Probabilistic Automated Bidding in Multiple Auctions

This paper presents an approach to develop bidding agents that participate in multiple auctions with the goal of obtaining an item with a given probability. The approach consists of a prediction method and a planning algorithm. The prediction method exploits the history of past auctions to compute probability functions capturing the belief that a bid of a given price may win a given auction. The planning algorithm computes a price and a set of compatible auctions, such that by sequentially bidding this price in each of the auctions, the agent can obtain the item with the desired probability. Experiments show that the approach increases the payoff of their users and the welfare of the market.     

Decentralized Peer-to-Peer Auctions

This paper proposes a flexible architecture for the creation of Internet auctions. It allows the custom definition of the auction parameters, and provides a decentralized control of the auction process. Auction policies are defined as laws in the Law Governed Interaction (LGI) paradigm. Each of these laws specifies not only the auction algorithm itself (e.g., open-cry, Dutch, etc.) but also how to handle the other parameters usually involved in the online auctions, such as certification, auditioning, and treatment of complaints. LGI is used to enforce the rules established in the auction policy within the agents involved in the process. After the agents find out about the actions, they interact in a peer-to-peer communication protocol, reducing the role of the centralized auction room to an advertising registry, and taking profit of the distributed nature of the Internet to conduct the auction. The paper presents an example of an auction law, illustrating the use of the proposed architecture.     

A Multi-attribute bidding strategy for a single-attribute auction marketplace

Auctions and negotiations are important mechanisms of economic exchange. Negotiations are less structured as they involve exchange of offers by both parties. They may involve several issues and new issues can be added in the course of negotiations. Auctions, on the other hand are highly structured, and thus they are much amenable to automation. However, most existing auctions involve bidding on a single issue (price). This is an obvious limitation as participants may have different preferences over multiple attributes of a given type of product or service. This paper proposes an approach by which a software agent solution could effectively implement a multi-attribute bidding strategy for single-attribute auction marketplaces. This allows to significantly expand the set of potential auctions to bid on, while assuring the minimum utility level for the bidder at a given point in time. The bidding model is based upon concession-making curve introduced in prior work on electronic negotiations. Using data collected from eBay the paper shows that bidding across several attributes would result in higher utility outcome, and faster results than bidding within a single attribute set.     

同时参加多宗网上拍卖的智能Agent的设计

本文提出的方法使得智能Agent能够同时监视多宗网上拍卖.Agent站在消费者的立场上作各种决定,并按照使用者的偏好尽力保证拍品的获得.Agent监视并收集各种正在进行的拍卖信息,然后决定它愿意参加哪个或同时参加哪些拍卖.至于在拍卖上的出价则基于一系列的策略和方法.     

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.     

t-Private and t-secure Auctions

In most of the auction systems the values of bids are known to the auctioneer. This allows him to manipulate the outcome of the auction. Hence, one might be interested in hiding these values. Some cryptographically secure protocols for electronic auctions have been presented in the last decade. Our work extends these protocols in several ways. On the basis of garbled circuits, i.e., encrypted circuits, we present protocols for sealed-bid auctions that fulfill the following requirements: 1) protocols are information-theoretically t-private for honest but curious parties; 2) the number of bits that can be learned by malicious adversaries is bounded by the output length of the auction; 3) the computational requirements for participating parties are very low: only random bit choices and bitwise computation of the XOR-function are necessary. Note that one can distinguish between the protocol that generates a garbled circuit for an auction and the protocol to evaluate the auction. In this paper we address both problems. We will present a t-private protocol for the construction of a garbled circuit that reaches the lower bound of 2t 1 parties, and a more randomness effcient protocol for (t 1)2 parties. Finally, we address the problem of bid changes in an auction.     

