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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/34149完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 巫和懋 | |
| dc.contributor.author | Li-Han Chen | en |
| dc.contributor.author | 陳立漢 | zh_TW |
| dc.date.accessioned | 2021-06-13T05:56:01Z | - |
| dc.date.available | 2006-07-14 | |
| dc.date.copyright | 2006-07-14 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-06-29 | |
| dc.identifier.citation | 1. Arthur, W. B., J. Holland, B. LeBaron, R. Palmer, and P. Tayler (1997). “Asset pricing under endogenous expectations in an artificial stock market model”, In Arthur, W. B., Durlauf, S. N., and Lane, D. A., eds., The economy as an evolving complex system II (pp. 15–44). Proceedings Vol. XXVII, SFI Studies in the Sciences of Complexity. Reading, MA: Addison-Wesley
2. Axelrod, R. (1997). “The complexity of cooperation: Agent-based models of conflict and cooperation”, Princeton, N.J.: The Princeton University Press 3. Brandenburger, A., B. Nalebuff (2004). “Co-opetition”, Currency Doubleday 4. Challet, D., M. Marsili, Y.-C. Zhang (2004). “Minority Games: interacting agents in financial markets”, Oxford University Press 5. Cont R., J. P. Bouchaud (2000). “Herd behavior and aggregate fluctuations in financial markets”, Macroeconomic Dynamics 4 (2), 170-196 6. Economides, N. (1996). 'The Economics of Networks', International Journal of Industrial Organization, vol. 14, no. 2, page 673-699 7. Farmer, J.D. (1999). “Physicists Attempt to Scale the Ivory Towers of Finance”, Computing in Science and Engineering (IEEE), November –December 1999, 26-39 8. Frels, J. K., J. A. Reggia, and D. Heisler. (20005). “Standard-Scape: An Agent-based Model of Competition in Markets with Network Externalities”, MSI report 05-105 9. Gribbin, J.R. (2005). “Deep Simplicity: Bringing Order to Chaos and Complexity”. Random House 10. Katz, M., and C. Shapiro (1985). “Network Externalities, Competition, and Compatibility”, American Economic Review, 75, page 424-440 11. Katz, M., C. Shapiro (1986). “Technology Adoption in the Presence of Network Externalities”, Journal of Political Economy, 94, page 822-841 12. LeBaron, B. (2000). “Agent-based computational finance: Suggested readings and early research”, Journal of Economic Dynamics and Control 24, 679–702 13. LeBaron, B. (2002), “Building the Santa Fe Artificial Stock Market,” Working Paper, Brandeis University 14. Levy, H., M. Levy, S. Solomon (2000). “Microscopic Simulation of Financial Markets”, Academic Press, New York 15. López-Sánchez, M., X. Noria, J. Rodríguez, N. Gilbert (2005). “Multi-Agent Based Simulation of News Digital Markets”, International Journal of Computer Science & Applications 16. McAfee, R. (2002). “Competitive solutions: the strategist’s toolkit”, Princeton University Press 17. Orme, B. (2006) “Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research”, Madison, Wis.: Research Publishers LLC 18. Palmer, R., W. B. Arthur, J. Holland, B. LeBaron, and P. Tayler (1994), “Artificial economic life: A simple model of a stock market”, Physica D 75, 264–274 19. Shapiro, C., H. Varian (1999). “Information Rules: A Strategic Guide to the Network Economy”, Harvard Business School Press 20. Stauffer, D. (2001). “Percolation