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.     

P2P网络中的代理拍卖协议

提出一个点对点（P2P）网络中的二价代理拍卖协议。投标者在投放出价链之后即可自由进出协议,由P2P节点代理完成拍卖;在整个拍卖过程中,低的出价链会在未被完全解密的状态下先被淘汰,最后只留下最高出价链。与已有的其他协议相比,它兼具密闭拍卖和分布式拍卖的优点,不仅能做到异步出价,而且能保护所有出价者的出价隐私。     

一种淘汰式无拍卖行的电子拍卖

在所有的封闭式电子拍卖中存在的一个共同的缺陷结点(tie)问题，即几个投标者同时投了最高价，由于存在着结点严重地影响了拍卖的效率。基于shamir的(t，n)门限体制提出了一种淘汰式无拍卖行的匿名电子拍卖，采用无拍卖行且每个投标者的标价在投标者之间分享实现标价的匿名性的同时，提出了一种淘汰式拍卖解决结点问题，从而使拍卖的效率得到了提高。     

基于区块链的密封式投标拍卖方案

随着互联网技术的飞速发展,传统拍卖正逐渐转变为电子拍卖,其中隐私保护越来越受到关注。针对当前电子投标拍卖系统中出现的问题,如竞买人隐私存在被泄露的风险、第三方拍卖中心的费用昂贵、第三方拍卖中心可能与竞买人勾结等,提出一种基于区块链智能合约技术的密封式投标拍卖方案。该方案充分利用区块链的去中心化、防篡改和可信赖性等特征构建了一个无第三方的拍卖环境,并通过区块链上的安全保证金策略约束竞买人的行为,从而提高密封式电子拍卖的安全性。同时该方案利用Pedersen承诺保护竞买人的竞拍价格不被泄露,并通过Bulletproofs零知识证明协议验证中标价格的正确性。安全性分析和实验结果表明,提出的拍卖方案满足安全性要求,各个阶段的时间消耗均在可接受范围内,满足日常拍卖要求。     

Strong activity rules for iterative combinatorial auctions

Activity rules have emerged in recent years as an important aspect of practical auction design. The role of an activity rule in an iterative auction is to suppress strategic behavior by bidders and promote simple, continual, meaningful bidding and thus, price discovery. These rules find application in the design of iterative combinatorial auctions for real world scenarios, for example in spectrum auctions, in airline landing slot auctions, and in procurement auctions. We introduce the notion of strong activity rules, which allow simple, consistent bidding strategies while precluding all behaviors that cannot be rationalized in this way. We design such a rule for auctions with budget-constrained bidders, i.e., bidders with valuations for resources that are greater than their ability to pay. Such bidders are of practical importance in many market environments, and hindered from bidding in a simple and consistent way by the commonly used revealed-preference activity rule, which is too strong in such an environment. We consider issues of complexity, and provide two useful forms of information feedback to guide bidders in meeting strong activity rules. As a special case, we derive a strong activity rule for non-budget-constrained bidders. The ultimate choice of activity rule must depend, in part, on beliefs about the types of bidders likely to participate in an auction event because one cannot have a rule that is simultaneously strong for both budget-constrained bidders and quasi-linear bidders.     

Cheating in online auction – Towards explaining the popularity of English auction

Action frauds constitute largest part of all Internet frauds. Cheating is a kind of fraud that does not have direct evidences of its occurrence. We conduct theoretical studies as well as simulation experiments to find out the effect of cheating in three important types of auctions: English auction, first-price sealed-bid, and second-price sealed-bid auction. Our cheating environment consists of shill bidding, bid shading and false bidding in English, first-price and second-price auction, respectively. In the experiments ordinary bidders, bidders with the equilibrium bidding strategy, and cheaters compete with each other. Both theoretical and experimental results confirm that the equilibrium bidding strategies indeed increases the bidders’ expected utility. Therefore, it can be concluded that adoption of rational bidding strategies can combat cheating. It is found that most of the auction sites intuitively prefer English auction to other auction mechanisms. There is not much theoretical or experimental evidence to support such an intuition. We use honest bidder’s expected gain and honest seller’s revenue loss as a basis to compare these three important auctions types. The analysis of the results reveals English auction to be the most preferred mechanism from both honest buyer’s and honest seller’s point of view. This result can be used as an experimental evidence to explain the popularity of English auction over the Internet.     

