PUMS99:1

UNIVERSITI MALAYSIA SABAH

BORANG PENGESAHAN STATUS TESIS

JUDUL: A COMPARATIVE STUDY FOR PARAMETER SELECTION IN ONLINE AUCTIONS

UAZAH: SARJANA SAINS

SAYA: GAN KIM SOON (HURUF BESAR)

SESI PENGAJIAN : =-'20"'-"0=6:...!:-2'-"'0=08"'----_ __ _

mengaku membenarkan tesis (LPSM/Sarjana/Doktor Falsafah) ini disimpan di Perpustakaan Universiti Malaysia Sabah dengan syarat-syarat kegunaan seperti berikut:-

1. Tesis adalah hakmilik Universiti Malaysia Sabah. 2. Perpustakaan Universiti Malaysia Sabah dibenarkan membuat salinan untuk

tujuan pengajian sahaja. 3. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan

pertukaran antara institusi pengajian tinggi. 4. Sila tandakan (/)

D SULIT

D TERHAD

~IDAK TERHAD

(Mengandungi maklumat yang berdarjah keselamatan atau Kepentingan Malaysia seperti yang termaktub di dalam

AKTA RAHSIA RASMI 1972)

(Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)

(TANDATANGAN PENULIS)

Alamat Tetap: No 54, LRG CP3/13 TMN Cheras Perdana, 43200, Selangor Darul Ehsan, Malaysia

Tarikh: ,;)~ 11:>+ (~b 10 Tarikh: ______ _

CATATAN: - * Potong yang tidak berkenaan. ** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi

berkenaan dengan nyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai sulit dan terhad.

@Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana secara penyelidikan atau disertai bagi pengajian secara kerja kursus dan Laporan Projek Sarjana Muda LPSM

A COMPARATIVE STUDY FOR PARAMETER

SELECTION IN ONLINE AUCTIONS

GAN KIM SOON

PERPUSTAKAAN fJlMRSITI MALAYSIA SABAH

THESIS SUBMITTED IN FULFILLMENT FOR

THE DEGREE OF MASTER OF SCIENCE

SCHOOL OF ENGINEERING AND

INFORMATION TECHNOLOGY

UNIVERSITI MALAYSIA SABAH

2009

DECLARATION

I hereby declare that the material in this thesis is my own except for quotations, excerpts, equations, summaries and references, which have been duly acknowledged.

