Chan Yiu Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 43-44). / Abstracts in English and Chinese. / Abstract --- p.11 / 摘要 --- p.1 / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Reviews of Existing Models --- p.5 / Chapter 2.1 --- Second Price Sealed-bid Auction --- p.5 / Chapter 2.2 --- First Price Sealed-bid Auction --- p.6 / Chapter 2.3 --- Revenue Equivalence Principle --- p.6 / Chapter 2.4 --- Risk-Averse Bidders --- p.7 / Chapter 2.5 --- Budget Constraints --- p.7 / Chapter 2.6 --- Budget-Constrained Sequential Auction with --- p.9 / Chapter Chapter 3. --- The Model --- p.11 / Chapter 3.1 --- Model´ةs Background --- p.11 / Chapter 3.2 --- Auction´ة s Rules… --- p.13 / Chapter 3.3 --- Critical Values --- p.17 / Chapter 3.4 --- Revenues of the Sequential Auction --- p.18 / Chapter 3.5 --- Results --- p.19 / Chapter Chapter 4. --- More General Cases --- p.28 / Chapter 4.1 --- A possibility of winning both goods by a bidder --- p.28 / Chapter 4.2 --- The winning bid of the first stage auction is concealed --- p.36 / Chapter Chapter 5 --- Conclusion --- p.39 / References --- p.41

published_or_final_version / abstract / Mathematics / Master / Master of Philosophy

published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

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.

Sui, Xin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 91-97). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- MAS-Based Resource Allocation Problems --- p.1 / Chapter 1.2 --- Combinatorial Auction As a Solution --- p.4 / Chapter 1.3 --- Strategy Issues --- p.5 / Chapter 1.4 --- Structure of This Work --- p.7 / Chapter 2 --- Combinatorial Auctions and Bidding Strategies --- p.9 / Chapter 2.1 --- Combinatorial Auctions for Resource Allocation --- p.9 / Chapter 2.2 --- Bidding Strategies in Combinatorial Auctions --- p.11 / Chapter 2.2.1 --- Berhault's Strategies --- p.11 / Chapter 2.2.2 --- An's Strategies --- p.13 / Chapter 2.2.3 --- Schwind´ةs Strategies --- p.15 / Chapter 2.2.4 --- Wilenius´ةs Strategy --- p.17 / Chapter 2.2.5 --- Overview of Previous Strategies --- p.18 / Chapter 3 --- An Adaptive Bidding Strategy in Static Markets --- p.21 / Chapter 3.1 --- Basic Concepts --- p.22 / Chapter 3.2 --- The Core Algorithm --- p.24 / Chapter 3.3 --- Experimental Evaluation --- p.31 / Chapter 3.3.1 --- Experiment Setup --- p.32 / Chapter 3.3.2 --- Experiment Results and Analysis --- p.33 / Chapter 4 --- An Adaptive Bidding Strategy in Dynamic Markets --- p.38 / Chapter 4.1 --- Basic Concepts --- p.39 / Chapter 4.2 --- The Core Algorithm --- p.42 / Chapter 4.3 --- Experimental Evaluation --- p.48 / Chapter 4.3.1 --- Experiment Setup --- p.49 / Chapter 4.3.2 --- Experiment Results and Analysis --- p.51 / Chapter 5 --- A Q-Learning Based Adaptive Bidding Strategy in Static Mar-kets --- p.59 / Chapter 5.1 --- An Overview of Q-Learning --- p.60 / Chapter 5.2 --- Basic Concepts --- p.63 / Chapter 5.3 --- The Core Algorithm --- p.65 / Chapter 5.4 --- Experimental Evaluation --- p.70 / Chapter 5.4.1 --- Experiment Setup --- p.70 / Chapter 5.4.2 --- Experiment Results and Analysis --- p.72 / Chapter 6 --- Discussion --- p.82 / Chapter 6.1 --- Applicability of the Adaptive Strategies --- p.82 / Chapter 6.2 --- Generalization of the Adaptive Strategies --- p.83 / Chapter 7 --- Conclusion and Future Work --- p.86 / Chapter 7.1 --- Conclusion --- p.86 / Chapter 7.2 --- Future Work --- p.88 / Bibliography --- p.91

