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In a sealed-bid auction, bidders simultaneously submit their sealed bids to the auctioneer without knowledge of the others’ bids. The auctioneer will then declare the bidder with the highest price (or second-highest price) as the winner during the opening stage. Although existing bidding solutions focus on ensuring certain characteristics of the auction, including correctness, fairness, privacy protection, and confidentiality, it is difficult for losing bidders to verify whether the winner is a genuine bidder or just a fake bidder that is manipulated by a malicious party (e.g., a malicious auctioneer). In this paper, we introduce a fair indictment mechanism for an online sealed-bid auction that includes self-enforcing privacy. Our solution allows for an honest bidder to detect malicious activity and provides the bidder with verifiable evidence to indict a dishonest party. A successful indictment will give an incentive to the honest bidder and will result in a withdrawal of the result of the auction, whereas a failed indictment will require the involved complainer to pay a penalty. We achieve this goal using a scheme designed with an oblivious polynomial evaluation and homomorphic cryptosystem. We also involve a semi-honest verification agent in the indictment process to help the honest party verify the winning bid. This prevents the auctioneer from controlling the entire auction process. We also provide an analysis of the indictment, requirements, security and efficiency of the proposed mechanism and demonstrate the use of our solution in a multiple-item sealed-bid auction (i.e., combinatorial auction).

An English auction is studied in which bidders can acquire information during the bidding process, allowing for heterogeneity both in ex-ante private information and the cost of information acquisition. The best response has a simple characterization where the optimal information acquisition time is unaffected by the other bidders’ strategies. We prove the existence of an equilibrium in a novel way by characterizing it as a fixed point in the space of bid distributions rather than the space of bid functions. Furthermore, we show that when bidders have homogeneous ex-ante private information about valuations: (1) The English auction generates more revenue than the Vickrey auction when the number of bidders is sufficiently large; and (2) the English auction is more efficient than the Vickrey auction when the information acquisition cost are relatively small. We present numerical simulations that show that these effects can be large. Our findings provide an additional explanation for the popularity of the English auction, even in settings where the bidders’ valuations are independent.

The wide deployment of tokens for digital assets on top of Ethereum implies the need for powerful trading platforms. Vickrey auctions have been known to determine the real market price of items as bidders are motivated to submit their own monetary valuations without leaking their information to the competitors. Recent constructions have utilized various cryptographic protocols such as ZKP and MPC, however, these approaches either are partially privacy-preserving or require complex computations with several rounds. In this paper, we overcome these limits by presenting Trustee as a Vickrey auction on Ethereum which fully preserves bids’ privacy at relatively much lower fees. Trustee consists of three components: a front-end smart contract deployed on Ethereum, an Intel SGX enclave, and a relay to redirect messages between them. Initially, the enclave generates an Ethereum account and ECDH key-pair. Subsequently, the relay publishes the account’s address and ECDH public key on the smart contract. As a prerequisite, bidders are encouraged to verify the authenticity and security of Trustee by using the SGX remote attestation service. To participate in the auction, bidders utilize the ECDH public key to encrypt their bids and submit them to the smart contract. Once the bidding interval is closed, the relay retrieves the encrypted bids and feeds them to the enclave that autonomously generates a signed transaction indicating the auction winner. Finally, the relay submits the transaction to the smart contract which verifies the transaction’s authenticity and the parameters’ consistency before accepting the claimed auction winner. As part of our contributions, we have made a prototype for Trustee available on Github for the community to review and inspect it. Additionally, we analyze the security features of Trustee and report on the transactions’ gas cost incurred on Trustee smart contract.

We study a stochastic auction in a multi-items and unit-demand setting where bidders have one-dimensional type spaces. In a usual Pareto-efficient auction such as the VCG auction, items are allocated deterministically to bidders depending on their bids. However, even if an auctioneer implements a deterministic auction repeatedly, data that the auctioneer and bidders can get from the auction would be limited because the bidder with the highest bid gets the best item, the bidder with second highest bid gets the second best item, and so on surely. Then, for example, the auctioneer cannot infer what happens if the best item was allocated to the other bidder. We consider a stochastic auction which allocates items stochastically to bidders according to their bids in order to get rich data. We introduce a stochastic auction rule that is strategy-proof and individual rational. Moreover, we show that our stochastic auction can be used to get rich data, and yields relatively high revenue compared to the VCG auction in a computer experiment.

