EFFECTS OF A RATIONING RULE ON THE AUSUBEL AUCTION: A GENETIC ALGORITHM IMPLEMENTATION

The increasing use of auctions as a selling mechanism has led to a growing interest in the subject. Thus both auction theory and experimental examinations of these theories are being developed. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this article is to develop a genetic algorithm to find automatically a bidder optimal strategy while the other players are always bidding sincerely. To this end a specific dynamic multiunit auction has been selected: the Ausubel auction, with private values, dropout information, and with several rationing rules implemented. The method provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments that differ in the elasticity of their demand curves, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform sincere bidding when rationing is needed.     

Real-time detection of shill bidding in online auctions: A literature review

Online auctions have become an increasingly popular and convenient way for conducting ecommerce transactions on the Web. However, the rapid surge of users participating in online auctions has led to auction fraud. Among the types of auction fraud, the most prominent is Shill bidding. Shill bidding is intentionally fake bidding by a seller on his/her own auction to inflate the final price. This can be accomplished either by the seller himself/herself or by someone colluding with the seller to place fake bids on his/her behalf. Therefore, it is difficult to manually investigate the large amount of auctions and bidders for shill bidding activities. Detecting shill bidding in real-time is the most effective way to reduce the loss result of the auction fraud. Researchers have proposed multiple approaches and experimented to control the losses incurred due to shill bidding. This paper investigates the real-time detection techniques of shill bidding. It also provides a brief overview of major work that has been conducted in shill bidding detection including both offline and real-time approaches. Furthermore, this paper identifies research gaps in the detection and prevention of shill bidding behaviours. It also provides future research issues and challenges to detect shill bidding in real-time.     

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.     

DECLARATIVE SPECIFICATION OF FAULT TOLERANT AUCTION PROTOCOLS: THE ENGLISH AUCTION CASE STUDY

Auction mechanisms are nowadays widely used in electronic commerce Web sites for buying and selling items among different users. The increasing importance of auction protocols in the negotiation phase is not limited to online marketplaces. In fact, the wide applicability of auctions as resource‐allocation and negotiation mechanisms have also led to a great deal of interest in auctions within the agent community. A challenging issue for agents operating in open Multiagent Systems (such as the emerging semantic Web infrastructure) concerns the specification of declarative communication rules which could be published and shared allowing agents to dynamically engage well‐known and trusted negotiation protocols. To cope with real‐world applications, these rules should also specify fault tolerant patterns of interaction, enabling negotiating agents to interact with each other tolerating failures, for instance terminating an auction process even if some bidding agents dynamically crash. In this paper, we propose an approach to specify fault tolerant auction protocols in open and dynamic environments by means of communication rules dealing with crash failures of agents. We illustrate these concepts considering a case study about the specification of an English Auction protocol which tolerate crashes of bidding agents and we discuss its properties.     

MoCAAS: auction agent system using a collaborative mobile agent in electronic commerce

To get the items that a buyer wants in an Internet auction, he must search for the items through several auction sites. When the bidding starts, the buyer needs to connect to these auction sites frequently so that he can monitor the bid states and re-bid. A reserve-price auction reduces the number of connections, but this limits the user's bidding strategy. Another problem is equity between the buyer and the seller. Both the buyer and the seller should profit within proper limits. In this paper, we propose an auction agent system using a collaborative mobile agent and a brokering mechanism called MoCAAS (Mobile collaborative auction agent system), which mediates between the buyer and the seller and executes bidding asynchronously and autonomously. This reduces the network load more than with other auction-agents, offers more intelligent bidding, and increases the clear ratio.     

Automatic price negotiation on the web: An agent-based web application using fuzzy expert system

A traditional internet auction is restricted by the limitation of time. It is necessary to conduct an internet auction in a certain time period. The final trading price is determined until this certain period ends. This study improves this situation by removing the time limitation. Based on the fuzzy inference theory, this paper proposes an agent-based price negotiation system for on-line auctions. Mainly, three agents are used in the study: a seller agent, a buyer agent, and a mediator agent. The proposed system provides an easy-to-use environment and good customizability for users (buyers or sellers) to customize their price negotiation strategies using user-defined fuzzy rules. The final negotiated price is immediately determined after the buyer sends his bids to the proposed system. This study develops a Java-based computer package to implement the price negotiation system where Model-View-Controller (MVC) design pattern is employed in design of the package. Unified Modeling Language (UML) is also utilized to describe the structures and behaviors of the package. To validate the proposed system, this study built an on-line auction website with the proposed price negotiation mechanism for internet users to buy or sell their merchandises. An evaluation was finally conducted to investigate the users’ satisfaction with the proposed system.Briefly, the proposed system is featured by: (1) instantly getting negotiated price without waiting; (2) conducting price negotiation at any time; (3) determining strategy rules easily, and (4) using customizable negotiation strategies defined by users.     

