系統識別號 | U0002-2201201616270300 |
---|---|
DOI | 10.6846/TKU.2016.00675 |
論文名稱(中文) | 代理人基預測市場、大學生非法下載行為及後數位時代音樂產業營運模式論文集 |
論文名稱(英文) | Essays on An Agent-Based Prediction Market, Behavior of Illegal Downloading of University Students and Music Industry Business Model in the Post Digital Era |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 產業經濟學系博士班 |
系所名稱(英文) | Department of Industrial Economics |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 104 |
學期 | 1 |
出版年 | 105 |
研究生(中文) | 白紀齡 |
研究生(英文) | Chi-Ling Pai |
學號 | 899540073 |
學位類別 | 博士 |
語言別 | 英文 |
第二語言別 | |
口試日期 | 2016-01-14 |
論文頁數 | 125頁 |
口試委員 |
指導教授
-
池秉聰
委員 - 于卓民 委員 - 樓永堅 委員 - 溫肇東 委員 - 胡名雯 |
關鍵字(中) |
預測市場 代理人基模型 隔離模型 信念分配 執法強度 營運模式 |
關鍵字(英) |
Prediction Market Agent-Based Modeling Segregation Model Belief Distribution Law enforcement strength Business Model |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
本論文集分為三篇研究,第一篇探討預測市場的預測能力,第二篇探討大學生非法下載行為,第三篇則接續深入探究後數位時代音樂產業營運模式。 第一篇:本文提出一個從底層個人信念出發的代理人基預測市場模型,並檢驗其市場效率。給定市場機制下,以隔離模型刻畫人際網路與形成個人的信念。本文推論2008與2012年總統選舉預測市場背後可能的信念分配。透過預測偏誤與模型參數的關係,以探測預測市場在不同信念分配下的預測能力。依交易所結果校準代理人基模型。發現信念分配在 2008 年較兩極化,2012年則相對信念分配較居中,其中衍生的噪音亦較多,說明其預測偏誤的機率增高。 第二篇:以大學生為研究對象,探討其對流行音樂之消費態度,以及對於非法下載流行音樂是否受執法強度的改變而影響其行為。採用問卷方法進行第一階段之調查分析,並據此樣本資料後設處理所產生的資訊,建構出代理人之行為模式,進入第二階段之代理人基建模與模擬。本文將執法強度拆解為:非法下載行為被查獲的機率與違法行為被查獲後處以的罰責。結果發現,當整體執法強度提高,無論是受訪者回應;亦或模型中之代理人,對非法下載之意願與行為均降低,且對非法下載被查獲的機率更為敏感。 第三篇:隨著資訊通訊科技全球化發展,世人見識到諸多的創造性破壞的經濟活動,特別是商品以內容型態呈現且載具可數位化的產業。這意味著消費者無償取得的能耐,其中最引發關注的莫過於唱片工業所受到的衝擊。本文試圖勾勒出一個符合唱片工業現況且能夠順應線上音樂發展的營運模式,以維繫音樂產業價值鏈上的主要參與者之間的夥伴關係、協同商務關係與彼此核心能力互補、銜接及最後的利潤共享,啟動營運模式並跨越組織與產業的界線,提供依循發展之道。 |
英文摘要 |
Essay 1: This study adopted an agent-based modeling approach to investigate results of the world’s leading Chinese prediction market, xFuture; in particular, Taiwan’s 2008 and 2012 presidential elections. Real transaction results of xFuture were used as the base model. We employed double auction mechanism and Schelling’s segregation model, and attempted to reconstruct the networking structures of the prediction market in 2008 and 2012. Purpose of this study is to discuss whether networking structures have any deterministic influence on how individual and hence joint belief distribution of participants could be formed. It is found that certain belief distribution properties, including shape, spread and location, could be critical factors for market efficiency/inefficiency. Our analysis suggested that in 2012 xFuture participants’ belief distributions for the main candidates were associated with high kurtosis, small variance and approximate means. In other words, it might have a higher probability to deviate from the true outcome. Essay 2: This study investigated the university students’ consumption attitude towards pop music and their behavior of illegal downloading of pop music behind various types of law enforcement degrees. In the first stage, we designed and conducted a questionnaire survey. Based on these samples, we developed a behavioral model that is customizable for each individual observation. Afterwards we came to the second stage and developed an agent-based model to perform the simulation. This study decomposed the types of law enforcement degrees into the combination of the probability of getting caught due to illegal downloading behavior and the financial penalty after getting caught. The results showed that, when strength of law enforcement is increased, the agents’ willingness of illegal downloading in the model is decreased. This empirical study found that the deterrent effects both in the real world and the simulated model were more sensitive to the probability of getting caught primarily due to illegal downloading