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研究生:葛智遠
研究生(外文):Chih-Yuan Ke
論文名稱:線上拍賣之研究:動態評價對超額出標的效果
論文名稱(外文):Study of On-line Auction: The Effect of Dynamic Evaluation on Over-Bidding
指導教授:蕭中強蕭中強引用關係
指導教授(外文):Chung-Chiang Hsiao
學位類別:碩士
校院名稱:國立清華大學
系所名稱:科技管理研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:51
中文關鍵詞:線上拍賣動態評價人工代理人基因演算法超額出標
外文關鍵詞:On-line AuctionDynamic EvaluationArtificial AgentGenetic AlgorithmOver-Bidding
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線上拍賣已經越來越盛行且廣泛地被大家所接受,因此有愈來愈多的人從事線上拍賣的機制設計,以促進線上拍賣的安全性與效率性。在線上拍賣中,我們會發現同樣的拍賣品重複的被拍賣,我們稱此種拍賣為重複拍賣,而重複拍賣中學習效果是一個非常重要的現象,另外,在動態的競標過程當中,超額出標卻是一個奇特的現象,這些現象是受到許多的因素所影響而造成的。在本研究中,吾人將探討三個參數,競標者的人數,重複拍賣的次數以及物品的市場價格作為控制變數,而其影響的結果平均報酬,超額出標的頻率和超額出標的比例。吾人利用人工代理人的機制,以基因演算法來模擬現實生活中的交易情況,加以觀察及分析拍賣的結果。吾人發現幾個有趣的現象:(1)當競標者數目增加的時候,競標者平均報酬會減少,而超額出標的頻率會增加,超額出標的比例會減少; (2)當物品市場價格增加的時候,競標者平均報酬會增加,而超額出標的頻率和比例都會減少; (3)當重複進行拍賣的次數增加的時候,競標者的平均報酬不固定,但是超額出標的頻率和比例都會增加。由於基因演算法裡面有擇優、交配和突變,讓競標者的策略更多樣化與學習效果的更全面化,因此讓吾人能在重複的線上拍賣中觀察到這些有趣的現象。
On-line auction becomes more and more popular now. Many people engage in developing the mechanism that makes on-line auction safety and effective. The learning effect is an important impression in the repeated on-line auction. The phenomenon over-bidding will be brought by the dynamic evaluation from the learning effect in the repeated on-line auction. There are many factors influencing the dynamic evaluation of the on-line auction. In this research, three parameters, number of bidders, number of blocks and market price of product are investigated to influence the results including average payoff over price, over-bidding frequency and over-bidding rate. We use artificial agent instead of the participators in the auction and simulation analysis with genetic algorithm to observe and analyze the results. We find some interesting trends in the repeated on-line auction. (1) When the number of bidders increases, the average payoff / price decreases, the over-bidding frequency increases, and the over-bidding rate decreases. It is due to the more number of bidders, the more competitive they are. (2) When the market price of product increases, the average payoff / price increases, the over-bidding frequency decreases, and the over-bidding rate decreases. It is due to facing of higher price product, bidders will be more cautious to bid. (3) When the number of blocks increases, the average payoff / price is uncertain, the over-bidding frequency increases, and the over-bidding rate increases. It is due to the learning effect of the bidders and the mutation of genetic algorithm. Thus it can be seen, the learning effect affect the auction result indeed.
摘要 3
ABSTRACT 4
1. INTRODUCTION 8
1.1 BACKGROUND 8
1.2 RESEARCH MOTIVATION 10
1.3 MAIN ISSUE 11
1.4 RESEARCH PURPOSE 12
1.5 STRUCTURE 13
2. LITERATURE REVIEW 14
2.1 INTERNET 14
2.1.1 Dynamic Pricing 14
2.1.2 Internet Exchange 15
2.2 AUCTION 16
2.2.1 Basic Auction Concepts 16
2.2.2 Private Value and Number of Bidders 17
2.3 INTERNET AUCTION 19
2.3.1 Last Minute Bidding 19
2.3.2 Reserve Price and Average Price of Product 20
2.3.3 Repeated Game and Round 20
2.4 GENETIC ALGORITHM 22
2.4.1 Adaptive Learning Effect 22
2.4.2 GA operators 22
3. RESEARCH MODEL 24
3.1 THE INTERNET AUCTION MODEL 24
3.2 PROXY-BID 27
3.3 PROPOSED MODEL 28
3.3.1 Repeated Game Auction 28
3.3.2 Strategy --- Finite automata 28
3.3.3 Example 29
4. RESEARCH METHODOLOGY 31
4.1 SIMULATION WITH ARTIFICIAL ADAPTIVE AGENT 31
4.2 THE GENETIC ALGORITHM OF BIDDING STRATEGY 33
5. RESEARCH RESULTS AND DISCUSSION 36
5.1 COMPARATIVE ANALYSIS 36
5.1.1 Number of Bidders 37
5.1.2 Market Price of the Product 38
5.1.3 Number of Blocks 39
5.2 SENSITIVE ANALYSIS 41
6. CONCLUSION AND SUGGESTION 43
6.1 CONCLUSION 43
6.2 CONTRIBUTION 45
6.2.1 To Academic 45
6.2.2 To Business 45
6.3 UNIQUENESS 47
6.4 LIMITATION 47
6.5 SUGGESTION 47
REFERENCE 49


TABLES
TABLE1 THE DEFAULT VALUE AND EXAMINING VALUE OF ENDOGENOUS VARIABLE 35
TABLE2 THE DEFAULT VALUE OF EXOGENOUS VARIABLE 35
TABLE 3 COMPARATIVE STATICS OF NUMBER OF BIDDERS IN THE AUCTION 38
TABLE 4 COMPARATIVE STATICS OF MARKET PRICE OF THE PRODUCT IN THE AUCTION 39
TABLE 5 COMPARATIVE STATICS OF NUMBER OF BLOCKS IN THE AUCTION 40
TABLE 6 SENSITIVE ANALYSIS OF DISCOUNT RATE IN THE MODEL 41
TABLE 7 SENSITIVE ANALYSIS OF CROSSOVER RATE IN THE MODEL 42
TABLE 8 SENSITIVE ANALYSIS OF MUTATE RATE IN THE MODEL 42
TABLE 9 THE COORDINATION OF THE CONCLUSION 44
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