Second-price Auction Research Articles

Second-price auctions with information acquisition costs

Second-price auctions with information acquisition costs

What role do attitudes, information and taste play in consumer preferences and willingness to pay for domestic alternatives to exotic superfoods?

Superfoods are a recent health-oriented food trend, especially among younger consumers. The most well-known superfoods, such as quinoa or goji berries, are considered exotic foods, at least for the European market. This contradicts another food trend: the movement towards regional or local foods. As little is known about how consumers evaluate this trade-off when consuming superfoods, we investigate i) consumers’ preferences and willingness to pay (WTP) for domestic and exotic superfood ingredients; and ii) factors determining a higher WTP for domestic superfood alternatives. To this end, we conducted a three-step Vickrey auction of fruit smoothies with exotic and domestic superfood ingredients. A total of 116 individuals participated in the within-subjects experiment, which included an information treatment, tastings and a sensory evaluation. In general, participants perceived superfoods as a healthy but expensive food trend potentially harmful to the environment. Moreover, participants were eager to try the exotic smoothie, but agreed more strongly with statements endorsing the positive health benefits and good taste of the domestic smoothie. In general, we found a higher WTP for the domestic smoothie, which was reinforced by providing information about the origin of the fruits. After tasting, the WTP a premium for the domestic smoothie was driven by differences in sensory evaluations, but no longer by product perceptions and food neophobia. This indicates that food neophobia is related to taste uncertainty, which was resolved by the tasting. We discuss practical implications for fruit growers and processors marketing products containing novel and familiar superfood ingredients.

Buyout decisions of level-k bidders in second-price auctions

Buyout decisions of level-k bidders in second-price auctions

An online auction-based mechanism for pricing and allocation of instant delivery services

Recently, instant delivery has been growing rapidly, with numerous platforms emerging to offer such services. Requestors dynamically arrive at the platform to place delivery service requests that detail their pickup locations, recipient locations, package weights, departure times, and willingness-to-pay (WTP). The platform then uses its dedicated riders, scattered in different places, to fulfill these requests. Given the dynamic and fluctuating characteristics of the demand, coupled with limited rider resources and heterogenous pickup costs, the platform faces the critical problem of dynamically pricing the requests and assigning the riders to maximize social welfare. To address this problem, we propose an online auction-based transaction mechanism. Specifically, we first propose a scoring function to evaluate the values of the requests over multi-period operations taking into account the requests’ attributes, riders’ delivery costs, and resource availability. Based on the scoring function, we design a time-varying Vickrey–Clarke–Groves (VCG)-like payment rule that can reflect the impacts of fluctuating supply-demand imbalances. Under this rule, a requestor will pay more during undersupply periods than during oversupply periods. To carve out the different impact degrees of the supply-demand imbalances, we further consider the linear, quadratic, and exponential time-varying resource parameters to construct the payment rule. In addition, we develop an online instant delivery resource allocation model to efficiently assign the riders to fulfill the accepted requests. We show that the proposed mechanism has desirable properties (individual rationality, budget balance, and incentive compatibility) and is computationally efficient. Furthermore, we give a lower bound for the mechanism efficiency. To validate the practicality of our mechanism and get some managerial insights into the operations of the instant delivery platform, we conduct numerical studies to compare the performance of our mechanism to the First-in, first-out (FIFO) allocation mechanism and to investigate the impacts of pricing functions, rolling horizon configurations, and rider numbers on the mechanism's performance.

Bidding behaviour in experimental auctions under risk and uncertainty

AbstractThis paper explores bidding behaviour under risk and uncertainty using the Becker-DeGroot-Marschak mechanism (BDM) and second price auction (SPA). It investigates whether values elicited via the two mechanisms are consistent and whether bidding behaviour can be influenced by differences in the number and type of sources of risk and uncertainty that people face when exposed to the two mechanisms. In our experiment, subjects are exposed to non-monetary lotteries where they bid for a high-quality seafood product, but there is a chance (known or unknown) that they receive a lower quality seafood product instead. Results indicate that bidding behaviour can be influenced by the number and type of sources of risk and uncertainty that subjects face and subjects’ bidding behaviour is only consistent with standard theories of decision making under risk and uncertainty when they bid on a risky product in the SPA. Despite this, BDM and SPA elicit equal values under risk and uncertainty in this study.