Internet service providers are offering shared data plans where multiple users may purchase and share a single pool of data. In the Chinese economy, users have the ability to sell unused data on the Hong Kong Exchange Market, called “2cm”, currently maintained by ATT a fully connected, pure “point of sale” market. A game-theoretical analysis identifies and defines rules for a progressive second price (PSP) auction, which adheres to the underlying market structure. We allow for a single degree of statistical freedom—the reserve price—and show that data exchange markets allow for greater flexibility in acquisition decision-making and mechanism design with an emphasis on optimization of software-defined networks.

In this work, we show how well-known mechanisms for extensively studied single-parameter environments can be implemented with asymptotically optimal communication complexity. Specifically, we first turn our attention to single-parameter domains in auctions, namely single item and multi-unit auctions. For the former case, we show that the Vickrey auction can be implemented with an expected communication complexity of at \(1 + \epsilon \) bits per bidder, for any \(\epsilon > 0\), assuming that the valuations can be represented with a constant number of bits. As a corollary, we provide a compelling method to increment the price in English auctions. By employing an efficient encoding scheme, we show that the same bound can be obtained for multi-item auctions with additive bidders and a constant number of items, and for multi-unit auctions with unit demand bidders. Moreover, we apply our framework to games without monetary transfers and in particular, the canonical case of facility location games. We present an implementation of Moulin’s generalized median mechanism that achieves an \(1 + \epsilon \) approximation of the optimal social welfare, for any \(\epsilon > 0\), while extracting an arbitrarily small fraction of information. Our results follow from simple sampling schemes and do not require any prior knowledge on the agents’ parameters.

The auction designer has one favorite among bidders and maximizes his utility by choosing an auction plan, i.e., choosing the auction format conditional on the favorite’s value. I show that the designer can favor one of the bidders even if favoritism within an auction is not allowed. In this case, instead of intra- auction favoritism, the designer uses inter-auction favoritism. My results imply that favoritism is possible in many situations that could be called fair, e.g., in those that satisfy the EU or WTO rules for public procurement. I also provide a set of restrictions on the auction rules that prevent any form of favoritism.

Dynamic spectrum allocation is an efficient approach to enhance the efficiency of spectral utilization by cognitive users (unlicensed users). Auction mechanism is one approach by which the cognitive users get a portion of the unused licensed band for lease from the primary users (licensed users). The multi-winner auction is a new challenge to the existing auction mechanisms like (Vickrey–Clarke-Groves) VCG and Second price auction. In this work, we have manipulated the pricing based auction mechanism among cognitive users. Specifically, the proposed mechanism enhances the spectral efficiency of the cognitive users and also motivates the primary users to lease the bands to the cognitive users with increased primary user revenue. The proposed mechanism also ensures fairness among cognitive users. Simulation results show that our mechanism increases the spectral efficiency of cognitive users and the revenue of primary users.

In about a decade of its existence, blockchain technology has seen some level of innovation diffusion to many sectors other than its genesis application of cryptocurrencies (see Figure 4-1). This chapter presents an objective view of the developments of blockchain and distributed ledger technologies beyond the cryptocurrencies. By drawing from the current states of the tech, I will present a detailed analysis of existing and potential use cases including ecommerce, Vickrey auction, self-sovereign digital identity management, and vital statistics recording. The chapter further lends key points to consider for applying blockchain and distributed ledger technologies in businesses. Here, starting from justifying why blockchain is a potential solution concept for a business, I will cover points such as asset digitization, participants identification and the degrees of permission, tokenization, decentralized applications, smart contracts and robust consensus algorithm, scalability of data throughput, payment channels, as well as a business model development for token sales.

We study collusion in a second-price auction with two bidders in a dynamic environment. One bidder can make a take-it-or-leave-it collusion proposal, which consists of both an offer and a request of bribes, to the opponent. We show that there always exists a robust equilibrium in which the collusion success probability is one. In the equilibrium, for each type of initiator the expected payoff is generally higher than the counterpart in any robust equilibria of the single-option model ( Eso and Schummer (2004) ) and any other separating equilibria in our model.