10 December 2009

ii

~~~£~ Gan Kim Soon PK2006-B040

CERTIFICATION

NAME GAN KIM SOON

MATRIC NO. PK2006-8040

llLE A COMPARATIVE STUDY FOR PARAMETER SELECTION IN ONLINE AUCTIONS

DEGREE Master of Science (Computer Sciences)

VIVA DATE 10JUN 2009

DECLARED BY

1. SUPERVISOR Assoc. Prof. Dr. Patricia Anthony

2. CO-SUPERVISOR Dr. Jason Teo Tze Wi

~ ¥~nature 0~

iii

UNIVERSITI MALAYSIA SABAH

BORANG PENGESAHAN STATUS TESIS

JUDUL: A COMPARATIVE STUDY FOR PARAMETER SELECTION IN ONLINE AUCTIONS

IJAZAH: SARJANA SAINS (KOMPUTER SAINS)

SESI PENGAJIAN: 2006-2008

Saya, GAN KIM SOON mengaku bahawa membenarkan tesis sarjana ini disimpan di perpustakaan Universiti Malaysia Sabah dengan syarat-syarat kegunaan seperti berikut:

1. Tesis adalah hak milik Universiti Malaysia Sabah. 2. Perpustakaan Universiti Malaysia Sabah dibenarkan membuat salinan untuk tujuan

pengajian sahaja. 3. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara

intstitusi pengajian tinggi. 4. TIDAK TERHAD

Disahkan oleh

Penulis: GAN KIM SOON TANDATANGAN PUSTAKAWAN

Alamat: SEKOLAH KEJURUTERAAN DAN TEKNOLOGI MAKLUMAT Universiti Malaysia Sabah, Locked Bag No. 2073, 88999, Kota Kinabalu, Sabah, Malaysia

Tarikh 2010

\OJ Penyelia bersama: Dr. Jason Teo

ACKNOWLEDGMENTS

Firstly, I would like to express my sincere gratitude to the Vice-Chancellor of Universiti Malaysia Sabah, Col. Prof. Datuk Dr. Kamaruzaman Hj. Ampon for his permission to carry out this research in Universiti Malaysia Sabah.

I would like to express my thanks to the the Dean of School of Engineering and Information Technology, Assoc. Prof. Dr. Rosalam Sarbartly for providing support during my research work.

I would like to take this opportunity to thank my supervisor Assoc. Prof. Dr. Patricia Anthony for accompanying me on the research journey by providing me

precious suggestions and for constantly encouraging me. I would like to express my gratitude to my co-supervisor, Dr. Jason Teo for the

help and guidance that I received from him.

My appreciation goes to the supporting staff of SEIT, lab assistants in Block B that

have helped me along the way. I would also like to show my appreciation to the Universiti Malaysia Sabah for

supporting this Master's program and for giving me the scholarship for my M.Sc. Lastly, I would like to thank everyone who helped me from the beginning until the

final phase of my research work.

iv

ABSTRACT

A COMPARATIVE STUDY FOR PARAMETER SELECTION IN ONLINE AUCTIONS

In this information-rich age, online auctions have become an important procurement tool in either commercial or personal use. As the number of auctions increases, the process of monitoring, tracking bid and bidding in multiple auctions become a problem. The user needs to monitor many auctions sites, picks the right auction to participate, and makes the right bid in making sure that the desired item satisfies the user's preference. All these tasks are somewhat complex and time consuming. The task even gets more complicated when there are different start and end times and when the auctions employ different protocols. Due to the complex and dynamic nature of the online auction, one of the strategies employed is using genetiC algorithm to discover the best strategy. Hence, this work attempts to improve an existing bidding strategy by taking into accounts the evolution of various model of genetiC algorithm in optimizing the parameter of the bidding strategies. In this work, three different models of genetic algorithms are considered. In the first model, the crossover and the mutation rate of the genetic algorithms are varied in order to create different combination of crossover and mutation rate. The new combination of genetic probabilities from this investigation will eventually perform better than the recommended genetiC probabilities adopted in the previous work. The second model is the dynamic adaptation model namely the dynamic deterministic adaptive model. The bidding strategy from the experimental result of this experiment will eventually perform better than the bidding strategy that applied fixed static genetic operator's probabilities. Self­adaptation genetic algorithm is the last model that will be used to evolve the bidding strategy. The bidding strategies applying self-adaptation model are expected to perform better than the deterministic dynamic adaptation because of the nature of the algorithm itself. The evaluations are conducted in a simulated online auction framework with multiple auctions running concurrently. The effectiveness of the bidding strategies is measured based on the average fitness of the individuals, the success rate and average payoff in obtaining the item in the auctions. The performance of these bidding strategies will be empirically demonstrated in this thesis.