models of financial market dynamics”, Adv. Complex Syst, 4, 19 21. Stauffer, D., S. Moss de Oliveira, P.M.C. de Oliveira, J.S. Sa Martins, (2006). “Biology, Sociology, Geology by Computational Physicists”, Elsevier, Amsterdam 22. Tesfatsion, L. (2002), “Agent-based computational economics: Growing economies from the bottom up”, Artificial Life 8:55-82 23. Tesfatsion, L. and K. L. Judd (2006), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier 24. Tesfatsion, L. (2006). 'Agent-Based Computational Economics: A Constructive Approach to Economic Theory', Chapter 1 in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam 25. Waldrop, M.M. (1992). “Complexity: The Emerging Science at the Edge of Order and Chaos “, Simon and Schuster, New York | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/34149 | - |
| dc.description.abstract | 網路外部性商品(如線上遊戲)的定價策略並不是多數人想像的簡單. 我們知道, 提高商品的價格會造成買方人數的減少, 相反的, 降低商品的價格則可以提昇買方的購買意願. 但是要能定量的給出網路商品的最佳價格策略並非容易的事, 最主要的困難是網路外部性商品牽連著複雜的人與人的互動關系與行為. 為了嘗試解決這個網路商品定價的難題,我們建構了一個電腦模擬系統來分析不同的價格策略會對線上遊戲的經營利潤產生如何的影響. 此系統是用代理者模擬技術(agent-based simulation technique)所建構而成.玩家的行為資料是從目標玩家所回答的問卷匯整而成.在特定的實驗範圍內,我們初步的研究提供了以下的結果. 1, 線上遊戲的價格策略有最適解. 2, 線上遊戲的變動服務成本對其價格策略的選擇有重要的影響. 3,當遊戲的吸引程度到達一定的水準時,沒有必要再做任何的廣告或促銷活動. 4,增加玩家的朋友圈及持續的提供新的遊戲包,都可以增加遊戲的生命週期,因而產生更多的利潤. 5,如果只有一個競爭者,在某些條件下,競爭對手只會降低彼此的市佔率,比較弱的一方不會被逼出市場. 6, 當競爭激烈(對手超過兩人)時,較弱的產品很容易被擠出市場. 7,我們還碰到有趣的混沌現像,當有三個非常相似的產品在市場上競爭,最後的結果是無法預測的. | zh_TW |
| dc.description.abstract | Pricing strategy for strong network product such as online games is difficult. Qualitatively, we all know if we increase the product’s price, there will be fewer buyers, and vice versa; however, to quantitatively set the optimal price for online games is not easy - the complexity arises from the network interactions between players and complicated human behaviors. In order to solve this pricing difficulty, we have constructed a simulation framework to analyze the impacts of different pricing strategies for online games under various scenarios. This simulation framework is based on agent-based simulation technique, and the behavioral attributes of player agents are obtained by conducting the survey from the target consumers. We have demonstrated that, under certain simplifications and assumptions, our experimental approach provides some interesting results on the pricing strategy issues for online games. 1. The optimal price always exists. 2. Choice of pricing scheme is heavily influenced by service variable cost. 3. While reaching certain level of product interest, there is no need to spend more money on advertising. 4. Increasing social network size or introducing expansion pack helps to extend the product life cycle and improves profit. 5. Within certain simulation scope, the competition only partitions the market share, and the stronger product does not overpower the weaker one. 6. As competition intensifies, the stronger products will easily crash the weaker product. Finally, we have encountered interesting Chaos-like behavior in our simulations – small differences in the earlier process can cause dramatically different outcomes. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T05:56:01Z (GMT). No. of bitstreams: 1 ntu-95-P92746029-1.pdf: 732455 bytes, checksum: 1af9750ea413a351b05e8eecd9c287dc (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | Index
