Designing Electronic Auctions: An Internet-Based Hybrid Procedure Combining Aspects of Negotiations and Auctions

We discuss design issues pertaining to multiple issue auctions in the WWW environment. Based on a critical evaluation of existing auctions, we propose NegotiAuction^tm, an algorithmic Internet-based auction procedure, which combines certain elements of negotiations and auctions. It can be used either in reverse or forward auctions. When defining a multiple issue (multiple attribute) auction, the auction owner has more control over the bidding process than is possible in traditional auctions, signaling bid requirements to the bidders individually. This will result in a preferred set of auction winners. It is believed to reduce the total transaction time and eliminate the necessity of holding subsequent negotiations with the set of winners.     

A secure multi-item e-auction mechanism with bid privacy

The recent focus within the auction field has been multi-item auctions where bidders are not restricted to buying only one item of the merchandise. It has been of practical importance in Internet auction sites and has been widely executed by them. In this paper, we concentrate on the use of the multi-item auction for task assignment scenarios and propose a novel PUPA auction protocol to solve the problem of bid privacy in multi-item auctions. A verifiable technique of shared key chain is proposed to find the winners without revealing the losing bid and bidder’s privacy. It can be shown that our new scheme is robust against cheating bidders.     

Iterative Dutch combinatorial auctions

The combinatorial auction problem can be modeled as a weighted set packing problem. Similarly the reverse combinatorial auction can be modeled as a weighted set covering problem. We use the set packing and set covering formulations to suggest novel iterative Dutch auction algorithms for combinatorial auction problems. We use generalized Vickrey auctions (GVA) with reserve prices in each iteration. We prove the convergence of the algorithms and show that the solutions obtained using the algorithms lie within provable worst case bounds. We conduct numerical experiments to show that in general the solutions obtained using these algorithms are much better than the theoretical bounds.     

Multiagent System Simulations of Treasury Auctions

This study uses a multiagent system to determine which payment rule provides the most revenue in treasury auctions. The agents learn how to bid using straightforward bid adjustment rules that are based on impulse balance learning. The market model encompasses the when-issued, auction, and secondary markets, as well as bidding constraints for primary dealers. I find that when the number of primary bidders is less than 13 (Canada) the Discriminatory payment rule is revenue superior to the Uniform payment across most market price spreads. When the number of primary bidders is greater than 14 (United States), Uniform payment is revenue superior to Discriminatory payment for all market price spreads. In general, revenue increases with the minimum bid constraint and with the number of primary dealers for Uniform, Average, and Vickrey payment rules.      

群智感知中基于反拍卖模型的众包激励方法

激励是实现群智感知（CS）众包服务的主要方法,针对现有方法在服务过程中没有充分考虑节点参与数量和恶意竞争对群智感知带来的影响,提出一种基于反拍卖模型的激励（RVA-IM）方法。首先,研究众包的激励机制,结合反拍卖与Vickrey拍卖思想,构建面向任务覆盖的反拍卖模型;其次,对模型中涉及的任务覆盖、反拍卖选择和奖励实施等关键技术问题进行深入分析与研究;最后,从计算有效、个人理性、预算平衡、真实性和诚实性五个方面分析RVA-IM激励方法的有效性。实验结果表明,与IMC-SS和MSensing激励方法相比,RVA-IM在有效性和可行性方面均有较好的表现,能够解决现有方法中的恶意竞争问题,并能够平均提升众包服务完成率约21%。     

Maximizing sellers’ welfare in online auction by simulating bidders’ proxy bidding agents

This paper presents an agent-based simulation model to estimate the effects of auction parameters on the auction outcomes, and to find an optimal or, at least, a near-optimal infrastructure. In particular, this study intends to study how bidders’ personalities and bidding strategies with other auction parameters affect the closing prices in two auction mechanisms: English and Yankee auction. Experimental results show that the aggregated outcome of multiple English auctions is more favorable to auctioneers and sellers than that of a corresponding single Yankee auction. It is also shown that raising minimum bid increment or bid starting price positively affects the closing prices in both auction types. However, two auction systems respond differently to the changes in parameter values in terms of magnitude and robustness. Further, it is noted that the closing price of an auction is negatively related to the proportion of risk-takers who adopt sniping strategy to submit their bids. As the proportion of risk-takers increases, the closing price significantly decreases in hard-ending auctions (eBay-like system), but only marginally in soft-ending auctions (Amazon-like system). In particular, soft-ending auctions with less snipers and a longer time window of automatic extension return the most favorable closing price to sellers.     