v

A BSTRAK

Da/am era tekn%gi mak/umat maju kini, /e/ong da/am ta/ian te/ah menjadi yang satu cara pembe/ian yang penting sama ada untuk komersia/ atau kegunaan peribadi. Disebabkan jum/ah transaksi /e/ong yang , kian meningkaC proses pengawasan, penjejakan bida dan proses pembidaan da/am pe/bagai le/ong menjadi satu masa/ah. Pengguna perlu memantau banyak laman-/aman /e/ong, memilih /e/ong yang berpotensi untuk disertai, dan membida da/am le/ong yang dapat memenuhi permintaan pengguna. Semua tugas-tugas tersebut adalah agak kompleks dan memakan masa. Tugas ini akan menjadi /ebih kompleks apabi/a pe/bagai /e/ong mempunyai perbezaan da/am masa permu/aan dan masa tamat serta mengamalkan protokol berlainan. O/eh sebab sifat dinamik dan kompleks /e/ong ta/ian, salah satu strategi adalah menggunakan a/goritma genetik untuk mempero/ehi strategi terbaik. Justeru, projek ini ada/ah untuk meningkatkan strategi pembidaan yang sedia ada dengan mengambJ! kira kepe/bagaian model evo/usi algoritma genetik. Da/am projek ini, tiga model algoritma genetik diambi/ kira. Da/am model pertama, berbagai-bagai kadar penyilangan dan mutasi a/goritma genetik dieksperimentasikan untuk mempero/ehi pe/bagai gabungan kadar penyi/angan dan mutasi serta bagi memilih gabungan terbaik yang bo/eh menjana keputusan terbaik. Combinasi baru bagi kadar penYI'langan dan mutasi dijangka yang dipero/ehi daripada eksperiment ini dijangka akan menjana keputusan yang /ebih daripada combinasi kadar penyi/angan and mutasi yang lama. Mode/ kedua adalah model adaptasi dinamik iaitu model penentuan adaptasi dinamik. Strategi pembidaan daripada keputusan eksperiment ini dijangka akan menjana keputusan yang lebih baik daripada strategi pembidaan yang mengap/ikasikan kadar penyi/angan and mutasi yang tetap. Adaptasi diri a/goritma genetik merupakan model terakhir yang digunakan untuk mengevo/usikan strategi­strategi pembidaan. Strategi pembidaan yang mengap/ikasi adaptasi diri ada/ah dijangka akan menjana keputusan yang /ebih baik daripada strategi pembidaan ynag mengaplikasikan adaptasi dinamik disebabkan o/eh sifat a/gorithma sendirt: Kajian dikenda/ikan da/am simu/asi /e/ong ta/ian yang mempunyai pe/bagai le/ong yang dija/ankan serentak. Keberkesanan strategi-strategi pembidaan adalah diukur berdasarkan kepada purata kesesuaian individu, kadar kejayaan dan purata keuntungan da/am memenangi item da/am /e/ong. Prestasi strategi pembidaan ini akan didemonstrasi secara empirika/ da/am tesis in!:

vi

CONTENTS

Page

TITLE

DECLARATION ii

CERTIFICATION iii

ACKNOWLEDGMENTS iv

ABSTRACT v

ABSTRAK vi

LIST OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xii

LIST OF ABBREVIATION xvi

LIST OF SYMBOLS xvii

CHAPTER 1: INTRODUCTION 1.1 Introduction 1 1.2 What is Online Auction? 3 1.3 Problem Background 6 1.4 Genetic Algorithms 8 1.5 Objectives and Scope 10 1.6 Thesis Contributions 11 1.7 Thesis Structure 12

CHAPTER 2: LITERATURE REVIEW 2.1 Introduction 14 2.2 The Space of Auction 14 2.3 Bidding Strategy Model 17

2.3.1 Dominant Strategy 19 2.4 Evolutionary Algorithm 19

2.4.1 Adaptation 21 2.4.2 Genetic Algorithm 23 2.4.3 Adaptive Genetic Algorithm 27 2.4.4 Self-Adaptive Genetic Algorithm 28

2.5 Evolving Bidding Strategies 30 2.5.1 Evolutionary Approach for Studying Heterogeneous

Strategies 30 2.5.2 ZIP (Zero Intelligence Plus) 32

2.6 Bidding Agents for Multiple Heterogeneous Online Auctions 33 2.6.1 Electronic Marketplace Simulation 33 2.6.2 English Auction 36 2.6.3 Dutch Auction 37 2.6.4 Vickrey Auction 38 2.6.5 The Marketplace 38

vii

2.6.6 Bidding Strategy 39 2.6.7 Genetic Algorithm 47

2.7 Summary 54

CHAPTER 3: PARAMETER TUNING 3.1 Introduction 57 3.2 Parameter Tuning 58 3.3 Varying Crossover Rate Experiment 59

3.3.1 Experimental Setup for Varying Crossover Rate 59 3.3.2 Experimental Evaluation for Varying Crossover Rate 61 3.3.3 Experimental Result and Discussion for Varying 62

Crossover Rate 3.4 Varying Mutation Rate Experiment 68

3.4.1 Experimental Setup for Varying Mutation Rate 68 3.4.2 Experimental Evaluation for Varying Mutation Rate 69 3.4.3 Experimental Result and Discussion for Varying

Mutation Rate 69 3.5 Varying Mutation Rate (0.4 Crossover Rate) Experiment 72

3.5.1 Experimental Setup for Varying Mutation Rate (0.4 Crossover Rate) 72

3.5.2 Experimental Evaluation for Varying Mutation Rate (0.4 Crossover Rate) 73

3.5.2 Experimental Result and Discussion for Varying Mutation Rate 74

3.6 Summary 77

CHAPTER 4: DETERMINISTIC DYNAMIC ADAPTATION 4.1 Introduction 82 4.2 Deterministic Dynamic Adaptation by Varying One Genetic 83