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.     

An adaptive attitude bidding strategy for agents in continuous double auctions

A continuous double auction (CDA) is an efficient market institution for real-world trading of commodities and electronic marketplaces. In this paper, we present the design and analysis of a new bidding strategy for buyer and seller agents participating in agent-based CDAs. The strategy employs heuristic rules and a reasoning mechanism based on a two-level adaptive bid-determination method, including short-term and long-term attitudes. Agents adopting the strategy dynamically adjust their behaviors in response to the changes of the supply and demand relationships in the market. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature.     

An adaptive attitude bidding strategy for agents in continuous double auctions

A continuous double auction (CDA) is an efficient market institution for real-world trading of commodities and electronic marketplaces. In this paper, we present the design and analysis of a new bidding strategy for buyer and seller agents participating in agent-based CDAs. The strategy employs heuristic rules and a reasoning mechanism based on a two-level adaptive bid-determination method, including short-term and long-term attitudes. Agents adopting the strategy dynamically adjust their behaviors in response to the changes of the supply and demand relationships in the market. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature.     

An evolutionary approach to deception in multi-agent systems

Understanding issues of trust and deception are key to designing robust, reliable multi-agent systems. This paper builds on previous work which examined the use of auctions as a model for exploring the concept of deception in such systems. We have previously described two forms of deceptive behaviour which can occur in a simulated repeated English auction. The first of these types of deception involves sniping or late bidding, which not only allows an agent to conceal its true valuation for an item, but also potentially allows it to win an item for which it may not possess the highest valuation. The second deceptive strategy involves the placing of false bids which are designed to reduce an opponent’s potential profit. In this work we examine the potential shortcomings of those two strategies and investigate whether or not their individual strengths can be combined to produce a successful hybrid deceptive strategy.     

Algorithm selection in bilateral negotiation

Despite the abundance of strategies in the multi-agent systems literature on repeated negotiation under incomplete information, there is no single negotiation strategy that is optimal for all possible domains. Thus, agent designers face an “algorithm selection” problem—which negotiation strategy to choose when facing a new domain and unknown opponent. Our approach to this problem is to design a “meta-agent” that predicts the performance of different negotiation strategies at run-time. We study two types of the algorithm selection problem in negotiation: In the off-line variant, an agent needs to select a negotiation strategy for a given domain but cannot switch to a different strategy once the negotiation has begun. For this case, we use supervised learning to select a negotiation strategy for a new domain that is based on predicting its performance using structural features of the domain. In the on-line variant, an agent is allowed to adapt its negotiation strategy over time. For this case, we used multi-armed bandit techniques that balance the exploration–exploitation tradeoff of different negotiation strategies. Our approach was evaluated using the GENIUS negotiation test-bed that is used for the annual international Automated Negotiation Agent Competition which represents the chief venue for evaluating the state-of-the-art multi-agent negotiation strategies. We ran extensive simulations using the test bed with all of the top-contenders from both off-line and on-line negotiation tracks of the competition. The results show that the meta-agent was able to outperform all of the finalists that were submitted to the most recent competition, and to choose the best possible agent (in retrospect) for more settings than any of the other finalists. This result was consistent for both off-line and on-line variants of the algorithm selection problem. This work has important insights for multi-agent systems designers, demonstrating that “a little learning goes a long way”, despite the inherent uncertainty associated with negotiation under incomplete information.     

基于博弈分析的电子商务自动协商系统

为提高基于拍卖机制的电子商务自动协商系统效率，该文以非合作博弈论为基础提出了基于博弈分析的自动协商Agent模型及基于拍卖机制和博弈分析的自动投标协商算法，实现了一个基于拍卖机制和博弈分析的电子商务自动协商原型系统，并应用在一个企业敏捷供应链管理系统中实现自动协商交易。     

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.     

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.     