Blockchain has transformed business processes from centralized to decentralized. It can better help in making food grain supply chain fully decentralized with peer to peer (P2P) business by removing intermediaries, and thus reducing the overall cost at end user side with better returns to farmers. In this paper, we explore a blockchain technology and the required smart contracts for trustworthy and incentivized P2P trading of food grains. We propose different smart contracts such as food grain supply, bidding, trading and utilization, which are deployed on ethereum blockchain for the decentralized trading of food grains. We use Vickrey-Clarkegrove (Vickrey auction) method for achieving the incentivized trading for both farmers and end users. The proposed framework offers P2P trading, security of food grain data, data transparency, user's anonymity and incentives in the trading process. The performance of the proposed framework is evaluated and analyzed in terms of fulfilling the requirements of food grain supply management.

We propose the first auctioneer-free sealed-bid auction protocol with a linear computation and communication complexity $O(c)$ , $c$ being the bit length of the bid price. Our protocol, called Self-Enforcing Auction Lot (SEAL), operates in a decentralized setting, where bidders jointly compute the maximum bid while preserving the privacy of losing bids. In our protocol, we do not require any secret channels between participants. All operations are publicly verifiable; everyone including third-party observers is able to verify the integrity of the auction outcome. Upon learning the highest bid, the winner comes forward with a proof to prove that she is the real winner. Based on the proof, everyone is able to check if there is only one winner or there is a tie. While our main protocol works with the first-price sealed-bid, it can be easily extended to support the second-price sealed-bid (also known as the Vickrey auction), revealing only the winner and the second highest bid, while keeping the highest bid and all other bids secret. To the best of our knowledge, this work establishes to date the best computation and communication complexity for sealed-bid auction schemes without involving any auctioneer.

In this paper, we investigate the auction-based secondary relay selection on overlay spectrum sharing in hybrid satellite–terrestrial sensor networks (HSTSNs), where both the decode-and-forward (DF) and amplify-and-forward (AF) relay protocols are analyzed based on time division multiple access (TDMA). As both the primary and secondary networks are rational, honest but with incomplete network information, they prefer to obtain maximum possibility payoffs by the cooperation between the primary and secondary networks, and the competition among secondary networks. Hence, Vickery auction is introduced to achieve the effective and efficient secondary relay selection by distinct sub-time slot allocation for one shot in terms of a distributed manner. Finally, numerical simulations are provided to validate the effectiveness of the auction mechanism on cooperative spectrum sharing in HSTSNs for secondary relay selection. Besides, the effect of key factors on the performance of the auction mechanism are analyzed in details.

In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in $T$ rounds by the second price auction, and she can set the reserve price in each round. In each round, the bidders draw their private values from a joint distribution unknown to the seller. Then, the seller announced the reserve price in this round. Next, bidders with private values higher than the announced reserve price in that round will report their values to the seller as their bids. The bidder with the highest bid larger than the reserved price would win the item and she will pay to the seller the price equal to the second-highest bid or the reserve price, whichever is larger. The seller wants to maximize her total revenue during the time horizon $T$ while learning the distribution of private values over time. The problem is more challenging than the standard online learning scenario since the private value distribution is non-stationary, meaning that the distribution of bidders' private values may change over time, and we need to use the \emph{non-stationary regret} to measure the performance of our algorithm. To our knowledge, this paper is the first to study the repeated auction in the non-stationary setting theoretically. Our algorithm achieves the non-stationary regret upper bound $\tilde{\mathcal{O}}(\min\{\sqrt{\mathcal S T}, \bar{\mathcal{V}}^{\frac{1}{3}}T^{\frac{2}{3}}\})$, where $\mathcal S$ is the number of switches in the distribution, and $\bar{\mathcal{V}}$ is the sum of total variation, and $\mathcal S$ and $\bar{\mathcal{V}}$ are not needed to be known by the algorithm. We also prove regret lower bounds $\Omega(\sqrt{\mathcal S T})$ in the switching case and $\Omega(\bar{\mathcal{V}}^{\frac{1}{3}}T^{\frac{2}{3}})$ in the dynamic case, showing that our algorithm has nearly optimal \emph{non-stationary regret}., Comment: Accepted to AAAI-20