Operator Probability 4.2.1 Experimental Setup for Deterministic Dynamic

Adaptation by Varying One Genetic Operator Probability 84

4.2.2 Experimental Evaluations for Deterministic Dynamic Adaptation by Varying One Genetic Operator Probability 86

4.2.3 Experimental Result and Discussion for Deterministic Dynamic Adaptation by Varying One Genetic Operator Probability 87

4.3 Deterministic Dynamic Adaptation by Varying Two Genetic 92 Operators Probability 4.3.1 Experimental Setup for Deterministic Dynamic

Adaptation by Varying Two Genetic Operator Probability 93

4.3.2 Experimental Evaluations for Deterministic Dynamic Adaptation by Varying Two Genetic Operator Probability 94

viii

4.3.3 Experimental Result and Discussion for Deterministic Dynamic Adaptation by Varying Two Genetic Operator Probability 95

4.4 Summary 98

CHAPTER 5: SELF-ADAPTATION 5.1 Introduction 102 5.2 Self-Adaptation 102 5.3 Experimental Setup for Self-Adaptation 104 5.4 Experimental Evaluation for Self-Adaptation 107 5.5 Experimental Result and Discussion for Self-Adaptation 107 5.6 Summary 111

CHAPTER 6: CONCLUSION 6.1 Conclusion 112 6.2 Future Work 119

REFERENCES 122 APPENDIX 134

ix

LIST OF TABLES

Page Table 1.1 Comparison between traditional auction and online auction 4

Table 2.1 The characteristic of different types of auctions 17

Table 2.2 The classification of adaptation in EAs 22

Table 2.3 The bidding strategies representation 49

Table 2.4 The mutation range of parameter values 53

Table 3.1 Crossover value for testing 60

Table 3.2 Genetic algorithm evolutionary setting 60

Table 3.3 Genetic algorithm parameter setting 60

Table 3.4 Configurable parameters for simulated marketplace 61

Table 3.5 P value of the t-test statistical analysis for varying crossover 63 rate experiment

Table 3.6 Mutation value for testing with 0.6 crossover rate 68

Table 3.7 Parameter setting for mutation testing with 0.6 crossover 69 rate

Table 3.8 Mutation values for testing with 0.4 crossover rate 73

Table 3.9 Parameter setting for mutation testing with 0.4 crossover 73 rate

Table 3.10 P value of the t-test statistical analysis for varying mutation 74 rate experiment

Table 3.11 P value of the t-test statistical analysis for success rate 75

Table 3.12 P value of the t-test statistical analysis for average payoff 75

Table 3.13 P value of the t-test statistical analysis for comparison 78 between newly discovered genetic operator probabilities with the old set of genetic operator probabilities

Table 4.1 Deterministic dynamic adaptation by varying one genetic 84 operators testing sets

x

Table 4.2 Deterministic dynamic adaptation by varying one genetic 85 operator parameter setting

Table 4.3 P value for success rate for in deterministic dynamic 90 adaptation by varying one genetic operator probability

Table 4.4 P value for average payoff for in deterministic dynamic 92 varying one genetic operator probability

Table 4.5 Deterministic dynamic adaptation by varying two genetic 93 operators testing set testing sets

Table 4.6 Deterministic dynamic adaptation by varying two genetic 94 operators parameter setting

Table 5.1 Self-adaptive testing sets 105

Table 5.2 Self-adaptation genetic algorithm parameter setting 106

Table 6.1 P value for the comparison between different disciplines in 118 term of success rate and average payoff

xi

LIST OF FIGURES

Page Figure 2.1 A classification of classic auction types 15

Figure 2.2 Pseudocode for the Evolutionary Algorithm 20

Figure 2.3 Divisions of Evolutionary Algorithms 20

Figure 2.4 The pseudocode for the Genetic Algorithm 25

Figure 2.5 The marketplace simulator 35

Figure 2.6 The bidding agent algorithm 39

Figure 2.7 Building active auction list algorithm 40

Figure 2.8 The curve with varying ~ values 42

Figure 2.9 Procedure for selecting potential auction 45

Figure 2.10 Various combinations of the bidding constraints 46

Figure 2.11 The crossover mechanism 52

Figure 2.12 The mutation mechanism 53

Figure 3.1 Genetic algorithm 61

Figure 3.2 Average fitness of population with varying crossover rates. 64

Figure 3.3 The upper bound and lower bound of the average fitness of 65 the various crossover rates

Figure 3.4 The success rate for strategies evolved with varying 66 crossover rates

Figure 3.5 The average payoff for strategies evolved with varying 67 crossover rates

Figure 3.6 The average fitness of population with varying mutation rates 70 with 0.6 crossover rate.

xii

Figure 3.7 The success rate for strategies evolved with varying mutation 71 rates with 0.6 crossover rate.

Figure 3.8 The average payoff for strategies evolved with varying 71 mutation rates with 0.6 crossover rate