Visualizing combinatorial auctions

We propose a novel scheme to visualize combinatorial auctions; auctions that involve the simultaneous sale of multiple items. Buyers bid on complementary sets of items, or bundles, where the utility of securing all the items in the bundle is more than the sum of the utility of the individual items. Our visualizations use concentric rings divided into arcs to visualize the bundles in an auction. The arcs’ positions and overlaps allow viewers to identify and follow bidding strategies. Properties of color, texture, and motion are used to represent different attributes of the auction, including active bundles, prices bid for each bundle, winning bids, and bidders’ interests. Keyframe animations are used to show changes in an auction over time. We demonstrate our visualization technique on a standard testbed dataset generated by researchers to evaluate combinatorial auction bid strategies, and on recent Federal Communications Commission (FCC) auctions designed to allocate wireless spectrum licenses to cell phone service providers.     

Preference-based English reverse auctions

This paper studies English reverse auctions within a unified framework for preference-based English reverse auctions. In this context, and particularly for electronic auctions, representing and handling the buyer?s preferences, so as to enable him/her to obtain the best possible outcome, is a major issue. Existing auction mechanisms, which are based on single or multi-attribute utility functions, are only able to represent transitive and complete preferences. It is well known, however, in the preference modeling literature that more general preference structures, allowing intransitivity and incomparability, are more appropriate to capture preferences. On the other hand, we must also consider properties on the evolution and, above all, on the outcome of any auction executed by an auction mechanism. These properties, as well as properties of non-dominance and fair competition defined for multiple criteria auctions, impose restrictions on the preference relation. This leaves room for interesting preference models to be implemented within English reverse auction mechanisms.     

Assessing the benefits of group-buying-based combinatorial reverse auctions

Combinatorial reverse auctions represent a popular business model in procurement. For multiple buyers, different procurement models based on combinatorial reverse auctions may be applied. For example, each buyer may hold one combinatorial reverse auction independently. Alternatively, the buyers may delegate the auction to a group-buyer and let the group-buyer hold only one combinatorial reverse auction on behalf of all the buyers. A combination of a combinatorial reverse auctions with the group-buying model makes it possible to reduce the overall cost to acquire the required items significantly due to complementarities between items. However, combinatorial reverse auctions suffer from high computational complexity. To assess the advantage of combining group-buying with combinatorial reverse auctions, three issues must be addressed, including performance, computational efficiency and the scheme to reward the buyers. This motivates us to compare the performance and efficiency of the aforementioned two different combinatorial reverse auction models and to study the possible schemes to reward the buyers. To achieve these objectives, we first illustrate the advantage of group-buying-based combinatorial reverse auctions over multiple independent combinatorial reverse auctions. We then formulate the problems for these two combinatorial reverse auction models and propose solution algorithms for them. We compare performance and computational efficiency for these two combinatorial reverse auction models. Our analysis indicates that a group-buying-based combinatorial reverse auction not only outperforms multiple independent combinatorial reverse auctions but also is more efficient than multiple independent combinatorial reverse auctions. We also propose a non-uniform scheme to reward the buyers in group-buying based combinatorial reverse auctions.     

Competitive analysis of incentive compatible on-line auctions

This paper studies auctions in a setting where the different bidders arrive at different times and the auction mechanism is required to make decisions about each bid as it is received. Such settings occur in computerized auctions of computational resources as well as in other settings. We call such auctions, on-line auctions.

We first characterize exactly on-line auctions that are incentive compatible, i.e. where rational bidders are always motivated to bid their true valuation. We then embark on a competitive worst-case analysis of incentive compatible on-line auctions. We obtain several results, the cleanest of which is an incentive compatible on-line auction for a large number of identical items. This auction has an optimal competitive ratio, both in terms of seller's revenue and in terms of the total social efficiency obtained.     

What Format for Multi-Unit Multiple-Bid Auctions?