We analyze the allocation of an indivisible project in a security-bid auction in which: (i) the allocation of the project to one bidder causes a “negative externality” to his opponents; (ii) the winner has to pay a fixed cost to implement the project; and (iii) the winner’s implementation decision is not contractible. To study the effect of such features on the seller’s expected revenue, we focus on a second-price auction based on one of four securities: (i) cash; (ii) equity; (iii) a fixed-equity hybrid; and (iv) a fixed-cash hybrid. We show that the fixed-equity hybrid generates the highest expected revenue, whereas equity generates the lowest, even though it is the instrument with the highest linkage. Absent negative externalities, equity would generate the highest expected revenue among these four securities. The revenue ranking of the instruments is robust to the information structure and the presence of insurance deposits and entry fees. Fil: Hernandez-Chanto, Allan. The University Of Queensland; Australia Fil: Fioriti, Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina

Auction is the common paradigm for resource allocation which is a fundamental problem in human society. Existing research indicates that the two primary objectives, the seller's revenue and the allocation efficiency, are generally conflicting in auction design. For the first time, we expand the domain of the classic auction to a social graph and formally identify a new class of auction mechanisms on graphs. All mechanisms in this class are incentive-compatible and also promote all buyers to diffuse the auction information to others, whereby both the seller's revenue and the allocation efficiency are significantly improved comparing with the Vickrey auction. It is found that the recently proposed information diffusion mechanism is an extreme case with the lowest revenue in this new class. Our work could potentially inspire a new perspective for the efficient and optimal auction design and could be applied into the prevalent online social and economic networks. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

The revenue of online display advertising in the U.S. is projected to be 7.9 billion U.S. dollars by 2022. One main way of display advertising is through real-time bidding (RTB). In RTB, an ad exchange runs a second price auction among multiple advertisers to sell each ad impression. Publishers usually set up a reserve price, the lowest price acceptable for an ad impression. If there are bids higher than the reserve price, then the revenue is the higher price between the reserve price and the second highest bid; otherwise, the revenue is zero. Thus, a higher reserve price can potentially increase the revenue, but with higher risks associated. In this paper, we study the problem of estimating the failure rate of a reserve price, i.e., the probability that a reserve price fails to be outbid. The solution to this problem have managerial implications to publishers to set appropriate reserve prices in order to minimizes the risks and optimize the expected revenue. This problem is highly challenging since most publishers do not know the historical highest bidding prices offered by RTB advertisers. To address this problem, we develop a parametric survival model for reserve price failure rate prediction. The model is further improved by considering user and page interactions, and header bidding information. The experimental results demonstrate the effectiveness of the proposed approach.

A standard result from auction theory is that bidding truthfully in a second price auction is a weakly dominant strategy. The result, however, does not apply in the presence of Cost Per Action (CPA) constraints. Such constraints exist, for instance, in digital advertising, as some buyer may try to maximize the total number of clicks while keeping the empirical Cost Per Click (CPC) below a threshold. More generally the CPA constraint implies that the buyer has a maximal average cost per unit of value in mind. We discuss how such constraints change some traditional results from auction theory. Following the usual textbook narrative on auction theory, we focus specifically on the symmetric setting, We formalize the notion of CPA constrained auctions and derive a Nash equilibrium for second price auctions. We then extend this result to combinations of first and second price auctions. Further, we expose a revenue equivalence property and show that the seller's revenue-maximizing reserve price is zero. In practice, CPA-constrained buyers may target an empirical CPA on a given time horizon, as the auction is repeated many times. Thus his bidding behavior depends on past realization. We show that the resulting buyer dynamic optimization problem can be formalized with stochastic control tools and solved numerically with available solvers.

Recently, increasingly heterogeneous wireless networks are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing technology paradigms. By achieving an efficient spectrum sharing among heterogeneous networks (HetNets), traffic offloading is a promising solution for boosting the capacity of traditional macro-cell networks. In this paper, a cognitive spectrum sharing and traffic offloading mechanism is proposed to realize the cooperation and competition between the macrocell base station (MBS) and small-cell base stations (SBSs). Under the cooperation mode, the MBS stops occupying a corresponding channel, and a selected SBS helps offload the traffic from the MBS by exclusively using this channel. To facilitate the offloading negotiation between the MBS and SBSs, we design a secondprice auction mechanism, which presents positive allocative externalities, i.e., other uncooperative SBSs can benefit from the cooperation between the MBS and the SBS performing offloading. Meanwhile, the unique optimal biding strategies for different SBSs to achieve the symmetric Bayesian equilibrium are derived and obtained in this paper. The performance of the proposed cognitive traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MBS to achieve the maximum utility.