Exploring local search in winner determination problem

In Combinatorial Auctions, multiple goods (items) are available for auction simultaneously, and bidders bid for combinations of goods called bundles. In the single unit combinatorial auction problem, there is only single unit of each item present and the items are considered to be indivisible. This forms the supply side constraint resulting in a need by the seller to decide on which bundles to allocate for maximizing the revenue. Determining these winning bundles is an NP-complete problem and is known by the name of Winner Determination Problem (WDP) in literature. Combinatorial auction mechanism has a wide range of practical applications such as allocation of railroads, auction of adjacent pieces of real estate, auction of airport landing slots, distributed job shop scheduling etc. The optimal heuristic search algorithms like CASS and CABOB proposed for solving the WDP take a lot of CPU time for solving large problem instances. Though these algorithms give optimal solutions, they would be useful only in scenarios where time is not at all a constraint, and the bidding is a one time affair. This poses a limitation in conducting combinatorial auctions with multiple rounds of bidding and hence hinders the widespread use of combinatorial auctions on the patterns of traditional English auction and internet auctions. Thus faster solution of WDP holds the key to widespread use and popularity of combinatorial auctions. This paper attempts to address the above issue, and proposes a simple local search technique LSWDP (Local Search for Winner Determination Problem) that runs very fast and provides solutions quite close to optimal for a number of applications. It also outputs optimal solutions in many instances taking only a fraction of the CPU time taken by CASS. In addition LSWDP was found to outperform the anytime algorithm CASS given a time cutoff. This paper also presents an enhancement called LSCN (Local Search using Complimentary Neighborhood) that provides still better solutions in terms of close to optimal behavior and achieves a much better strike rate in hitting optimal solutions. LSCN runs slower than LSWDP but still takes only a fraction of the CPU time taken by CASS thus encouraging the use of local search for combinatorial auctions. Finally, an extension to multi-partition search has been proposed and analyzed.

Bidding strategies for realistic multi-unit sealed-bid auctions

When autonomous agents decide on their bidding strategies in real world auctions, they have a number of concerns that go beyond the models that are normally analyzed in traditional auction theory. Oftentimes, the agents have budget constraints and the auctions have a reserve price, both of which restrict the bids the agents can place. In addition, their attitude need not be risk-neutral and they may have uncertainty about the value of the goods they are buying. Some of these issues have been examined individually for single-unit sealed-bid auctions. However, in this paper, we extend this analysis to the multi-unit case, and also analyze the multi-unit sealed-bid auctions in which a combination of these issues are present, for unit-demand bidders. This analysis constitutes the main contribution of this paper. We then demonstrate the usefulness in practice of this analysis; we show in simulations that taking into account all these issues allows the bidders to maximize their utility. Furthermore, using this analysis allows a seller to improve her revenue, i.e. by selecting the optimal reserve price and auction format.     

eMediator: A Next Generation Electronic Commerce Server

This paper presents eMediator , an electronic commerce server prototype that demonstrates ways in which algorithmic support and game–theoretic incentive engineering can jointly improve the efficiency of e–commerce. eAuctionHouse , the configurable auction server, includes a variety of generalized combinatorial auctions and exchanges, pricing schemes, bidding languages, mobile agents, and user support for choosing an auction type. We introduce two new logical bidding languages for combinatorial markets: the XOR bidding language and the OR–of–XORs bidding language. Unlike the traditional OR bidding language, these are fully expressive. They therefore enable the use of the Clarke–Groves pricing mechanism for motivating the bidders to bid truthfully. eAuctionHouse also supports supply/demand curve bidding. eCommitter , the leveled commitment contract optimizer, determines the optimal contract price and decommitting penalties for a variety of leveled commitment contracting mechanisms, taking into account that rational agents will decommit strategically in Nash equilibrium. It also determines the optimal decommitting strategies for any given leveled commitment contract. eExchangeHouse , the safe exchange planner, enables unenforced anonymous exchanges by dividing the exchange into chunks and sequencing those chunks to be delivered safely in alternation between the buyer and the seller.     

基于自适应差分进化算法的多边多议题协商

为提高基于智能体(agent)的电子商务多边多议题协商的效率及稳定性,提出改进的自适应差分进化算法(ADE)并将其引入到合作环境下的多边多议题协商问题中。差分进化(DE)算法是目前求解连续空间内全局优化问题性能最优的进化优化算法之一。利用该算法收敛速度快、收敛精度高、全局寻优能力强等特点加快多边多议题协商的速度,使协商效率更高、稳定性更强。通过与目前解决多边多议题协商问题效果最好的混合遗传算法(HGA)对比,实验结果表明,自适应差分进化算法具有更快的收敛速度和更好的稳定性,可以使多边多议题协商中的各智能体达到协商最优解,并有效地减少协商次数,提高协商的效率和稳定性。     

Understanding auction fever: a framework for emotional bidding

Auction fever is a multifaceted phenomenon that is frequently observed in both traditional and Internet auctions. In order to gain a better understanding of its causes, we develop a conceptual framework to analyze emotions in auctions, which is based on an exhaustive literature review. The framework integrates rational calculus with emotional aspects and suggests that emotional processing is triggered at three different stages of an auction: First, the economic environment can affect a bidder’s level of perceived competition and thus influence the bidding strategy prior to the auction. Second, auction events may have ramifications on the bidder’s emotional state during the auction due to previous investments or perceived ownership. Third, past auction outcomes may impact future bidding behavior through emotions such as the joy of winning or loser regret. Auction fever, eventually, is a phenomenon that results from the interplay of these emotional processes and causes a bidder to deviate from an initially chosen bidding strategy.