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.     

Auction design with costly preference elicitation

We consider auction design in a setting with costly preference elicitation. Well designed auctions can help to avoid unnecessary elicitation while determining efficient allocations. Careful design can also lead to more efficient outcomes when elicitation is too costly to permit perfect allocative efficiency. An incremental revelation principle is developed and used to motivate the role of proxied and indirect auction designs. Proxy agents, situated between bidders and an auction, can be used to maintain partial information about bidder preferences, to compute equilibrium bidding strategies based on the available information, and to elicit additional preference information as required. We derive information-theoretic elicitation policies for proxy agents under a simple model of costly elicitation across different auction designs. An experimental analysis demonstrates that indirect mechanisms, such as ascending-price auctions, can achieve better allocative efficiency with less preference elicitation than sealed-bid (direct) auctions because they promote better decisions about preference elicitation.A preliminary version of this paper appeared in the Proc. of the IJCAI’99 Workshop on Agent Mediated Electronic Commerce.     

Implications of a Reserve Price in an Agent-Based Common-Value Auction

Auction sellers can use a reserve price to require a minimum bid before items are sold. Theoretical and experimental research has tested the influence of a reserve price in an independent private values auction, but little focus has been given to the influence of a reserve price in a first-price common-value auction. We establish an agent-based first-price common-value auction to determine the impact of the reserve price with two buyers and with three buyers. An agent-based approach to this problem is both a unique contribution to the literature and appropriate since finding analytical solutions with common-value auctions is difficult. The agent-based model approach also allows us to consider buyers that have non-symmetric bid functions. Furthermore, we introduce a combination of numerical integration techniques with a new particle swarm learning algorithm. The buyers in the model choose their expected-net-revenue-maximizing bid price, and sellers choose their expected-revenue-maximizing reserve price. In the two-buyer and three-buyer auction, a reserve price increases the equilibrium winning bid price, decreases the probability that the item is sold, and increases the seller’s expected revenue. A reserve price in a two-buyer auction increases the winning bid price more than including an additional buyer in the auction with no reserve price. However, due to only receiving a salvage value when the item does not sell in the auction, the seller’s expected revenue is higher in the three-buyer first-price common-value auction without a reserve price than in the two-buyer auction with a reserve price.     

Improving the Performance of Online Auctions Through Server-side Activity-based Caching

Online auction sites have very specific workloads and user behavior characteristics. Previous studies on workload characterization conducted by the authors showed that (1) bidding activity on auctions increases considerably after 90% of an auction’s life time has elapsed, (2) a very large percentage of auctions have a relatively low number of bids and bidders and a very small percentage of auctions have a high number of bids and bidders, (3) prices rise very fast after an auction has lasted more than 90% of its life time. Thus, if bidders are not able to successfully bid at the very last moments of an auction because of site overload, the final price may not be as high as it could be and sellers, and consequently the auction site, may lose revenue. In this paper, we propose server-side caching strategies in which cache placement and replacement policies are based on auction-related parameters such as number of bids placed or percent remaining time till closing time. A main-memory auction cache at the application server can be used to reduce accesses to the back-end database server. Trace-based simulations were used to evaluate these caching strategies in terms of cache hit ratio and cache efficiency. The performance characteristics of the best policies were then evaluated through experiments conducted on a benchmark online auction system.     

Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions

Cooperation among bidders in traditional auctions is typically forbidden. This is because it is viewed as being harmful to the interests of sellers, who hope to obtain fair prices for their sale items. It also may be harmful to other bidders who are not able to take advantage of any cooperation that is occurring. In online group-buying auctions, in contrast to traditional auctions, cooperation results in higher welfare, leading to market expansion that benefits buyers and sellers, as well as the auction intermediary. This has not been well understood in prior research, however. In this article, we show how the online group-buying auction mechanism on the Internet can be effectively enhanced to produce higher welfare for the participants. The key to achieving this, we find, is for the auction intermediary to provide a means for bidders to cooperate, so as to collectively express greater demand. Such cooperation, it turns out, permits the group-buying auction mechanism to dominate the fixed-price mechanism from the seller’s point of view under some circumstances. Through an analytical modeling analysis, we offer insights into how sellers can set their group-buying auction price curves more effectively, so as to take advantage of bidder cooperation to improve auction performance. We further argue that the goal of the auction intermediary should be to offer an information sharing mechanism to facilitate bidding ring formation, as a means to maximize the value of this market mechanism.     