Multicast transmission has been gaining importance as an efficient means of delivering bandwidth hungry video content and is expected to become an integral part of cellular networks worldwide. This has resulted in the need for generalized allocation algorithms that are capable of handling multiple multicast and unicast services with different Quality of Service (QoS) requirements. In this paper, we propose such a Vickrey-Clarke-Groves (VCG) auction based resource allocation and pricing algorithm. The proposed algorithm takes allocation decisions based on the QoS requirements of the end users for maximizing the system social utility. Even though the users in a multicast group are served on the same PRB, requirements of each individual user are taken into consideration while making the allocation decisions. The proposed algorithm ensures that the users report their true valuations of the system resources. VCG auctions provide a general framework for designing truthful optimal mechanisms. However, in many cases, VCG auction turns out to be NP-hard. In this paper, we propose an efficient, polynomial time implementation of the proposed VCG mechanism. Using simulations, we show that the algorithm successfully meets the unique demands of all unicast and multicast services.

With the rapid development of blockchain technology, the cross-blockchain asset transfer has been in great demand. However, most existing cross-blockchain solutions encounter low efficiency problems due to the centralized features, unfriendly development environment, and difficulty in cooperation. This paper proposes an interaction protocol for secure and efficient cross-blockchain transfer process, wherein the cross-blockchain asset transfer is modeled as an auction process. We design our protocol by leveraging the atomic swap technology and Vickrey auction scheme to achieve efficient cross-blockchain asset transfer, without sacrificing the decentralized control. To achieve the transfer efficiency, we optimize the Vickrey auction scheme to share data within the auction and delivery process synchronously. This results in a efficient user information exchange. The experimental results show that not only can our protocol achieve compatibility, but it also incurs little communication overhead in high throughput. A cross-blockchain transfer process can be accomplished in average 4 rounds of interaction. The difference between the transaction completion time and the bid waiting time is less than 1 second. Besides, our protocol guarantees the exchange rate at a reasonable range. The ratio of the cross-blockchain exchange rate to the real exchange rate converges to 0.9 for approximately 200 participants. The transaction fee decreases sharply with the increase of the number of auction participants.

We consider second-price auctions with participation costs and investigate the revenue effects of a resale possibility. There are two potential bidders whose valuations are independently and identically distributed, and each bidder must incur a participation cost to bid in the auction. Due to these costs, there may be a resale opportunity in an otherwise efficient second-price auction. We show that when values are drawn from a uniform distribution, resale increases (decreases) entry of the lower-(higher-)cost bidder and decreases the original seller’s expected revenue.

The smart grid integrated with renewable energy sources (RESs) allows to perform peer to peer selling of energy via Local Energy Market (LEM). However, it faces the challenges of lack of transparency and verification of energy distribution, single-point failure in the energy data management, user’s privacy, non-incentivized trading and lack of trust on the energy trading. To address these problems, we propose a trustworthy and incentivized framework for smart grid energy trading using distributed ledger technology and smart contracts. We propose different smart contracts viz; energy injection into smart grid, energy bidding to submit energy demand, energy trading and energy utilization. These contracts run on an ethereum blockchain platform with proof of stake (PoS) consensus mechanism to record energy trading related data. We have applied an iterative Vickrey–Clarke–Grove (Vickrey auction) method for the incentivized energy trading in context of both prosumers and consumers. The proposed framework offers trustworthy and incentivized trading of energy, participants’ privacy, data transparency and no single point failure. The experimental results of the proposed framework are derived in terms of average cost of energy injection and bidding transactions, throughput and the incentivized trading by considering different test scenarios.

In this paper, two types of parking spaces, namely public and private parking spaces, are technically allocated to the drivers who may require a long or short time slot. Dynamic allocation and pricing process increase the complexity of evaluating the performance. We design a Physical Internet (PI) enabled parking management system to establish parking networks and collect real-time information for management and evaluation. Then, we propose a truthful, reverse Vickrey auction to allocate and price parking spaces in a static setting. We further incorporate the static auction mechanism into a continuous-time Markov Chain model and analyze the effects of the key factors (e.g., dynamic arrival rate and abandonment rate) in a dynamic setting. Our experimental results show that the expected price of the reverse Vickrey auction is lower than the market price. The expected profit is increasing in the arrival rate of parking spaces in the system. Finally, we numerically examine the benefits of parking space owners, drivers and the system, and highlight the associated managerial implications.