A fair multi-attribute data transaction mechanism supporting cross-chain

Reliable storage and high-speed data networks enable individuals to access high-quality Internet of Things (IoT) data for scientific research through global transactions. Blockchain technology provides transparency for institutions to securely store and manage IoT data, while cross-chain transaction mechanisms facilitate the flow of IoT data. However, fairness issues may arise when it comes to cross-chain transactions of IoT data. This paper proposes a mechanism for multi-attribute data transactions to support cross-chain. The solution utilizes Vickrey–Clarke–Groves (VCG) auction, Paillier, Intel SGX, and other technologies to design a secure and equitable data seller selection scheme. The scheme ensures that the selection process for data sellers is both informed and private. Additionally, we generate a key pair for each attribute in the dataset to produce the corresponding attribute data signature. The dataset’s legitimacy is verified through batch verification to ensure that the data seller’s purchased attributes align with their requirements. The exchange of crypto assets and private keys between data sellers and buyers is designed to achieve fair payment. Our research suggests that the scheme meets the necessary security standards, and simulation results confirm its feasibility and effectiveness.

On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design

Market designers often only have partial information about the environment and prefer simple mechanisms that are robust to the underlying uncertainty. In “On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design,” Anunrojwong, Balseiro, and Besbes study the fundamental problem of selling an item to n buyers, when only an upper bound on values is known and the seller minimizes worst-case regret. They establish that the same mechanism is robustly optimal across a wide range of environments for distributions of values: i.i.d., mixtures of i.i.d., exchangeable, and affiliated (the last two capturing positive dependence). Moreover, this robust mechanism is a second-price auction with random reserve price depending on n. Without positive dependence, the problem reduces to the one-buyer case. This result supports the wide use of second-price auctions in practice and allows them to quantify the robust value of competition in auctions.

A novel experimental test of truthful bidding in second-price auctions with real objects

We present experimental evidence on bidding in second-price auctions with real objects. Our novel design, combining a second-price auction with an individual-specific binary-choice task based on the outcome of the auction, allows us to directly identify over- and under-bidding. We analyze bidding in real-object and induced-value auctions, and find significant deviations from truthful bidding in both. Overall, under-bidding is somewhat more prevalent than over-bidding; yet, the latter has a bigger magnitude, especially with induced values. At the individual level, however, we find that participants who over-bid in induced-value auctions are equally likely to over- or under-bid in real-object ones; moreover, there is no association in the size of the deviations from truthful bidding across the two types of auctions. Hence, we conclude that there is no relation between the tendency to deviate from truthful bidding in induced-value vs. real-object auctions.

Long-Term Adaptive VCG Auction Mechanism for Sustainable Federated Learning With Periodical Client Shifting

Long-Term Adaptive VCG Auction Mechanism for Sustainable Federated Learning With Periodical Client Shifting

Literature review. Pricing rule alternatives for the European day-ahead market

This literature review delves into the intricate realm of pricing rules in the day-ahead electricity market, spurred by a notable surge in 2022 electricity prices. Employing a comparative analysis of uniform pricing (UP), discriminatory pricing (DP), and the Vickrey–Clarke–Groves (VCG) pricing rule, the study navigates the complexities of these mechanisms. The evaluation unveils contrasting perspectives: the political push for consumer-friendly low prices versus the efficiency aspiration for a self-regulating market. DP, minimizing average prices but increasing volatility, and VCG, excelling in productive efficiency, are pitted against UP, the prevailing yet imperfect norm. The findings underscore the nuances of each rule; DP leads to reduced average prices and heightened volatility, while VCG ensures superior productive efficiency. Despite UP’s simplicity, it prompts inefficiencies and political costs. These insights illuminate the pivotal choice faced by policymakers, balancing efficiency, fairness, and market sustainability.