behavior. Essay 3: Following the globalization of information and communications technology, the world witnessed economic activity based in creative destruction. This was especially evident in the realm of digital content as it implied consumers' ability to duplicate and upgrade. This phenomenon was most representative in the impact it had on Taiwan’s music industry. This study attempts to find a line within the industry that is able to adapt to the future by business model change. To sustain the music industry value chain partnerships between key players. Also, the collaborative relationships with each other's core business ability to complement each others, the convergence and the final profit sharing, so that the line is able to adapt to the future development. |
第三語言摘要 | |
論文目次 |
Contents 1. Introduction 1 2. An Agent-Based Prediction Market: A Case Study of xFuture in Taiwan 5 2.1 Introduction 5 2.2 Literature Review 8 2.2.1 Introduction to the Prediction Market 8 2.2.2 Market Efficiency 8 2.3 Research Method 10 2.3.1 Trader’s Information and Belief Distribution 10 2.3.2 Social Network 11 2.3.2.1 Schelling’s Segregation Model 11 2.3.2.2 Information Exchange 11 2.3.2.3 Agent-Based Prediction Market 12 2.3.3 Model Details 13 2.3.3.1 Initial Segregation 13 2.3.3.2 Belief Formation 13 2.3.3.3 The Behavior of Agents and the Continuous Double Auctions 14 2.4 Results 18 2.4.1 Simulation Summaries 20 2.4.2 The Hypotheses Test of the 2008 and 2012 Presidential Elections 21 2.4.2.1 Hypotheses Test 21 2.4.2.2 The Simulation Results 24 2.4.2.3 The Misjudgment Probability of xFuture 24 2.5 Conclusions 28 References 30 3. Behavior of Illegal Downloading of Pop music of University Students in Taiwan: An Agent-based Modeling Simulation 33 3.1 Introduction 33 3.1.1 Research Background 33 3.1.2 Research Purpose 35 3.2 Literature Review 36 3.3 Research Method 45 3.3.1 Research Structure 45 3.3.2 Research Design 47 3.3.3 Research Process 50 3.4 Research Analysis 51 3.4.1 Statistical Description on Valid Samples 51 3.4.2 Descriptive Statistics and OLS Regression 56 3.4.3 Correlation between Illegal Determination and Level of Illegal Downloading of Pop music 58 3.4.4 Agent-Based Modeling 59 3.5 Conclusions 64 References 65 Appendix A 70 4. Music Industry Business Model in the Post Digital Era 74 4.1 Introduction 74 4.1.1 Background Research 74 4.1.2 Research questions and objectives 76 4.1.3 Research Process 78 4.2 Relevant literature and theory 79 4.2.1 Background theory 80 4.2.2 Business model of the study 82 4.3 The Research Methods 91 4.3.1 Conceptual framework 91 4.3.2 Study Design 93 4.4 Taiwan’s music industry business model and case studies 93 4.4.1 Record label still adheres to its business model 93 4.4.2 Record labels extend the value of activities 94 4.5 The music industry embedded mode of operation post online music 98 4.5.1 Online music business model analysis of Taiwan 99 4.5.2 Taiwanese music industry’s major market participants and the value of the exchange process 106 4.5.3 Scenarios of the new music industry business model 110 4.6 Conclusions and Suggestions 116 4.6.1 Three proposals for existing record labels 117 4.6.2 Three proposals for the existing online music industry 117 References 118 5 Conclusions: From Agent-Based Modeling to the Post Digital Era 121 5.1 The Application of Agent-Based Modeling 121 5.2 Agent-Based Modeling and the Post Digital Era 122 5.3 Music Industry Business Model in the Post Digital Era 123 Table contents Table 2-1 Experimental Parameters 19 Table 2-2 The One-Way ANOVA Test Results 22 Table 2-2-1 Scheffé Test Results for Post-hoc Differences between Sets for 2008 dx,y 22 Table 