Advances in information technology have created radically new business models, most notably the integration of advertising with keyword-based targeting, or "keyword advertising." Keyword advertising has two main variations: advertising based on keywords employed by users in search engines, often known as "sponsored links," and advertising based on keywords embedded in the content users view, often known as "contextual advertising." Keyword advertising providers such as Google and Yahoo! use auctions to allocate advertising slots. This dissertation examines the design of keyword auctions. It consists of three essays. The first essay "Ex-Ante Information and the Design of Keyword Auctions" focuses on how to incorporate available information into auction design. In our keyword auction model, advertisers bid their willingness-to-pay per click on their advertisements, and the advertising provider can weigh advertisers' bids differently and require different minimum bids based on advertisers' click-generating potential. We study the impact and design of such weighting schemes and minimum-bids policies. We find that weighting scheme determines how advertisers with different click-generating potential match in equilibrium. Minimum bids exclude low-valuation advertisers and at the same time may distort the equilibrium matching. The efficient design of keyword auctions requires weighting advertisers' bids by their expected click-through-rates, and requires the same minimum weighted bids. The revenue-maximizing weighting scheme may or may not favor advertisers with low click-generating potential. The revenue-maximizing minimum-bid policy differs from those prescribed in the standard auction design literature. Keyword auctions that employ the revenue-maximizing weighting scheme and differentiated minimum bid policy can generate higher revenue than standard fixed-payment auctions. The dynamics of bidders' performance is examined in the second essay, "Keyword Auctions, Unit-price Contracts, and the Role of Commitment." We extend earlier static models by allowing bidders with lower performance levels to improve their performance at a certain cost. We examine the impact of the weighting scheme on overall bidder performance, the auction efficiency, and the auctioneer's revenue, and derive the revenue-maximizing and efficient policy accordingly. Moreover, the possible upgrade in bidders' performance levels gives the auctioneer an incentive to modify the auction rules over time, as is confirmed by the practice of Yahoo! And Google. We thus compare the auctioneer's revenue-maximizing policies when she is fully committed to the auction rule and when not, and show that she should give less preferential treatment to low-performance advertisers when she is fully committed. In the third essay, "How to Slice the Pie? Optimal Share Structure Design in Keyword Auctions," we study the design of share structures in keyword auctions. Auctions for keyword advertising resources can be viewed as share auctions in which the highest bidder gets the largest share, the second highest bidder gets the second largest share, and so on. A share structure problem arises in such a setting regarding how much resources to set aside for the highest bidder, for the second highest bidder, etc. We address this problem under a general specification and derive implications on how the optimal share structure should change with bidders' price elasticity of demand for exposure, their valuation distribution, total resources, and minimum bids. / text

published_or_final_version / Business / Master / Master of Philosophy

Lam, Yim Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 40). / Abstract also in Chinese. / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Terminology --- p.4 / Chapter 3. --- Literature Review --- p.4 / Chapter 4. --- The Model --- p.13 / Chapter 5. --- Main Results --- p.18 / Chapter 6. --- Extensions / Chapter 6.1 --- Market with informed speculators and uninformed speculators --- p.26 / Chapter 6.2 --- "Market with informed speculators, uninformed speculators and noise traders" --- p.33 / Chapter 7. --- Conclusion --- p.36 / Appendix --- p.37 / References --- p.40