Contrast effects in online auctions

Prior research has shown online auction features can serve as information cues and affect consumers’ willingness-to-pay. We argue that auctions are not only affected by their information cues but also by contrasting, peripheral information cues from adjacent auction listings. Applying contrast effects theory, we examined the moderating effects of time urgency and persuasion intent on the processing of contrasting peripheral information from adjacent auctions. Using two controlled experiments and an empirical field study, we showed that time urgency experienced by bidders in online auctions resulted in increased heuristic processing of contrasting information from adjacent auction listings. Under time pressure, bidders were more likely to be affected by this contrasting peripheral information. We also found that bidders will discount contrasting peripheral information if they perceive salient persuasion intents in advertising presented by the auctioneers. The resulting contrast effects ultimately lead to changes in willingness-to-pay and underscore the importance of peripheral information from adjacent auctions in impacting auction outcomes.     

Distributed ascending proxy auction — A cryptographic approach

In recent years, auctions have become a very popular price discovery mechanism in the Internet. The common auction formats are typically centralized in nature. The peer-to-peer paradigm demands gearing up auctions for decentralized infrastructures. In this context, this paper proposes a distributed mechanism for ascending second-price auctions that relies on standard cryptographic algorithms. In essence, the auction protocol has the capability of preserving the privacy of the winning bidder’s true valuation. The auction protocol makes use of a high number of auctioneers divided into several groups. A bidder creates an encrypted chain of monotonously increasing bidding steps, where each bidding step can be decrypted by a different auctioneer group. This considerably reduces the attack and manipulation possibilities of malicious auctioneers. In addition, this secure approach does not require bidders to be online unless they are submitting their bid chain to the auctioneers.     

IntelliBid: An Event-Trigger-Rule-Based Auction System over the Internet

This paper presents the design and implementation of an Event-Trigger-Rule-Based auction system called IntelliBid. A network of Knowledge Web Servers, each consisting of a Web server, an Event-Trigger-Rule (ETR) Server, an Event Engine, a Knowledge Profile Manager, and Bid Servers and their proxies constitutes IntelliBid. Together, they provide auction-related services to the creator of an auction site and the bidders and suppliers of products. IntelliBid offers a number of desirable features. First and foremost is the flexibility offered to bidders for defining their own rules to control their bids in an automatic bidding process, which frees the bidders from having to be on-line to place bids. By using different rules, the bidders can apply different bidding strategies. Second, it furnishes valuable statistical information about past auctions to both suppliers (or sellers) and bidders. The information can assist a bidder in bidding and a seller in setting a reasonable base price and/or the minimum incremental price. Third, since rules that control the automatic bidding are installed and processed by the ETR servers installed at bidders' individual sites, bidders' privacy and security are safeguarded. The statistical information that is released by IntelliBid only depicts the trend of the bidding prices of a product. The information about bidders is kept completely secret, thus safeguarding the privacy of the bidders. Fourth, IntelliBid's event, event filtering and event notification mechanisms keep both bidders and suppliers timely informed of auction events so that they or their software system can take the proper actions in the auction process. Fifth, any registered user of IntelliBid, bidder or supplier, can monitor the bids placed to any product being auctioned in IntelliBid. Sixth, IntelliBid allows bidders to do both on-line (or manual) bidding and automatic bidding. It also allows a bidder to participate in several auctions at the same time, in both manual and automated modes. The bidding of a product can depend on the result of the bidding of another product. Last, but not least, IntelliBid allows a person or organization to play both the role of bidder and the role of supplier simultaneously. The Profile Manager keeps the information as a bidder and information as a supplier separately. Moreover, IntelliBid's architecture uses a parallel event management system to do event registration and notification. This paper also reports the result of a performance study on the implication of using such a parallel system to achieve scalability.     