New Algorithms for Mining the Reputation of Participants of Online Auctions

The assessment of credibility and reputation of contractors in online auctions is the key issue in providing reliable environment for customer-to-customer e-commerce. Confident reputation rating system is an important factor in managing risk and building customer satisfaction. Unfortunately, most online auction sites employ a very simple reputation rating scheme that utilizes user feedbacks and comments issued after committed auctions. Such schemes are easy to deceive and do not provide satisfactory protection against several types of fraud. In this paper we propose two novel measures of trustworthiness, namely, credibility and density. We draw inspiration from social network analysis and present two algorithms for reputation rating estimation. Our first algorithm computes the credibility of participants by an iterative search of inter-participant connections. Our second algorithm discovers clusters of participants who are densely connected through committed auctions. We test both measures on real-world data and we experimentally compare them with existing solutions.     

Optimal Design of Internet-Based Auctions

Online auctions are a profitable, exciting, and dynamic part of electronic commerce, and behave in ways, which do not match classical auction theory. This paper models an online auction in terms of a Markov chain on a state space defined by the current price of the item and the number of bidders who have been previously bumped. It provides a mathematical model, some approximations, which were necessary to convert it into a tractable problem and solutions to a small and a medium-sized theoretical auction. The model results were validated through a comparison with real-world online auction data, showing promise as a predictor of final auction prices. The results of the auction model are also useful in solving an optimization problem that incorporates inventory management considerations in determining optimal auction size.     

Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions

In developing open, heterogeneous and distributed multi-agent systems researchers often face a problem of facilitating negotiation and bargaining amongst agents. It is increasingly common to use auction mechanisms for negotiation in multi-agent systems. The choice of auction mechanism and the bidding strategy of an agent are of central importance to the success of the agent model. Our aim is to determine the best agent learning algorithm for bidding in a variety of single seller auction structures in both static environments where a known optimal strategy exists and in complex environments where the optimal strategy may be constantly changing. In this paper we present a model of single seller auctions and describe three adaptive agent algorithms to learn strategies through repeated competition. We experiment in a range of auction environments of increasing complexity to determine how well each agent performs, in relation to an optimal strategy in cases where one can be deduced, or in relation to each other in other cases. We find that, with a uniform value distribution, a purely reactive agent based on Cliff’s ZIP algorithm for continuous double auctions (CDA) performs well, although is outperformed in some cases by a memory based agent based on the Gjerstad Dickhaut agent for CDA.     

Kombinatorische Auktionen in der betrieblichen BeschaffungEine Analyse grundlegender Entwurfsprobleme – An Analysis of Design Problems in Combinatorial Procurement Auctions

Traditional auction mechanisms support price negotiations on a single item. The Internet allows for the exchange of much more complex offers in real-time. This is one of the reasons for much research on multidimensional auction mechanisms allowing negotiations on multiple items, multiple units, or multiple attributes of an item, as they can be regularly found in procurement. Combinatorial auctions, for example, enable suppliers to submit bids on bundles of items. A number of laboratory experiments has shown high allocative efficiency in markets with economies of scope. For suppliers it is easier to express cost savings due to bundling (e. g., decreased transportation or production costs). This can lead to significant savings in total cost of the procurement manager. Procurement negotiations exhibit a number of particularities:

It is often necessary to consider qualitative attributes or volume discounts in bundle bids. These complex bid types have not been sufficiently analyzed.

The winner determination problem requires the consideration of a number of additional business constraints, such as limits on the spend on a particular supplier or the number of suppliers.

Iterative combinatorial auctions have a number of advantages in practical applications, but they also lead to new problems in the determination of ask prices.

In this paper, we will discuss fundamental problems in the design of combinatorial auctions and the particularities of procurement applications. Reprint of an article from WIRTSCHAFTSINFORMATIK 47(2)2005:126–134.     

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.     

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.     

Information Aggregation in Double Auctions: Rational Expectations and the Winner's Curse

This paper inquires about the ability of double auction institutions to aggregate information in the context of a common value information structure that is known to produce the winner's curse in sealed bid environments. While many fundamental features of the economic trading mechanism are different from those studied in the context of sealed bids, the pattern of information distributed to the population of traders is the same. This gives us an opportunity to determine if the behaviors reported in sealed bid environments can be detected in the more active market environment. As such, the experiments are also a test of the robustness of earlier experiments that demonstrate that in economies with homogeneous preferences single compound securities organized by double auctions are able to aggregate information. The basic result is that a severe winner's curse is not observed. The irrationality observed in sealed bids does not extend itself to the double auction environment. Information aggregation is observed and the rational expectations model receives support.     