Chan Ying Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 85-89). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Importance of Reverse Auctions --- p.1 / Chapter 1.2 --- A Research Gap in Existing Literatures --- p.3 / Chapter 1.3 --- Research Focus --- p.4 / Chapter 1.4 --- Contributions of This Thesis --- p.5 / Chapter 1.5 --- Organization of This Thesis --- p.5 / Chapter 2 --- Background and Literature Review --- p.6 / Chapter 2.1 --- Review of Reverse Auctions --- p.6 / Chapter 2.1.1 --- Benefits of Reverse Auctions to Buyers --- p.9 / Chapter 2.1.2 --- Types of Reverse Auctions --- p.11 / Chapter 2.1.3 --- Implementation of the Entry-fee Reverse Auction --- p.13 / Chapter 2.2 --- Linkage between the Entry-fee Reverse Auction and the Multi- period Inventory Replenishment Model --- p.16 / Chapter 2.3 --- The Multi-period Inventory Replenishment Model with TOQC --- p.17 / Chapter 3 --- The Basic Models --- p.18 / Chapter 3.1 --- Strategic Sourcing Methodology - The Entry-fee Reverse Auc- tion --- p.18 / Chapter 3.1.1 --- Numerical Example --- p.26 / Chapter 3.2 --- Operational Procurement Methodology ´ؤ The Multi-period Inventory Replenishment Model with TOQC --- p.29 / Chapter 3.2.1 --- Numerical Example --- p.33 / Chapter 3.3 --- Chapter Summary --- p.34 / Chapter 4 --- Modifications Required for Integrating the Entry-fee Reverse Auction and the Multi-period Replenishment Model with TOQC --- p.36 / Chapter 4.1 --- Formulation of the Buyer's Expected Profit Function in the Multi-period Setting --- p.37 / Chapter 4.2 --- The Existence of Optimal TOQC --- p.38 / Chapter 4.2.1 --- Convexity of the Last-period Optimality Equation --- p.39 / Chapter 4.2.2 --- Convexity of the Two-period Problem --- p.41 / Chapter 4.2.3 --- Convexity of the N-period Problem --- p.44 / Chapter 4.3 --- The Computability of the Optimal TOQC --- p.47 / Chapter 4.4 --- Chapter Summary --- p.47 / Chapter 5 --- The Revised Model --- p.48 / Chapter 5.1 --- The Entry-fee Reverse Auction in the Multi-period Setting with TOQC --- p.48 / Chapter 5.1.1 --- Numerical Example --- p.55 / Chapter 5.2 --- Chapter Summary --- p.59 / Chapter 6 --- Numerical Analysis --- p.60 / Chapter 6.1 --- Comparison between the Fixed-quantity Reverse Auction and the Entry-fee Reverse Auction --- p.60 / Chapter 6.1.1 --- Number of Supplier --- p.63 / Chapter 6.1.2 --- Retail Price --- p.65 / Chapter 6.1.3 --- Coefficient of Variation for Demand Distribution . --- p.66 / Chapter 6.1.4 --- Average Improvement --- p.67 / Chapter 6.2 --- Duration of the Entry-fee Reverse Auction Cycle --- p.68 / Chapter 6.3 --- Chapter Summary --- p.76 / Chapter 7 --- Factors of Success for Holding the Entry-fee Reverse Auction --- p.77 / Chapter 7.1 --- Internal Organizational Infrastructure of the Buyer --- p.77 / Chapter 7.2 --- Supplier's Qualifications and Control --- p.78 / Chapter 7.3 --- Attractions of the Entry-fee Reverse Auction for Suppliers . . --- p.78 / Chapter 7.4 --- Procedural Fairness --- p.81 / Chapter 7.5 --- Total Cost Analysis --- p.81 / Chapter 7.6 --- Chapter Summary --- p.82 / Chapter 8 --- Concluding Remarks --- p.83 / Bibliography --- p.85 / Chapter A --- Order Statistics --- p.90 / Chapter B --- Conditional Order Statistics --- p.92 / Chapter C --- Virtual Marginal Cost of Procurement --- p.93

