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Bidding strategies in agent based continuous double auctions. / CUHK electronic theses & dissertations collection

Continuous Double Auction (CDA) is an efficient market institution for real-world trading. Negotiation capabilities for software agents are a central concern. Specially, agents need to be able to prepare bids for and evaluate offers on behalf of the users they represent with the aim of obtaining the maximum benefit for their users. They do this according to some bidding strategies. However, in many cases, on one hand, determining which strategy to employ is a complex decision making task because of the inherent uncertainty and dynamics of the auction market; on the other hand, strategies in the literature do not adapt very well to the dynamic markets. To this end, this thesis is concerned with developing novel bidding strategies for CDAs and enhancing the performance of different strategies in CDAs with respect to adaptivity by designing some generally used tools. / In this thesis, we focus on two types of CDAs. One is the CDAs with a deadline of inactive interval. Another is the CDAs with a fixed deadline. Three kinds of adaptive behaviors are proposed to enhance the performance of the widely adopted strategies in CDAs in the literature. They are adaptive softness, adaptive judgment of price acceptability, and adaptive time strategies. First, in the CDAs with a deadline of inactive interval, we design novel adaptive strategies, named Adaptive Attitude strategies, based on eagerness. Eagerness indicates the current supply and demand relationship from the agent's own point of view. To compute the value of eagerness, fuzzy sets and fuzzy logic are used to cope with the significant degrees of uncertainty in CDA markets. We define two kinds of adaptive behaviors, adaptive softness and adaptive judgment of price acceptability. Both of them resemble human traders' behaviors to compromise and set thresholds on acceptable prices in the trading process of real life markets and can enhance the performance of various strategies. Secondly, in the CDAs with a fixed deadline, time strategies are researched by us. In this market, every agent is aware of the time. Therefore adaptive time strategies are introduced to guide the agent to arrange his behavior according to time, which can enhance the performance of different strategies. Both the novel strategies and the enhanced strategies have been demonstrated to be superior in a wide range of CDA circumstances. We show that eagerness is a, practical solution for this class of application. We believe that this work represents an important step towards adapting agents in auctions. / Through the work in this thesis, Adaptive Attitude (AA) strategies have been demonstrated to be superior in a wide range of CDA scenarios. Moreover, three kinds of adaptive behaviors have been shown to greatly enhance the performance of the widely adopted strategies in CDAs. / Ma Huiye. / "August 2006." / Adviser: Ho Fung Leung. / Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1731. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 151-162). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_343929
Date January 2006
ContributorsMa, Huiye., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, theses
Formatelectronic resource, microform, microfiche, 1 online resource (xix, 162 p. : ill.)
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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