Optimal-in-expectation redistribution mechanisms

Many important problems in multiagent systems involve the allocation of multiple resources among the agents. If agents are self-interested, they will lie about their valuations for the resources if they perceive this to be in their interest. The well-known VCG mechanism allocates the items efficiently, is strategy-proof (agents have no incentive to lie), and never runs a deficit. Nevertheless, the agents may have to make large payments to a party outside the system of agents, leading to decreased utility for the agents. Recent work has investigated the possibility of redistributing some of the payments back to the agents, without violating the other desirable properties of the VCG mechanism.Previous research on redistribution mechanisms has resulted in a worst-case optimal redistribution mechanism, that is, a mechanism that maximizes the fraction of VCG payments redistributed in the worst case. In contrast, in this paper, we assume that a prior distribution over the agents' valuations is available, and our goal is to maximize the expected total redistribution.In the first part of this paper, we study multi-unit auctions with unit demand. We analytically solve for a mechanism that is optimal among linear redistribution mechanisms. We also propose discretized redistribution mechanisms. We show how to automatically solve for the optimal discretized redistribution mechanism for a given discretization step size, and show that the resulting mechanisms converge to optimality as the step size goes to zero. We present experimental results showing that for auctions with many bidders, the optimal linear redistribution mechanism redistributes almost everything, whereas for auctions with few bidders, we can solve for the optimal discretized redistribution mechanism with a very small step size.In the second part of this paper, we study multi-unit auctions with nonincreasing marginal values. We extend the notion of linear redistribution mechanisms, previously defined only in the unit demand setting, to this more general setting. We introduce a linear program for finding the optimal linear redistribution mechanism. This linear program is unwieldy, so we also introduce one simplified linear program that produces relatively good linear redistribution mechanisms. We conjecture an analytical solution for the simplified linear program.     

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.     

A graphical formalism for mixed multi-unit combinatorial auctions

Mixed multi-unit combinatorial auctions are auctions that allow participants to bid for bundles of goods to buy, for bundles of goods to sell, and for transformations of goods. The intuitive meaning of a bid for a transformation is that the bidder is offering to produce a set of output goods after having received a set of input goods. To solve such an auction the auctioneer has to choose a set of bids to accept and decide on a sequence in which to implement the associated transformations. Mixed auctions can potentially be employed for the automated assembly of supply chains of agents. However, mixed auctions can be effectively applied only if we can also ensure their computational feasibility without jeopardising optimality. To this end, we propose a graphical formalism, based on Petri nets, that facilitates the compact represention of both the search space and the solutions associated with the winner determination problem for mixed auctions. This approach allows us to dramatically reduce the number of decision variables required for solving a broad class of mixed auction winner determination problems. An additional major benefit of our graphical formalism is that it provides new ways to formally analyse the structural and behavioural properties of mixed auctions.     

“Bid more,pay less” – overbidding and the Bidder’s curse in teleshopping auctions

This paper provides a theoretical and empirical analysis of the multi-unit Dutch teleshopping auctions of 1–2–3.tv. There are two channels for sales transactions: Customers either bid in the teleshopping auctions or purchase in the online shop for a simultaneously available fixed price. Our theoretical analysis indicates that profit-maximizing customers do not overbid the fixed price, since they risk experiencing the Bidder’s Curse: Buying at a uniform auction price that is higher than the fixed price. Our data set includes nearly 700,000 recorded bids. We find that customers overbid in 26% of all bids, but the Bidder’s Curse only occurs in 5% of all auctions. Offline-bidders (telephone calls) overbid both by larger amounts and more often than do online-bidders (website or app), and, on average, the most frequent customers do not experience learning effects.     

Optimal reserve prices in weighted GSP auctions

Most search engines use the weighted Generalized Second Price (wGSP) auction to sell keyword-based text ads, generating billions of dollars of advertising revenue every year. Designing and implementing near-optimal reserve prices for these wGSP auctions are naturally important problems for both academia and industry.In this paper, we show how to calculate and implement the near-optimal reserve price of the wGSP mechanism in realistic settings. Unlike reserve prices in standard single-item auctions, optimal reserve prices in wGSP auctions are discriminatory, different even for advertisers bidding on the same keyword. The optimal reserve price results can be extended to support CPA/CPC/CPM¹ hybrid auctions.Our simulations indicate that setting a proper reserve price will transfer some bidder utility (payoff) to auctioneer utility, resulting in higher revenue for the search engine. We describe a practical methodology to implement optimal reserve prices in production systems.