechanism design, these include: efficiency first and foremost, also revenue comparison between different types of auctions and the issue of incentive compatibility, individual rationality with the general idea and proof that bilateral trade is inefficient. Mechanism design theory tells us that if buyers and sellers both have private information full efficiency is impossible, however Vickrey auction (single unit auction) will be efficient i.e. will put the goods in the hands of the buyers that value them most. However, the conclusion from this paper is that because of overvaluation of bidders the main result is inefficient, i.e. bids are too high. When weak and strong bidders are compared the main conclusion is that strong bidders’ expected payoff is higher in second price auction (SPA), while weak bidder prefers first price auction (FPA) bid.

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Driven by the vision of cloud and edge computing as the 5th utility, a major challenge is the efficient resource management and pricing taking into account incentives and preferences of cloud/edge providers, users, and the system. In recent years, auction mechanism design has gained wide attention as a tool for addressing this challenge. Here existing surveys lack detailed studies in this area, hence this work presents a comprehensive survey on the state-of-the-art auction-based mechanisms in the field of cloud/edge computing. First, the problem statements and background are presented for cloud/edge computing and auction theory. This is followed by a collaboration model on the benefits of auction mechanism designs to cloud/edge computing. Next, various auction mechanisms in cloud computing are reviewed based upon the following aspects: (1) direction of bids—forward, reverse, and two-sided auctions, (2) the heterogeneity of resources—combinatorial auctions, (3) the number and types of adopted attributes in winner determination—multi-attribute auctions, (4) the setting of auction—offline, online, or sequential auctions, and (5) the interoperability among cloud providers—inter-clouds. For each aspect, existing studies are summarized along with their saliencies and drawbacks. Moreover, existing auction mechanisms in the context of edge computing are also presented. Our comprehensive survey shows that although significant progress has been achieved in this field, there still exist key challenges that need to be investigated, as discussed here to provide future research directions.

Auction Mechanism Design for Quota of Construction Land Based on Time Value of Projects on Land

Abstract The main function of the quota of construction land, which is generated through famers reclaiming their idle homesteads, is to have the land be developed in advance. The value of the quota...

Strategy-Proof and Non-Wasteful Multi-Unit Auction via Social Network

Auctions via social network, pioneered by Li et al. (2017), have been attracting considerable attention in the literature of mechanism design for auctions. However, no known mechanism has satisfied strategy-proofness, non-deficit, non-wastefulness, and individual rationality for the multi-unit unit-demand auction, except for some naïve ones. In this paper, we first propose a mechanism that satisfies all the above properties. We then make a comprehensive comparison with two naïve mechanisms, showing that the proposed mechanism dominates them in social surplus, seller's revenue, and incentive of buyers for truth-telling. We also analyze the characteristics of the social surplus and the revenue achieved by the proposed mechanism, including the constant approximability of the worst-case efficiency loss and the complexity of optimizing revenue from the seller's perspective.

Efficient and Strategy-Proof Multi-Unit Object Allocation with Money: (Non)decreasing Marginal Valuations without Qquasi-Linearity

We consider the problem of allocating multiple units of an indivisible object among agents and collecting payments. Each agent can receive multiple units of the object, and his (consumption) bundle is a pair of the units he receives and his payment. An agent's preference over bundles may be non-quasi-linear, which accommodates income effects or soft budget constraints. We show that the generalized Vickrey rule is the only rule satisfying efficiency, strategy-proofness, individual rationality, and no subsidy for losers on rich domains with non-decreasing marginal valuations. We further show that if a domain is minimally rich and includes an arbitrary preference exhibiting both decreasing marginal valuations and a positive income effect, then no rule satisfies the same four properties. Our results suggest that in non-quasi-linear environments, the design of an efficient multi-unit auction mechanism is possible only when agents have non-decreasing marginal valuations.