Cache Update and Delivery of Dynamic Contents: A Stochastic Game Approach

In this paper, we propose a cache update policy for wireless networks considering dynamic popularity for file requests. Our network scenario is a wireless network comprised of some cache-equipped access points (APs) which are deployed densely in an area and have connections with core network servers. We model the dynamics of file requests as Markov-modulated Poisson processes. Considering the congestion-dependent delay of APs' services and their overlapping coverage areas, we model the cache service for the cache-missed requests, as a stochastic game in which a Vickrey–Clarke–Groves (VCG) mechanism is exploited at each stage of the game as our proposed file routing policy, and the APs decide independently how to update their caches regarding the routed files. To this end, the APs' utilities are defined based on long-term average file delivery delay including the queueing delay in APs. By finding the Nash equilibrium of the game, we propose policies for both delivery and cache update of dynamic contents. Finally, by comparing numerical results of our proposed scheme with some conventional caching schemes, we show significant improvements in network performance in terms of file delivery delay in different conditions.

Vertical Equity Analysis of Parking Reservation Based on the Auction Strategy

As an on-demand mobility service, parking reservations can greatly alleviate the issue of parking challenges. There are currently three primary strategies for parking reservation: first-come-first-served, permit reservation, and auction. In contrast to the first-come-first-served and permit-reserved strategies, the auction strategy uses dynamic pricing to allocate parking supplies efficiently based on the auction, which attracts more scholars for the research. However, parking reservations based on the auction process may have an inequity issue because drivers’ age, gender, income level, and location of residence fluctuate. This inequity may limit the growth of reserved parking by influencing parking drivers’ acceptance of reserved parking. But currently, very few scholars focus on the issue of reserved parking equity, and even fewer measure this nebulous and personal issue. In consideration of this, the Lorenz curve of parking reservation and the vertical equity index of parking reservation are proposed in this paper along with the calculation method for the index, which enables the problem of reserved parking vertical equity to be visualized and made concrete. The numerical experimental method is used to analyze the vertical equity of drivers with varying income levels, utilizing the Vickrey–Clarke–Groves (VCG) auction process as an example. According to the research, loss-averse drivers are more than gain-neutral and gain-seeking drivers when the income levels of the drivers using reserved parking are the same. With the increasing number of high-income drivers involved in the parking reservation, medium to low-income drivers would lose their chances of successful reservations because of their uncompetitive bid price which leads to inequity issues when the number is less than the number of parking spaces. In contrast, the vertical equity index changes more for gain-seeking drivers while being generally steady when loss-averse and gain-neutral drivers participate. For instance, when the profit and loss coefficient is 3 and 15% of drivers with high-income levels use the parking reservation platform, the vertical equity index rises from 0.09128 to 0.45434. The reference price has a moderating influence on the vertical equity index when the number of driver participants at high-income levels remains constant. In general, within a reasonable range, the higher the reference price, the more equitable the parking reservation procedure and the lower the vertical equity index.

Dynamic Budget Throttling in Repeated Second-Price Auctions

In today's online advertising markets, a crucial requirement for an advertiser is to control her total expenditure within a time horizon under some budget. Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in. This paper provides a theoretical panorama of a single advertiser's dynamic budget throttling process in repeated second-price auctions. We first establish a lower bound on the regret and an upper bound on the asymptotic competitive ratio for any throttling algorithm, respectively, when the advertiser's values are stochastic and adversarial. Regarding the algorithmic side, we propose the OGD-CB algorithm, which guarantees a near-optimal expected regret with stochastic values. On the other hand, when values are adversarial, we prove that this algorithm also reaches the upper bound on the asymptotic competitive ratio. We further compare throttling with pacing, another widely adopted budget control method, in repeated second-price auctions. In the stochastic case, we demonstrate that pacing is generally superior to throttling for the advertiser, supporting the well-known result that pacing is asymptotically optimal in this scenario. However, in the adversarial case, we give an exciting result indicating that throttling is also an asymptotically optimal dynamic bidding strategy. Our results bridge the gaps in theoretical research of throttling in repeated auctions and comprehensively reveal the ability of this popular budget-smoothing strategy.