Discrimination (for instance, along the lines of race or gender) is often prohibited in auctions. This is legally enforced by preventing the seller from explicitly biasing the rules in favor of bidders from certain groups (for example, by subsidizing their bids). In this paper, we study the efficacy of this policy in the context of a single object: independent private value setting with heterogeneous bidders. We show that restricting the seller to using an anonymous, sealed bid auction format (or, simply, a symmetric auction) imposes virtually no restriction on her ability to discriminate. Our results highlight that the discrepancy between the superficial impartiality of the auction rules and the resulting fairness of the outcome can be extreme. (JEL D44, D82)

This paper presents a simulation-based analysis of a multiple-round timber combinatorial auction in the timber industry. Currently, most timber auctions are single-unit auctions (i.e., each forest stand is sold separately). However, other types of auctions could be applied to take advantage of the various needs of the bidders with respect to species, volumes, and quality. This study aims to analyze the use of combinatorial auction to this specific context using a simulation approach. Various number of auctions per year, periodicity, lot size, and number of bidders are considered as parameters to set up the different market configurations. The outcomes of both combinatorial auction and single-unit auction are compared with respect to different setup configurations. This analysis shows that combinatorial auction can bring more profit for both seller and buyer when the market is less competitive.

This note analyzes different types of all-pay auctions when the number of bidders becomes large. We compute the distributions of the highest bids for the first-price all pay auction and we show that the expected payment made by the winning bidder converges to half of the total payments. In the second-price all-pay auction (the war of attrition), the highest bid amounts to about 35.5% of the total revenues. We also compute the payments for all-pay auctions with multiple prizes.

We experimentally study the effect of auction format (sealed-bid versus closed clock versus open clock) and auction sequence (simultaneous versus sequential) on bidding behaviour and auction outcomes in auctions of multiple related multi-unit items. Prominent field applications are the sale of emission permits, fishing rights, and electricity. We find that, when auctioning simultaneously, clock auctions outperform sealed-bid auctions in terms of efficiency and revenues. This advantage disappears when the items are auctioned sequentially. In addition, auctioning sequentially has positive effects on total revenues across all auction formats, resulting from fiercer competition on the item auctioned first.

The theoretical literature suggests that the seller of a unique object maximizes her expected revenue by running a standard auction with an optimally chosen reserve price. However, in reality, there are instances in which sellers decide to accept an offer before the auction starts and cancel the auction. This paper first theoretically investigates the rationale behind this type of behavior and then empirically analyses evidence from real-world auctions. In particular, we suggest that, if some individuals are risk averse—in contrast to the standard auction theory models, in which all parties are risk neutral—then it is possible that accepting a price before the auction maximizes the expected revenue for the seller. Finally, with data on housing market auctions, we show evidence of such behavior and further investigate the implications of the theory.

This article examined the empirical relationship between prices paid for national 3G wireless licenses when spectrums were sold by auction. The reduced-form modelling approach was based on the prem...

Crowd sensing (CS) is a new paradigm of collecting large amounts of sensing information from a crowd. Unfortunately, not everyone is willing to participate in sensing tasks or provide high-quality information. Offering incentives is a common method of increasing the success of CS. Existing incentive mechanisms based on auction models lack deep consideration regarding the effects of malicious competition behavior and the “free-riding” phenomenon in crowdsourcing services. The design proposed in this paper focuses on an incentive mechanism based on a reverse auction. First, the crowdsourcing system incentive model is built. Second, an incentive mechanism is proposed based on an auction which combines the concepts of reverse auctions and Vickrey auctions. Next, proof that the mechanism is computationally efficient, individually rational, budget-balanced, truthful, and honest is provided. Simulation results indicate that the proposed incentive mechanism can effectively improve fairness of the bids and the quality of the sensed data.

In an experiment, we first elicit the distributional preferences of subjects and then let them bid for a lottery, either in a Becker–DeGroot–Marschak (BDM) mechanism or a Vickrey auction (VA). The standard theory predicts that altruistic subjects underbid in the VA—compared to the BDM—while spiteful subjects overbid in the VA. The data do not confirm those predictions. While we observe aggregate underbidding in the VA, the result is not driven by the choices of altruistic subjects.