2-2-2 Scheffé Test Results for Post-hoc Differences between Sets for 2008 dv,y 22 Table 2-2-3 Scheffé Test Results for Post-hoc Differences between Sets for 2012 dx,y 22 Table 2-2-4 Scheffé Test Results for Post-hoc Differences between Sets for 2012 dv,y 23 Table 2-3 Summary of Experimental Results 23 Table 3-1 Summary of Questionnaire Items and Research Variables 48 Table 3-2 Descriptive Statistics of Importance Attached to Online Purchase of Pop music Products 54 Table 3-3 Proportion of illegal downloading under different strength of law enforcement 54 Table 3-4 Descriptive statistics of Independent variables and Dependent variable 56 Table 3-5 Descriptive statistics of dummy variables 56 Table 3-6 Influence of various variables on level of illegal downloading of pop music 57 Table 3-7 Illegal determination and level of illegal downloading of pop music 59 Table 4-1 Taiwan's music industry - market participants comprehensive checklist 77 Table 4-2 Stages of improving business models 80 Table 4-3 Expansion of new business models in 10 steps 82 Table 4-4 The business model of the four "pillars" and nine “building blocks” 87 Table 4-5 Strategy Map 88 Table 4-6 Rock Records Percentage of Revenue 98 Table 4-7 List of Taiwan's online music business model 100 Table 4-8 Table 4-8 Taiwan's music industry, market participants’ value exchange analysis 107 Table 4-9 The Independent & New talent embedded at the core of online music 113 Table 4-10 The Virtual Products embedded as the core operation of online music 114 Table 4-11 The Artist fans Community is embedded as the core of online music 115 Figure contents Figure 2-1 An Illustration of Segregation Results 13 Figure 2-2 The von Neumann Neighborhoods 14 Figure 2-3 Ranges of Bid, Ask, and the Transaction Price 17 Figure 2-4 Transaction Process in the Double Auction Market 17 Figure 2-5 The Pseudo Code of the Model 18 Figure 2-6 Examples under Various Parameter Settings of Social Belief Distribution 19 Figure 2-7 The Corresponding Demand (the Downward Sloping Curve) and Supply (the Upward Sloping Curve) for the Parameter Settings in Figure 2-6 20 Figure 2-8 Examples of Transaction Price Series under Different Supply and Demand Curves 20 Figure 2-9 The Simulated Misjudgment Probability 27 Figure 2-10 Histograms of Simulated Misjudgment Probability for Various θ 27 Figure 2-11 The Voting Results (x-axis) and Simulated Prices (y-axis) 28 Figure 3-1 Total sales amount of records in Taiwan 34 Figure 3-2 Research structure 46 Figure 3-3 Distribution of monthly disposable amount of money 52 Figure 3-4 Distribution of number of hours of internet use per day 52 Figure 3-5 Distribution of number of media of information 52 Figure 3-6 Distribution of time of the latest purchase of pop music products 53 Figure 3-7 Distribution of amount of money spent on the purchase of music products 53 Figure 3-8 Distribution of number of pop music songs downloaded online per month 55 Figure 3-9 Distribution of number of pop music songs illegally downloaded per month 56 Figure 3-10 Model interface 61 Figure 3-11 Simulation results 62 Figure 4-1 Time series analysis 76 Figure 4-2 A scenario-based methodology is viewed for BM change 84 Figure 4-3 A-Model 89 Figure 4-4 The Reverse A-Model 90 Figure 4-5 Conceptual framework 92 Figure 4-6 Taiwan's music industry business model 94 Figure 4-7 Record Label Value Chain 95 Figure 4-8 Transfer of goods and cash flow of retailers and the online music industry 109 Figure 4-9 Value of the exchange diagram between market participants in Taiwan's music industry 110 Figure 4-10 New Record industry value chain 112 |
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