This paper uses computational experiments where bidders learn over nonlinear bidding strategies to compare outcomes for alternative pricing format for multi-unit multiple-bid auctions. Multi-unit multiple-bid auctions, in which bidders are allowed to submit multiple price-quantity bids, are promising mechanisms for the allocation of a range of resources. The main advantage of such auctions is to avoid the lumpy bid problem which arises when bidders can only compete on the basis of one bid. However, there is great uncertainty about the best auction formats when multi-unit auctions are used. The theory can only supply the expected structural properties of equilibrium strategies and the multiplicity of potential equilibria makes comparisons across auction formats difficult. Empirical studies and experiments have improved our knowledge of multi-unit auctions but they remain scarce and most experiments are restricted to two bidders and two units. Moreover, they demonstrate that bidders have limited rationality and learn through experience. This paper constructs an agent-based computational model of bidders to compare the performance of alternative procurement auction formats under circumstances where bidders submit continuous bid supply functions and learn over time to adjust their bids in order to improve their net incomes. The setting is for independent private values. We show that bidding behaviour displays more interesting patterns than is depicted in the theoretical literature and that bidding patterns depend on the interplay between heterogeneity in the bidder population and the degree of rationing in the auction. Results indicate that the three auction formats have similar performance for most levels of competition but that their performances differ when competition is weak. This ranking is dependent on whether the population of bidders is homogenous or heterogeneous.     

Protocol completion incentive problems in cryptographic Vickrey auctions

Despite attractive theoretical properties, Vickrey auctions are seldom used due to the risk of information revelation and fear of cheating. CVAs (Cryptographic Vickrey Auctions) have been proposed to protect bidders’ privacy or to prevent cheating by the bid taker. This paper focuses on incentive issues for certain CVAs. First, it defines the CVAs of interest and identifies ideal goals for this class of CVAs. One of the criteria identifies an incentive problem that is new to the literature on CVAs, the disincentive of bidders to complete the protocol once they have learned that they lost the auction. Any auction protocol that requires losing bidders to do additional work after learning they have lost the auction must provide the losers with proper incentives to follow the protocol. Second, this paper shows that for a class of CVAs, some losers must continue to participate even though they know they have lost. Finally, it describes two new CVA protocols that solve the protocol-completion incentive problem. Both protocols use bidder-bidder comparisons based on a modified Yao’s Millionaires’ protocol. The first protocol performs O(n ²) bidder-bidder comparisons, while the second protocol performs O(n) comparisons.     

Bidding strategies for real-life small loan auctions

We define and identify bidding strategies in real-life small loan auctions (Prosper.com). In such auctions, lenders bid for borrowers' loan listings and the winners get to fund the loan at an interest rate determined by the auction. The exceptionally large empirical database provided by Prosper.com offers a unique opportunity to test and further develop the theory of online auctions. This study shows that bidding behavior is not homogeneous among bidders, as the traditional auction theory suggests. Instead, bidders use many different bidding strategies. Moreover, learning and bidders' consistency over time in different auctions is studied.     

Real time prediction of closing price and duration of B2B reverse auctions

Nowadays, online auctions have become the most successful business model in the electronic marketplace. To the best of the authors’ knowledge, no other work has been devoted to the prediction of closing price and duration of Business-to-Business (B2B) English reverse online auctions in which goods or service providers compete with each other to win contracts by lowering offering prices with each bid, which is conducted on a virtual platform hosted on the Internet. This research designs and proposes a new methodology to predict closing prices and duration within the first few bids of the corresponding auctions based on real time bidding information rather than static auction information. In this article, we employ real time information and prediction rules to forecast the behavior of live auctions. This is in contrast to the static prediction approach that takes into consideration only information available at the beginning of an auction such as products, item features, or the seller’s reputation. This simulation is based on discretized auction data derived from a B2B online auction marketplace over a two-year period. Three measurements including accuracy, coverage, and benefit are used to evaluate the methodology. Results show that after observing the first 4 bids, this methodology can predict closing prices and duration with 84.6 and 71.9% accuracy, respectively.     