In financial disputes arising from divorce, inheritance, or the dissolution of a partnership, frequently the need arises to assign ownership of an indivisible item to one member of a group. This paper introduces and analyzes a dynamic auction for simply and efficiently allocating an item when participants are privately informed of their values. In the auction, the price rises continuously. A bidder who drops out of the auction, in return for surrendering his claim to the item, obtains compensation equal to the difference between the price at which he drops and the preceding drop price. When only one bidder remains, that bidder wins the item and pays the compensations of his rivals. We characterize the unique equilibrium with risk-neutral and CARA risk averse bidders. We show that dropout prices are decreasing as bidders become more risk averse. Each bidder's equilibrium payoff is at least 1 / N -th of his value for the item.

PurposeThis research aims to contrast bid competitiveness with respect to the average bid auction (ABA) and the non-ABA bidding formats used by the Public Works Department (PWD) of Malaysia.Design/methodology/approachThe research uses the ordinary least square regression and the Monte–Carlo simulation to point out significant predictors which affect the bid ratio and fitting probability distributions to bidding data, respectively.FindingsThis research shows that the bidding strategy adopted is dependent on the different formats used. In the ABA format, bidders are more likely to submit identical bid prices. In the non-ABA format, they bid according to the first-price auction strategy, which suggests greater variation between bid prices as a winning strategy and the reduction in the bid price to an estimated price ratio when more bidders bid.Practical implicationsBidders lose more money when the distance between the project location and a firm’s operational office is greater. Best-fit probability density functions follow a gamma distribution for the ABA format and a Weibull distribution for the non-ABA format. The location and number of bidders affect bidders’ strategy to win.Originality/valueThis research presents empirical insights concerning the comparisons of different type of bidding formats practiced by PWD of Malaysia and its implications on the construction companies’ bidding behaviors especially when it comes to its economic consequences. The significant factors that affect the different auction mechanisms used can serve as a basis for improving the present methods employed by PWD and in other countries.

In online auction research, sellers’ and buyers’ strategies have been largely examined as separate research streams and their interdependencies have not been adequately explored. This deficiency is a serious limitation since an integrative approach could build the conceptual bridge necessary to provide sellers and buyers with a deeper understanding of how to achieve desired auction outcomes. Consequently, the present study integrates these two perspectives by proposing that the seller’s auction duration strategy impacts the effectiveness of bidders’ strategies in the forms of both winning likelihood and surplus extraction. We drew upon the concept of bidders’ time costs and information asymmetry to build our hypotheses. Field data collected from eBay supported our model and demonstrated significant differences in bidders’ winning likelihood and surpluses across auctions with shorter and longer durations. The results suggested that online bidders should pay closer attention to auction duration sinc...

I examine bid behavior in uniform-price auctions and multi-unit Vickrey auctions, without the standard quasilinearity restriction on bidder preferences. Instead of assuming quasilinearity, I assume that bidders have weakly positive wealth effects, i.e. the goods are normal goods. My setting nests quasilinearity, but also allows for budget constraints, financial constraints, and risk aversion. I show that without the quasilinearity restriction, truthful reporting is not a dominant strategy in the Vickrey auction. Instead, bidders truthfully demand for their first unit and weakly overreport their demand for later units. The incentive to overreport demand means that the Vickrey auction is generally inefficient. This mirrors the well-known demand reduction results in uniform-price auctions. Moreover, the efficiency ranking of the two auctions is ambiguous. In fact, there are cases where the uniform-price auction is Pareto efficient and the Vickrey auction is Pareto inefficient, even if only one bidder has non-quasilinear preferences.

We study auctions with resale and reserve prices. We characterize the equilibrium, and compare the revenues of the first price auction with that of the second price auction. We show that several results change when a reserve price is introduced. First, the second-price auction may yield higher revenues than the first-price auction. Second, the strong bidder is more likely to win than the weak bidder.