Figure 3.9 The average fitness of population with varying mutation rates 75 with crossover rate of 0.4.

Figure 3.10 The success rate for strategies evolved with varying mutation 76 rates with crossover rate of 0.4

Figure 3.11 The average payoff for strategies evolved with varying 76 mutation rates with crossover rate of 0.4

Figure 3.12 Comparing the average fitness of population between 0.02 77 with 0.01 and 0.1 mutation rates with 0.4 crossover rate.

Figure 3.13 The comparison of average fitness between benchmark and 79 the newly discovered rate.

Figure 3.14 The success rate for strategies evolved with benchmark and 79 newly discovered rate

Figure 3.15 The average payoff for strategies evolved with benchmark 80 and newly discover rate

Figure 4.1 Deterministic dynamic adaptation by varying one genetic 86 operator

Figure 4.2 Average fitness for deterministic dynamic adaptation by 88 varying one genetic operator probability

Figure 4.3 Success rate for strategies evolved with deterministic 89 dynamic adaptation by varying one genetic operator probability

Figure 4.4 Average payoff for strategies evolved with deterministic 90 dynamic adaptation by varying one genetic operator probability

xiii

Figure 4.5 Deterministic dynamic adaptation by varying two genetic 94 operators

Figure 4.6 Average fitness for deterministic dynamic adaptation by 96 varying two genetic operator probabilities

Figure 4.7 Success rate for strategies evolved with deterministic 96 dynamic adaptation by varying two genetic operator probabilities

Figure 4.8 Average payoff for strategies evolved with deterministic 97 dynamic adaptation by varying two genetic operator probabilities.

Figure 4.9 Comparisons between the average fitness of CFMF, CFMD, 99 and CDM!

Figure 4.10 Success rate comparison between CFMF, CFMD and CDM! 99

Figure 4.11 Average payoff comparison between CFMF, CFMD and CDM! 100

Figure 5.1 Encoding of a bidding strategy for self-adaptive crossover 105 rate

Figure 5.2

Figure 5.3

Figure 5.4

Figure 5.5

Figure 5.6

Figure 5.7

Figure 5.8

Figure 6.1

Encoding of a bidding strategy for self-adaptive mutation rate 105

Encoding of a bidding strategy for self-adaptive crossover 105 and mutation rate

The self adaptive algorithm both genetic operators 106

The self-adaptive algorithm for one genetic operator 107

Average fitness for different self-adaptation schemes 109

Success rate for strategies evolved from different 109 self-adaptation schemes Average payoff for strategies evolved from different 110 self-adaptation schemes

Average fitness population with different genetic algorithm 115 disciplines

xiv

Figure 6.2 Success rate for strategies evolved from different genetic 116 algorithm disciplines

Figure 6.3 Average payoff for strategies evolved from different genetic 117 algorithm disciplines

xv

LIST OF ABBREVIATIONS

EA Evolutionary Algorithms

E-Commerce Electronic Commerce

GA

CFMD

CFMI

CIMF

CDMF

CDMI

CDMD

CIMD

CIMI

SACM

SAC

SAM

Genetic Algorithm

Deterministic Decrease Mutation Rate

Deterministic Increase Mutation Rate

Deterministic Increase Crossover Rate

Deterministic Decrease Crossover Rate

Deterministic Decrease Crossover Rate with Deterministic Increase

Mutation Rate

Deterministic Decrease Mutation Rate with Deterministic Decrease

Crossover Rate

Deterministic Increase Crossover Rate with Deterministic Decrease

Mutation Rate

Deterministic Increase Crossover Rate with Deterministic Increase

Mutation Rate

Self Adaptive Crossover and Mutation

Self Adaptive Crossover

Self Adaptive Mutation

xvi

>

<

=

E

171

Pc

Pm

Pr

L{t)

E{t)

D{t)

V{t)

LIST OF SYMBOLS

Greater than

Less than

Greater than or equal to

Less than or equal to

Sum over

Equal to

Is an element of

Agent's bid increment value

Auction starting time for auction i

Auction ending time for auction i

Auction status for auction i

Crossover rate

Mutation rate

Private valuation

Wining for auction i

Set of active auctions

Set of active English auctions

Set of active Dutch auctions

Set of active Vickrey auctions

The current bid value for remaining time tactic

The current bid value for remaining auction tactic

The current bid value for user's desire of bargain tactic

The current bid value for user's level of desperateness tactic

xvii

firl

W rl

8

Constant that determines the value of the starting bid for remaining time tactic

Constant that determines the value of the starting bid for remaining auction tactic

Constant that determines the value of the starting bid for user's desire of bargain tactic