Design of Double Auction Mechanism Based on Social Network

In the traditional double auction market, the number of achievable tradable pairs are usually limited by the market size. To solve this problem, we propose new double auction mechanisms based on social networks to realize market expansion. In this paper, we examine the double auction market, which consists of a group of sellers and a group of buyers, where the sellers provide the same type of items, and the buyers respectively purchase one from them. In addition, every buyer can invite other potential buyers to enter the market through social networks. The goal of this paper is to propose mechanisms such that they can encourage buyers already in the market to invite other potential buyers to join the auction through social networks, and achieve an effective allocation of merchandises and increase profits for sellers, which cannot be achieved under the existing double auction mechanism. We found that the extended McAfee auction mechanism cannot motivate existing buyers to invite other potential ones, while the traditional Vickrey-Clark-Groves (VCG) mechanism cannot guarantee to break even. To solve these problems, we propose two mechanisms, which are called information network auction (INA) mechanism and double network auction (DNA) mechanism. Both of these mechanisms can encourage old buyers in the market to invite new potential customers to participate. Moreover, INA focuses on achieving more effective allocation and DNA focuses on ensuring a balance between income and expenditure.

Privacy-Preserving Spectrum Auction Design: Challenges, Solutions, and Research Directions

Spectrum auction is deemed as an efficient approach for redistributing spectrum and realizing dynamic spectrum access. Though there have been extensive research endeavors in designing spectrum auctions that are truthful with desirable social welfare, security and privacy concerns give rise to new challenges and calls for the design of privacy- preserving spectrum auction mechanisms. In this article, we begin with an overview of conventional spectrum auction mechanisms, then discuss the potential security threats in them. In particular, bidders' private information, for example, bid values and geo-locations, are submitted to the non-trustworthy auctioneer and can be observed by rival bidders who may leverage this information to rig the auction. To address this problem, we outline the framework for privacy-preserving auction mechanism design, which applies cryptographic tools to the original auction and ensures that the private information of bidders is protected, while the basic functionalities of the auction are maintained. Based on the proposed framework, we further construct a privacy-preserving heterogeneous spectrum double auction, which is shown to uphold the closely similar allocation efficiency with the original auction, and realizes privacy preservation with moderate communication and computation overheads.

IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility

Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform the state of the art work by improving the efficiency in recruiting participants by 30 percent.

Double Auction Mechanism Design for Video Caching in Heterogeneous Ultra-Dense Networks

Recently, wireless streaming of on-demand videos of mobile users (MUs) has become the major form of data traffic over cellular networks. As a response, caching popular videos in the storage of small base stations (SBSs) has been regarded as an efficient approach to reduce the transmission latency and alleviate the data traffic loaded over backhaul channels. This paper considers a small-cell based caching market composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, the MNO manages and operates its SBSs, and assigns these SBSs’ storage to different VSPs, who have caching requirements. However, videos have different popularities and MUs present different preferences to these VSPs when they request videos. In addition, the caching service brings different utilities to different VSPs as well as that providing caching service to different VSPs causes distinct costs to the MNO. Such privacy information cannot be aware of among VSPs and the MNO. Therefore, to elicit this hidden information, this paper designs a double auction-based caching mechanism, which ensures the efficient operation of the market by maximizing the social welfare, i.e., the gap between VSPs’ caching utilities and MNO’s caching costs. Moreover, this paper demonstrates the economic properties of the designed caching mechanism, which are also validated by the simulation results.

Towards an efficient market mediator for divisible resources

Auction-based markets of divisible resources have proliferated over recent years. One fundamental problem facing every market mediator is how to achieve market efficiency for optimal social welfare, especially when a limited number of agents forms a monopolistic or oligopolistic market, because each agent’s selfish strategic behavior may lead to serious degradation in efficiency. In general, it is difficult for a market mediator to achieve efficiency since agents’ preferences are hidden information that they are unwilling to reveal due to security and privacy concerns. Therefore, the design of auction mechanisms should align the selfish behavior of agents with the altruistic objective of social welfare and allow the mediator to elicit necessary private information during the auction process.In this paper, we consider a market of divisible resource consisting of agents on both sides of demand and supply. We design an adaptive auction framework for a market mediator to achieve efficient resource allocation and acquisition. Our novel design generalizes demand/supply function bidding mechanisms by introducing price differentiation via tunable parameters. We design algorithms that enable the mediator and agents to jointly run the market in an adaptive fashion: the mediator sends market signals to agents; each agent submits her bid based on the signals in a distributed manner; the mediator adjusts tunable parameters based on bids and update market signals. We also design an adaptive algorithm to dynamically determine the optimal amount of resource that needs to be transacted so as to maximize social welfare, if not known a priori. By utilizing our market mechanisms, the market mediator will be able to reach an efficient market outcome under Nash equilibrium.