The combinatorial clock auction has become popular for large-scale spectrum awards and other uses, replacing more traditional ascending or clock auctions. We describe some surprising properties of the auction, including a wide range of ex post equilibria with demand expansion, demand reduction, and predation. Our results obtain in a standard homogeneous good setting where bidders have well-behaved linear demand curves, and suggest some practical difficulties with dynamic implementations of the Vickrey auction. (JEL D44, D47, H82, L13)

We study a type of complex system arising from economics, the lowest unique bid auction (LUBA) system which is a new generation of online markets. Different from the traditional auction in which the winner is who bids the highest price, in LUBA, the winner is whoever places the lowest of all unique bids. In this paper, we propose a multi-agent model to factually describes the human psychologies of the decision-making process in LUBA. The model produces bid-price distributions that are in excellent agreement with those from the real data, including the whole inverted-J shape which is a general feature of the real bid price distribution, and the exponential decreasing shape in the higher price range. This implies that it is possible for us to capture the essential features of human psychologies in the competitive environment as exemplified by LUBA and that we may provide significant quantitative insights into complex socio-economic phenomena.

Due to the popularity of auction mechanisms in real-world applications and the increasing awareness of securing private information, auctions are in dire need of bid-privacy protection. In this paper, we design three secure, multi-unit, sealed-bid, first-price auction schemes. The first is a secure auction using homomorphic encryption and is denoted by SAHE; the second is a secure action using masking values and is denoted by SAMV; and the third has an improved masked noise algorithm, denoted by ISAMV. In the first, SAHE, the auction is processed on encrypted bids by a server, and the final output is only known by the auctioneer. Neither the auctioneer nor the server can obtain the full information of the bidders. The second and third auctions, SAMV and ISAMV, decrease computational complexity. Instead of homomorphic encryption, they use random noise to mask the bid values. By using a masking method, the server only knows the noise, and the auctioneer only knows the auction results; neither will see the private information of the bidders. All three schemes enable the auctioneer to verify that the winners have paid the correct amounts. A thorough theoretical analysis is performed to evaluate the security properties, computational complexity, and communication complexity of the auctions. Copyright © 2016 John Wiley & Sons, Ltd.

Ausubel’s dynamic private-values auction for heterogeneous discrete goods, Ausubel (2006), yields an efficient equilibrium outcome but it is designed for a limited class of environments. If bidders’ values for bundles of goods are not integers, then Ausubel’s auction may end without allocating goods if no information on bidders’ values is available. In this paper, I extend Ausubel’s auction for heterogeneous discrete goods to real-valued quasilinear utility functions. The mechanism I propose reaches a Walrasian equilibrium price vector in finite “steps” without any additional information on bidders’ values. In the extension of Ausubel’s auction, truthful bidding constitutes an efficient equilibrium. JEL Category D44 ∗I would like to thank Ket Richter and Jan Werner for their continuous support, patience, and effort for my education. Their guidance has been enlightening. I would like to thank late Leo Hurwicz, and Andy McLennan for their contribution to my education. I benefited a lot from discussions with David Rahman and Itai Sher. I also would like to thank Pat Bajari, Tuba Inal, Ichiro Obara, Raj Singh, and the seminar participants at Central Michigan University, METU, the University of Minnesota, the Virginia Commonwealth University, Whitman College, the Games 2008, and the Society for Economic Design 2008 Conference for their valuable comments. All errors are mine. †http://hakaninal.weebly.com

In this paper, we explore the question of efficient allocation of energy, while buying the same from generation companies, to plug-in electric vehicles (PEVs) by aggregator (electricity utility or load serving entities) through auction mechanisms. Recognizing the practical limitations of the Vickrey–Clarke–Groves mechanism, which would be natural to apply in this context, we investigate two practical mechanisms that can be viewed as extensions of second price auction mechanisms and have limited message (bid) complexity. In the first mechanism, the elastic-supply multi-level second price, each PEV agent submits a number of price bids, one for each of a given set of energy levels (energy quantities). In the second mechanism, the elastic-supply progressive second price, the PEV agents submit a 2-D bid indicating the price as well as the desired energy quantity. Taking into account differences across PEV-owners in terms of their willingness-to-pay values and charging time constraints, we analyze the social optimality and incentive compatibility properties of the two auction mechanisms. We also complement our theoretical findings with numerical simulations.

This paper shows that for absolutely continuous valuation distributions the increasing virtual valuations is equivalent to the increasing odds rate. Based on this new characterization we develop two nonparametric tests for the strictly increasing virtual valuations by using the generalized total time on test transform. The empirical type I error rate and power performance of the two tests are examined through Monte Carlo simulations. As illustrations the two tests are also applied to two real data sets collected from eBay.