Constant that determines the value of the starting bid for user's level of desperateness tactic

The rate of concession to PI' for remaining time tactic

The rate of concession to PI' for remaining auction tactic

The rate of concession to PI' for user's desire of bargain tactic

The rate of concession to PI' for user's level of desperateness tactic

The relative weight for the remaining time tactic

The relative weight for the remaining auction tactic

The relative weight for the user's desire of bargain tactic

The relative weight for the user's level of desperateness tactic

P value is more than 0.05, which means no significant improvement

P value is less than 0.05, which means significant improvement

xviii

CHAPTER 1

INTRODUCTION

1.1 Introduction

Auction is defined as a bidding mechanism and is expressed by a set of auction

rules that specify how the winner is determined and how much he or she has to

pay (Wolfstetter, 2002). It has been used widely since 500 B.C whereby auctions

were used by the ancient people to allocate scarce resources in Babylon (Shubik,

1983). The community was using auction to bid for their prospective wives and

these bidding systems are still practiced in some of the places in Egypt. Moreover,

the ancient Rome has been practicing auctions for commercial trading to liquidate

property and to sell off leftover spoils of war at the battlefield. Since then, auctions

have been practiced widely in the human civilizations where they were used to

liquidate goods and to sell off the unsaleble goods. Throughout the years, auction

has gained its popularity due to its effectiveness in allocating resources by the

individuals who will value them the most (Reynolds, 1996). This effectiveness has

brought about many variants of auctions, particularly the last few years (Wuman et

al.2001).

The traditional single-sided auctions can mainly be classified into four

different types as follows (Klemperer, 1999).

a) The ascending-bid auction (also called the open, oral, or English auction)

b) The descending-bid auction (also called Dutch auction)

c) The first-price sealed bid auction

d) The second-price sealed bid auction (also called Vickrey auction (Vickrey,

1961))

English auction is the most common auction. In this type of auction, the

auctioneer will start the auction with a low price which will then be successively

raised up until only one bidder is remains. The remaining bidder will be the

winning bidder and thus, he or she will have to pay for the value of the item which

is equivalent to the bid value. This type of auction is executed in three different

ways; by having the seller to announce the price, by having the bidders to call out

the price themselves, or by having bids submitted electronically with the best latest

bid posted at each stage of the auction. The bidders will have the chance to

observe the latest high price posting while deciding either to continue bidding or to

quit at any stage of the bidding. Once the bidder has decided to quit, he or she will

not be allowed to rejoin the auction again. This type of auction can be commonly

found in antiques, artworks and bidding auction house.

The descending bidding auction is the opposite of the ascending bidding

auction. In a descending bidding auction, the auctioneer normally starts with a

relatively high price and progressively lowers the price until a bidder calls to claim

for the item. The winning bidder will be the first bidder who calls out for the item

at the current price stated. This type of auction is known as the Dutch auction and

is commonly used in Netherlands for selling flowers (van Heck & Ribbers, 1997).

Similar auction is also used to buy and sell fish and tobaccos in many countries

such as Spain, Israel and Canada (Klemperer, 1999).

The remaining two types of the auctions are called the sealed bid auctions.

In sealed bid auctions, each bidder will submit their bids independently without

knowing what the others' bid values are. The bids are opened when the auction is

closed and the winner will be decided. In the first-price sealed bid auction, the

winner will be the bidder with the highest bid and he or she will pay for a price

equivalent to his bid value for the item. In contrast, the winner will only have to

pay the price that is equivalent to the second highest bid instead of the highest bid

in the second-price sealed bid auction. First-price sealed bid auctions are normally

used in auctioning mineral rights in government owned land and also used

sometimes in the sales of artworks and real estates (Klemperer, 1999) whereas the

second-price sealed bid auctions are used for auctioning stamps, autographs and