Zhang, Nianbing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 113-118). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Motivation --- p.2 / Chapter 1.3 --- The Research Scope and Main Results --- p.4 / Chapter 1.3.1 --- The Mechanism --- p.4 / Chapter 1.3.2 --- Statement of Problem --- p.8 / Chapter 1.3.3 --- Main Results --- p.11 / Chapter 1.4 --- Structure of Research --- p.13 / Chapter 2 --- Survey of the Literature --- p.15 / Chapter 2.1 --- Overview --- p.15 / Chapter 2.2 --- E-business --- p.16 / Chapter 2.3 --- Auction --- p.18 / Chapter 2.3.1 --- Introduction --- p.18 / Chapter 2.3.2 --- Auction Theory --- p.20 / Chapter 2.3.3 --- Online Auctions --- p.21 / Chapter 2.4 --- Combination of Auction and Other Transaction Methods --- p.24 / Chapter 2.4.1 --- Auction with Posted Price --- p.24 / Chapter 2.4.2 --- Auction with Negotiation Process --- p.26 / Chapter 2.4.3 --- Auction with Noncompetitive Contract --- p.28 / Chapter 3 --- Single Unit Noncompetitive Contract --- p.29 / Chapter 3.1 --- The Model --- p.29 / Chapter 3.2 --- Strategy of Suppliers in the Auction --- p.31 / Chapter 3.3 --- Strategy of Selected Suppliers --- p.33 / Chapter 3.3.1 --- Selected Supplier of No-Information Case --- p.33 / Chapter 3.3.2 --- Supplier in Information Case --- p.40 / Chapter 3.4 --- Buyer´ةs Consideration --- p.44 / Chapter 3.4.1 --- Buyer's Cost in No-Information Case --- p.44 / Chapter 3.4.2 --- Buyer's Cost in Information Case --- p.45 / Chapter 3.4.3 --- Comparison of Costs in the Two Information Scenarios --- p.47 / Chapter 4 --- Buyer´ةs Strategy: Lower Cost and More Flexibility for Suppliers --- p.49 / Chapter 4.1 --- Is the Noncompetitive Contract Necessary --- p.50 / Chapter 4.1.1 --- No-Information Case --- p.50 / Chapter 4.1.2 --- Information Case --- p.52 / Chapter 4.2 --- Flexibility of Re-Entrance to Auction --- p.53 / Chapter 4.2.1 --- No-Information Case --- p.54 / Chapter 4.2.2 --- Information Case --- p.58 / Chapter 4.3 --- Flexibility of Making Decision After the Auction --- p.65 / Chapter 4.3.1 --- No-Information Case --- p.66 / Chapter 4.3.2 --- Information Case --- p.69 / Chapter 5 --- Multiple Noncompetitive Contracts --- p.73 / Chapter 5.1 --- No-Information Case --- p.74 / Chapter 5.1.1 --- Suppliers's Strategy --- p.74 / Chapter 5.1.2 --- Buyer´ةs Consideration --- p.76 / Chapter 5.2 --- Information Case --- p.78 / Chapter 5.2.1 --- Supplier's Strategy --- p.78 / Chapter 5.2.2 --- Computation of Buyer's Cost --- p.81 / Chapter 5.2.3 --- Comparison with the Case of One Noncompetitive Contract --- p.85 / Chapter 5.2.4 --- The Effect of Supplier´ةs Information --- p.89 / Chapter 5.3 --- General Number of Noncompetitive Contract --- p.91 / Chapter 5.3.1 --- No-Information Case --- p.91 / Chapter 5.3.2 --- Information Case --- p.94 / Chapter 6 --- Alternative Forms of Contracts --- p.96 / Chapter 6.1 --- SBIB Model --- p.97 / Chapter 6.2 --- SNIB Model --- p.98 / Chapter 6.2.1 --- Supplier's Strategy --- p.98 / Chapter 6.2.2 --- Buyer´ةs Cost in No-Information Case --- p.100 / Chapter 6.2.3 --- Buyer's Cost in Information Case --- p.103 / Chapter 6.2.4 --- Multiple Units Offered --- p.105 / Chapter 7 --- Conclusion --- p.110