Overbidding and underbidding in package allocation problems

AbstractWe study the problem of allocating packages of different objects to a group of bidders. A rule is overbidding-proof if no bidder has incentives to bid above his actual valuations. We prove that if an efficient rule is overbidding-proof, then each winning bidder pays a price between his winning bid and what he would pay in a Vickrey auction for the same package. In counterpart, the set of rules that satisfy underbidding-proofness always charge a price below the corresponding Vickrey price. A new characterization of the Vickrey allocation rule is provided with a weak form of strategy-proofness. The Vickrey rule is the only rule that satisfies efficiency, individual rationality, overbidding-proofness and underbidding-proofness. Our results are also valid on the domains of monotonic valuations and of single-minded bidders. Finally a family of overbidding rules is introduced that price the assigned packages at a fixed average of the Vickrey price and the pay-as-bid price.

Trust in ESG reporting: The intelligent Veri-Green solution for incentivized verification

In today's corporate environment, Environmental, Social, and Governance (ESG) reports crucially reflect an organization's commitment to sustainability, environmental preservation, and social responsibility. As corporations share these detailed reports, the responsibility to validate and assure adherence to respected ESG benchmarks critically lies with third-party assurance organizations. However, the essential verification process often encounters challenges related to authenticity, credibility, and fairness, underscoring the need for a new solution. The selection of verifiers is a crucial aspect of this process, as their expertise and impartiality directly impact the validity and trustworthiness of the verification. Consequently, “Veri-Green,” an innovative blockchain-based incentive mechanism, has been introduced to improve the ESG data verification process. Considering potential risks in verification systems, such as reputational damage due to oversight or inadvertent approval of inaccurate data, and data security risks involving the management of sensitive organizational information, the verifier selection process needs to be thoroughly considered and designed. Through the utilization of advanced machine learning algorithms, potential verification candidates are precisely identified, followed by the deployment of the Vickrey Clarke Groves (VCG) auction mechanism. This approach ensures the strategic selection of verifiers and cultivates an ecosystem marked by truthfulness, rationality, and computational efficiency throughout the ESG data verification process. In this framework, verifiers are not only encouraged but also properly incentivized, developing a more transparent and equitable verification process, thereby driving the ESG agenda towards a future defined by genuine, impactful corporate responsibility and sustainability.

Using lotteries instead of auctioning is both inefficient and inequality-creating

Using lotteries instead of auctioning is both inefficient and inequality-creating Yew-Kwang Ng, Emeritus Professor in the Department of Economics at Monash University, compares the use of the lottery and auctioning to allocate scarce goods. In a recent eBook for Open Access Government, I discuss the anti-market sentiment, including why it is common, based on incorrect reasonings, and how recognising this mistake may make us more accepting of the beneficial market expansion. Here, I discuss a specific manifestation of this anti- market sentiment: Using the lottery to allocate scarce goods (including resources and services) instead of using the market (such as auctioning). An example is the allocation of permits for car ownership. In Singapore, a Certificate of Entitlement (COE) is required to buy a car. Singapore allocates these COEs by the most efficient Vickrey second-price auction. (1)

Implementation in undominated strategies with applications to auction design, public good provision and matching

This paper considers implementation in undominated strategies by finite mechanisms, where multiple outcomes may be implemented at a single state of the world. We establish a sufficient condition for implementation applicable in a general environment with private values. We apply it to three well-known environments and obtain strikingly permissive results. In the single-object auction, the second-price auction with a reserve price can be outperformed in terms of revenue. In the public good provision problem, the Vickrey–Clarke–Groves mechanism can be outperformed from the viewpoint of a designer who wishes to minimise deficit subject to efficiency. In the two-sided matching environment where preferences on one side of the market are private information, the social choice correspondence that outputs all stable matchings at every preference profile, is implementable.