Towards Fast Vickrey Pricing using Constraint Programming

Ensuring truthfulness amongst self-interested agents bidding against one another in an auction can be computationally expensive when prices are determined using the Vickrey–Clarke–Groves (VCG) mechanism. This mechanism guarantees that each agent's dominant strategy is to tell the truth, but it requires solving n+ 1 optimization problems when the overall optimal solution involves n agents. This paper first examines a case-study example demonstrating how Operations Research techniques can be used to compute Vickrey prices efficiently. In particular, the case-study focuses on the Assignment Problem. We show how, in this case, Vickrey prices can be computed in the same asymptotic time complexity as that of the original optimization problem. This case-study can be seen as serving a pedagogical role in the paper illustrating how Operations Research techniques can be used for fast Vickrey pricing. We then propose a Constraint Programming approach that can be used in a more general context, where nothing is assumed about the nature of the constraints that must be satisfied or the structure of the underlying problem. In particular, we demonstrate how nogood learning can be used to improve the efficiency of constraint-based Vickrey pricing in combinatorial auctions.     

Bid evaluation behavior in online procurement auctions involving technical and business experts

Multi-attribute reverse auction-based procurement has been widely used by large organizations. The attributes of the auctioned objects are usually divided into two groups: technical and business attributes. They are reviewed and scored by technical and business experts who act as referees in the bid evaluation process. To analyze their bid evaluation behavior, we built a model for a multi-attribute reverse auction. With correlations between the bid evaluations of the different referee groups across the attributes, the bid evaluation problem is not the usual multi-attribute decision-making problem. We assess the cause–effect relationship that is present, and show that antagonism between referee groups tends to grow over time. We tested how this works with data from simulated auctions. To diminish the potential for antagonism between the two referee groups, we propose a modified bid evaluation mechanism. We also conducted role-playing experiments involving the referee behaviors as a means for assessing the proposed mechanism. Our results suggest that the modified bid evaluation mechanism is beneficial.     

Robust combinatorial auction protocol against false-name bids

This paper presents a new combinatorial auction protocol that is robust against false-name bids. Internet auctions have become an integral part of Electronic Commerce (EC) and a promising field for applying agent and Artificial Intelligence technologies. Although the Internet provides an excellent infrastructure for combinatorial auctions, we must consider the possibility of a new type of cheating, i.e., an agent tries to profit from submitting several bids under fictitious names (false-name bids). If there exists no false-name bid, the Generalized Vickrey Auction protocol (GVA) satisfies individual rationality, Pareto efficiency, and incentive compatibility. On the other hand, when false-name bids are possible, it is theoretically impossible for a combinatorial auction protocol to simultaneously satisfy these three properties.

Our newly developed Leveled Division Set (LDS) protocol, which is a modification of the GVA, utilizes reservation prices of auctioned goods for making decisions on whether to sell goods in a bundle or separately. The LDS protocol satisfies individual rationality and incentive compatibility even if agents can submit false-name bids, although it is not guaranteed to achieve a Pareto efficient social surplus. Simulation results show that the LDS protocol can achieve a better social surplus than that for a protocol that always sells goods in one bundle.     

Design of online auctions: Proxy versus non-proxy settings

Recent years have witnessed the rapid development of online auctions. Currently, some online auctions, such as eBay, introduce a proxy bidding policy, under which bidders submit their maximum bids and delegate to a proxy agent to automatically outbid other competitors for the top bidder, whereas other online auctions do not. This paper compares these two widely used auction mechanisms (proxy setting and non-proxy setting) and characterizes the equilibrium bidding behavior and the seller's expected revenue. We find the proxy auction outperforms the non-proxy auction in terms of the seller's expected revenue. This dominance result is not prone to the specific bid announcement policy, the bidder's knowledge regarding the number of bidders, the impact of traffic congestion along the bidding process, the number of items sold through the auction, and the existence of a reserve price.We further find that the proxy setting usually fails to sustain the truthful bidding as a dominant strategy equilibrium even if no minimum bid increments are adopted, and the possibility of a low-valuation-bidder dilemma where the low-valuation bidders could be better off if all bidders collude to bid at the last minute. We also discuss the dramatically different equilibrium bidding behaviors under the two auction mechanisms.     

Experiments with probabilistic quantum auctions

We describe human-subject laboratory experiments on probabilistic auctions based on previously proposed auction protocols involving the simulated manipulation and communication of quantum states. These auctions are probabilistic in determining which bidder wins, or having no winner, rather than always having the highest bidder win. Comparing two quantum protocols in the context of first-price sealed bid auctions, we find the one predicted to be superior by game theory also performs better experimentally. We also compare with a conventional first-price auction, which gives higher performance. Thus to provide benefits, the quantum protocol requires more complex economic scenarios such as maintaining privacy of bids over a series of related auctions or involving allocative externalities.