Civil War memorabilia by mail (Lucking-Reiley, 2000b; Rothkopf et al. 1990).

2

1.2 What is Online Auction?

Jansen defines an online auction as an Internet-based version of a traditional

auction (Jansen, 2003). The advancement of the internet technology has brought a

new method of trading, namely, the e-commerce. Any business transaction

(buying and selling process) whose price or essential terms are negotiated over an

online system such as the Internet, Extranet or Electronic Data Interchange

network is called the E-commerce (or electronic commerce). In today's e­

commerce market, online auction has acted as an important tool in the services for

procuring goods and items either for commercialize purposed or for personal used.

Online auctions have been reported as one of the most popular and effective ways

of trading goods over the Internet (8apna et al. 2001). Electronic devices, books,

computer software, and hardware are among the thousands items sold in the

online auctions every day. To date, there are 2603 auction houses that conduct

online auctions as listed on the Internet (Internet Auction List, 2008). These

auction houses conduct different types of auctions according to a variety of rules

and protocols. eBay, as one of the largest auction house alone has more than

338.2 million registered users and had transacted more than USD15.68 billion

worth of goods during the second quarter of 2008 (e8ay, 2008). These figures

clearly show the importance of online auctions as an essential method for

procuring goods in today's e-commerce market.

The major difference between the traditional auction and online auction is

the flexibility in conducting the auction. There are many limitations in a traditional

auction setting. With the aid of online auction, many constraints that used to be in

the traditional auction have now been diminished. Table 1.1 shows some of the

differences between traditional auction and online auction.

3

Table 1.1: Comparison between traditional auction and online auction

Traditional Auction Online Auction

The auctioneer and the bidders have to The users just need to be in front of a

gather in one room at a given time to personal computer with an internet

decide who gets the item and at what connection to participate in an online

price. auction that may be located in another

part of the world (Lucking-Reiley,

2000a).

Auctioneers and bidders are required to Online auction increases flexibility and

come to the auction's venue. This ease the participation in auction for

practice limits many of the potential users, thus allowing the users to

bidders that cannot attend the auction. participate in an auction wherever they

are and whenever they want.

Traditional auctions normally sell an The duration for online auctions lasts

item within a few minutes or even longer than traditional auctions, it

seconds. The rapid process with only normally lasts for days and weeks, and

limited time for the auction participants this allows the bidders to have more

to make decision may cause many of time to think and to decide when to

them to pull out from bidding for the submit their bids.

item in the auction. As a consequence,

the sellers may not get the highest

possible price for their goods (Turban et

al.2000).

The goods to be auctioned may also Online auctions allow sellers to sell their

cause problems in traditional auction goods effiCiently with little action or

because of the difficulty in transferring effort required.

them to the auction site.

A large cost is associated with operating In online auction, seller will only be

the traditional auction since the sellers required to set up a seller's account by

have to rent the auction site while the filling up a seller's form detailing the

4

REFERENCES

Aguirre, H. E. and Tanaka, K. 2002. Parallel Varying Mutation in Deterministic and Self-Adaptive Gas. Proceedings of the 7th International Conference on Parallel Problem Solving from Nature. London: Springer-Verlag.

Alvarez, G. 2002. Can we make genetic algorithms work in high-dimensionality problems? Stanford Exploration Project (SEP) report 112.

Angeline, P. J. 1995. Adaptive and Self-Adaptive Evolutionary Computation. In Palaniswami, M., Attikiouzel, Y., Marks, R. J., Fogel, D., & Fukuda, T. (eds.). A Dynamic System Perspective, pp. 264-270. New York: IEEE Press.

Anthony, P., V. D. Dang, W. Hall, and Jennings, N. R. 2001. Autonomous Agents for Participating in Multiple Online Auctions. Proceedings of the DCAI Workshop on E-Business and Intelligent Web, pp. 54-64. Washington: ACM.

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