Meta-heuristic scheduling auction applying distributed genetic algorithm

Scheduling Auction is a VCG auction, combined with scheduling, utilized where the number of and the kind of auctioned goods have yet to be determined. In its winner determination problem, not only the winners of the goods but also the schedule for providing them should be optimally determined. Due to the NP-hard nature of this problem, it is tempted to apply meta-heuristics to the problem. However, this damages some desirable properties of the VCG mechanism. Thus, we propose the Meta-Heuristic Scheduling Auction (MHSA) to mitigate this issue by using the Distributed Genetic Algorithm. MHSA solves multiple winner determination problems called “islands” in parallel using Genetic Algorithm and correlates the coverage of the solution space in each island by having genes migrate among islands. This study confirmed that the migration operation leads to a better performance.

A Cost-Aware Utility-Maximizing Bidding Strategy for Auction-Based Federated Learning.

Auction-based federated learning (AFL) has emerged as an efficient and fair approach to incentivize data owners (DOs) to contribute to federated model training, garnering extensive interest. However, the important problem of helping data consumers (DCs) bid for DOs in competitive AFL settings remains open. Existing work simply treats that the actual cost paid by a winning DC (i.e., the bid cost) is equal to the bid price offered by that DC itself. However, this assumption is inconsistent with the widely adopted generalized second-price (GSP) auction mechanism used in AFL, including in these existing works. Under a GSP auction, the winning DC does not pay its own proposed bid price. Instead, the bid cost for the winner is determined by the second-highest bid price among all participating DCs. To address this limitation, we propose a first-of-its-kind federated cost-aware bidding strategy () to help DCs maximize their utility under GSP auction-based federated learning (FL). It enables DCs to efficiently bid for DOs in competitive AFL markets, maximizing their utility and improving the resulting FL model accuracy. We first formulate the optimal bidding function under the GSP auction setting, and then demonstrate that it depends on utility estimation and market price modeling, which are interrelated. Based on this analysis, jointly optimizes in a novel end-to-end framework, and then executes the proposed return on investment (ROI)-based method to determine the optimal bid price for each piece of the data resource. Through extensive experiments on six commonly adopted benchmark datasets, we show that outperforms eight state-of-the-art methods, beating the best baseline by 4.39%, 4.56%, 1.33%, and 5.43% on average in terms of the total amount of data obtained, number of data samples per unit cost, total utility, and FL model accuracy, respectively.

Mechanism design for a fair and equitable approach to global vaccine distribution: The case of COVID-19.

Vaccines are one of the most effective tools humanity has in the fight against pandemics. One of the major challenges of vaccine distribution is achieving fair and equitable allocation across the countries of the world, regardless of their economic wealth. The self-interested behaviour of high-income countries and the underutilisation of vaccines allocated to underprepared countries are some of the failures reported during COVID-19 vaccine roll-out. These shortcomings have motivated the need for a central market mechanism that takes into account the countries' vulnerability to COVID-19 and their readiness to distribute and administer their allocated vaccines. In this paper, we leverage game theory to study the problem of equitable global vaccine distribution and propose a fair market mechanism that aligns self-interested behaviour with optimal global objectives. First, we model the interaction between a central vaccine provider (e.g. COVAX) and a country reporting its demand as a two-player game, and discuss the Nash and mixed Nash equilibria of that game. Then, we propose a repeated auction mechanism with an artificial payment system for allocating vaccines among participating countries, where each auction round is based on a Vickrey-Clarke-Groves (VCG) mechanism. The proposed allocation mechanism aims at minimising deaths and incentivises the self-interested countries to report their demand truthfully. Compared with real-world COVAX allocation decisions, our results show that the proposed auction mechanism achieves more efficient outcomes that maximise the number of averted deaths. Pragmatic considerations are investigated and policy recommendations are discussed.