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.     

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.     

Online auctions enabling the secondary computer market

The Internet is enabling new forms of commerce and novel markets. One example is the secondary computer market, facilitating exchange between quality sensitive sellers, oftentimes businesses, and price sensitive buyers. As this market does not have a viable physical counterpart with reference prices, it is developing via online auctions. One question of interest in the evolution of this market is the determinants of prices. Using a dataset of 2,000 laptop auctions in a seven-month period, this research provides support for accepted auction theory while raising questions that deserve further explanation. The negative relationship between supply and auction price supports standard supply and demand theory, while higher prices for better features is consistent with vertical differentiation. Even within accepted theory this research broadens the understanding of auction behavior. There is clear support for the “price decline anomaly” where prices in sequential auctions decline, violating the “law of one price.” One result that deserves further attention is that midweek auctions realize higher prices. A second is that price changes over time are not monotonic. Future research should replicate and explain these results, as well as extend them to other auction settings. As the secondary computer market evolves it will impact the primary computer market.     

求解组合拍卖NP问题的遗传算法

多智能体系统采用多件物品组合拍卖协商协议能高效地实现组合资源及多任务的分配,但在传统组合拍卖协商协议中拍卖方选择买方以实现最大赢利的过程是一个NP问题,本文提出了利用改进的遗传算法来求解该NP问题的新方法,并应用于一个企业供应链管理的自动协商交易系统中。实验表明,该算法具有较优的性能。     

A theoretical and empirical investigation of multi‐item on‐line auctions

In this paper we explore and analyze the structure of Internet auctions from an analytical and an empirical perspective. Such web‐based auctions are rapidly emerging as a mercantile process of choice in the electronic marketplace. We observe current Internet auctions for one‐time products, such as rapidly aging hardware, and analyze them within the framework of the existing auction theory. While traditional auction theory focuses on single‐item auctions, we observe that a majority of on‐line auctions are multi‐item auctions. A significant contribution of this work is the theoretical derivation of the structure of the winning bids in multi‐item progressive on‐line auctions. Additionally, for comparative purposes, we explore the structural characteristics of alternative multi‐item auction mechanisms proposed in the auction theory. We derive hypotheses based on our analytical results and compare two different types of auction mechanisms. We test the traditional auction theory assumption regarding the homogeneity of bidders and present the first ever empirically derived classification and performance‐comparison of on‐line bidders. We test our hypotheses using real‐world empirical data obtained by tracking a premier web‐based auction site. Statistical analysis of the data indicates that firms may gain by choosing alternative auction mechanisms. We also provide directions for further exploration of this emerging but important dimension of electronic commerce. This revised version was published online in July 2006 with corrections to the Cover Date.     

Auction-Based Mechanisms for Electronic Procurement

Auction-based mechanisms are extremely relevant in modern day electronic procurement systems since they enable a promising way of automating negotiations with suppliers and achieve the ideal goals of procurement efficiency and cost minimization. This paper surveys recent research and current art in the area of auction-based mechanisms for e-procurement. The survey delineates different representative scenarios in e-procurement where auctions can be deployed and describes the conceptual and mathematical aspects of different categories of procurement auctions. We discuss three broad categories: 1) single-item auctions: auctions for procuring a single unit or multiple units of a single homogeneous type of item; 2) multi-item auctions: auctions for procuring a single unit or multiple units of multiple items; and 3) multiattribute auctions where the procurement decisions are based not only on costs but also on attributes, such as lead times, maintenance contracts, quality, etc. In our review, we present the mathematical formulations under each of the above categories, bring out the game theoretic and computational issues involved in solving the problems, and summarize the current art. We also present a significant case study of auction based e-procurement at General Motors.