THESIS ABSTRACT V INDEX....... VI Chapter 1. Introduction 1 1.1. BACKGROUND 1 1.2. MOTIVATION 1 1.3. CONVENTIONAL APPROACH 2 1.4. NEW APPROACH (AGENT-BASED MODELING) 3 1.5. LIMITATIONS 3 Chapter 2. Agent-Based Modeling 5 2.1. INTRODUCTION TO AGENT-BASED MODELING 5 2.2. EXAMPLE: ISING MODEL 7 2.3. AGENT-BASED SIMULATION PROCESS (ISING MODEL EXAMPLE) 10 2.4. OTHER AGENT-BASED COMPUTATIONAL APPLICATIONS 11 2.5. CHAPTER SUMMARY 14 Chapter 3. Taiwan’s online game market 15 3.1. MARKET TREND 15 3.2. TARGET AUDIENCE 16 3.3. MAJOR PUBLISHERS 17 3.4. MAJOR GAMES 18 3.5. ONLINE GAME VALUE DISTRIBUTION 19 3.6. PRICING MODELS 20 3.7. CHAPTER SUMMARY 21 Chapter 4. Model Construction and Simulation Setup 22 4.1. SIMULATION FRAMEWORK DIAGRAM 22 4.2. SIMULATION PROCESS (PSEUDO CODE) 22 4.3. INPUTS 24 4.3.1. CONSUMER BEHAVIOR ATTRIBUTES 24 4.3.2. PRODUCT ATTRIBUTES 26 4.3.3. MARKET ATTRIBUTES 26 4.4. UI AND OUTPUTS 27 Chapter 5. Behavioral Attributes of Gamers 30 5.1. SURVEY DATA DESIGN 30 5.2. SAMPLING ISSUE 31 5.3. SURVEY RESULT 32 5.4. DIRECT DATA INTERPOLATION 34 Chapter 6. Scenarios and Results 36 6.1. STATIC ANALYSIS 36 6.2. SIMULATION VARIABLES 38 6.3. SIMULATION WITH NO COMPETITION, FIXED BEHAVIOR, AND FIXED PRODUCT INTEREST 40 6.3.1. EFFECT OF PRICING SCHEMES ON PROFIT 42 6.3.2. EFFECT OF SERVICE VARIABLE COST ON PROFIT 44 6.3.3. EFFECT OF PRODUCT INTEREST ON PROFIT 46 6.4. SIMULATION WITH NO COMPETITION, MODIFIED BEHAVIOR, AND FIXED PRODUCT INTEREST 48 6.4.1. EFFECT OF SOCIAL NETWORK SIZE ON PROFIT 48 6.4.2. EFFECT OF PRODUCT EXPANSION ON PROFIT 52 6.4.3. STRATEGY CHOICE ON ADVERTISING AND PRODUCT DEVELOPMENT 56 6.5. SIMULATION WITH COMPETITION, AND FIXED PRODUCT INTEREST 58 6.5.1. FACING ONLY ONE COMPETITOR WITH THE SAME PRICING MODEL 58 6.5.2. FACING ONLY ONE COMPETITOR WITH DIFFERENT PRICING MODELS 60 6.6. SIMULATION WITH NON-CONSTANT PRODUCT INTEREST 62 6.6.1. CRITICAL PRODUCT INTEREST LEVEL 63 6.6.2. FACING TWO COMPETITORS 64 6.6.3. FACING CHAOS 67 Chapter 7. Conclusion and Future Work 69 7.1. SUMMARY 69 7.2. SOP FOR ONLINE GAME PRICING STRATEGY 69 7.3. RESULT SUMMARY 71 7.4. ALTERNATIVE PRICING MODELS 72 7.5. APPLICATIONS TO OTHER INDUSTRIES’ NEW PRODUCT OFFERING 73 GLOSSARY 76 REFERENCES 77 APPENDIX I – SURVEY FORM 80 APPENDIX II – CLASS DIAGRAM 81 APPENDIX III – CODE FRAGMENTS FOR KEY CLASSES 82 APPENDIX IV – LIST OF ALL PROGRAM FILES 85 | |
| dc.language.iso | en | |
| dc.subject | 價格策略 | zh_TW |
| dc.subject | 線上遊戲 | zh_TW |
| dc.subject | 代理者模擬系統 | zh_TW |
| dc.subject | and online game | en |
| dc.subject | pricing strategy | en |
| dc.subject | agent-based simulation | en |
| dc.title | 線上遊戲的價格策略 | zh_TW |
| dc.title | Pricing Strategies of Online Games in Taiwan | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡揚宗,江炯聰 | |
| dc.subject.keyword | 價格策略,線上遊戲,代理者模擬系統, | zh_TW |
| dc.subject.keyword | pricing strategy,agent-based simulation,and online game, | en |
| dc.relation.page | 86 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-06-30 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 國際企業學研究所 | zh_TW |
| 顯示於系所單位： | 國際企業學系 | |
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