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研究生:林正峯
研究生(外文):Jheng-Fong Lin
論文名稱:經濟網絡、學習與演化的三篇論文
論文名稱(外文):Three Essays on Network Economics, Learning and Evolution
指導教授:莊委桐莊委桐引用關係
口試委員:葉俊顯梁孟玉袁國芝陳俊廷
口試日期:2018-07-06
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:經濟學研究所
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:89
中文關鍵詞:社會網絡意見形成演化學習重覆動態賽局理論
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本論文由三篇獨立的文章所組成。第一篇文章分析社會網路架構的改變對於政治抗爭的群眾團體規模之影響;第二篇文章透過演化經濟學探討學習者具有能力可以選擇是否執行需要花費成本的學習行為時,在變動的環境之下能否較其他僅採取固定行為模式的參賽者具有長期的演化優勢。第三篇論文討論當訊息在社群網路上受到網路平台的扭曲或截斷時,如何影響最終社會共識的收斂位置與其收斂路徑。關於各篇文章詳摘,請參閱第一章與隨後各章第一節。
This dissertation consists of three independent papers regarding the topics in the critical group size of political protest on a social network, the evolutionary result of the smarter learners who can decide to execute costly learning in a changing world and the effect and dynamic of the manipulation on the public opinion. The abstracts of each papers are relegated to the Introduction of this dissertation.
論文口試委員審定書 i
謝辭 ii
中文摘要 iii
Abstract iv
1 Introduction 1
2 The Political Protest on a Social Network 4
2.1 Introduction 4
2.2 Model 6
2.2.1 Behavior of the Individuals 7
2.2.2 Description of Network Structures 9
2.3 Examples under Different Degree Distributions 11
2.3.1 General Degree Distribution 11
2.3.2 Regular Network 15
2.3.3 Scale Free Network 15
2.4 Conclusion and Future Extension 15
2.5 Reference 19
2.6 Appendix A: Configuration Model 21
2.7 Appendix B: Some Basic Operations of p.g.f. 21
2.8 Appendix C: Proofs 22
2.8.1 Proof of Proposition 2.1 22
2.8.2 Proof of Proposition 2.2 23
2.8.3 Proof of Lemma 1 23
2.8.4 Proof of Proposition 2.4 24
3 The Smarter Learner in a Changing World 26
3.1 Introduction 26
3.2 Model 29
3.3 The Analysis in the Long Run 32
3.3.1 The Asymptotic Relative Proportion Between Two Simple Types 33
3.3.2 The Asymptotic Relative Proportion of a Simple Type Over Learners 34
3.3.3 The Range of p for The Learners to Asymptotically Dominate The Population 36
3.3.4 The Parameters and The Corresponding Change of Survival Space 38
3.3.5 Simulations 39
3.3.6 Two Learning Types in The World 49
3.4 Conclusion 53
3.5 Reference 54
3.6 Appendix 55
3.6.1 The Proof of Lemma 3.3 55
3.6.2 The Proof of Lemma 3.4 56
4 The Manipulation of the Social Opinion on a Simple Network 59
4.1 Introduction 59
4.2 Model 61
4.2.1 General Model 61
4.2.2 Simple Model without Manipulation 63
4.2.3 Simple Model with Manipulation 76
4.3 Conclusion and Further Research 80
4.4 Reference 82
4.5 Appendix 82
4.5.1 The proof of Proposition 4.1 82
4.5.2 The proof of Proposition 2 84
4.5.3 The proof of Proposition 4.3 84
4.5.4 The proof of Proposition 4.4 85
4.5.5 The proof of Lemma 4.2 86
Reference 89
Reference of Chapter 2
Bender and Canfield (1978), “The Asymptotic Number of Labelled Graphs with Given Degree Sequences.” Journal of Combinatorial Theory A 24:296-307.
Centola (2013), Homophily, Networks, and Critical Mass: Solving the Start-up Problem in Large Group Collective Action, Rationality and Society 25: 3 doi: 10.1177/104346311247373
Duncan S. Callaway, M. E. J. Newman, Steven H. Strogatz, and Duncan J. Watts. (2000), “Network Robustness and Fragility: Percolation on Random Graphs.” Physical Review Letters 85 (25): 5468–71.
Erdös and Rényi (1959), “On Random Graphs,” Publicationes Mathematicae, 6, 290- 297.
Erdös and Rényi (1960), “On the evolution of random graphs,” Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17-61.
Erdös and Rényi (1961), “On the strength of connectedness of a random graph,” Acta Mathematica Hungarica, 12(1-2), 261-267.
Ellis and Fender (2010), Information Cascades and Revolutionary Regime Transitions, Economic Journal, 121, 763-792.
M. O. Jackson (2008), Social and Economic Networks. Princeton University Press.
M. O. Jackson and Yariv. (2008), “Diffusion, Strategic Interaction, and Social Structure”, in J. Benhabib, A. Bisin and M. O. Jackson (eds.) Handbook of Social Economics, Elsevier.
Lohmann (1994), Information Aggregation Through Costly Political Action, American Economic Review, 84(3), 518-530.
M. E. J. Newman (2002), “Assortative mixing in networks,” Physical Review Letters 89(20): 208701.
M. E. J. Newman (2003), “Mixing patterns in networks,” Physical Review Letters 67(2): 026126.
M. E. J. Newman (2010), Networks: An Introduction. Oxford University Press.
Olson (1965), The Logic of Collective Action. Cambridge, MA: Harvard University Press.
Prell (2012), Social Network Analysis, SAGE Publications Ltd.
C. C. Yin (1998), “Equilibria of Collective Action in Different Distributions of Protest Thresholds.” Public Choice, 97: 535–567.
Zomeren (2009), “Metaphors of Protest: A Classification of Motivations for Collective Action,” Journal of Social Issues, Vol. 65, No. 4, 2009, pp. 661–679.

Reference of Chapter 3
Blume and Easley (1992): “Evolution and Market Behavior,” Journal of Economic Theory, 58, 9-40.
Börgers and Sarin (1997): “Learning Through Reinforcement and Replicator Dynamics,” Journal of Economic Theory, 77, 1-14.
Conlisk (1996): “Bounded Rationality and Market Fluctuations,” Journal of Economic Behavior and Organization, 29, 233-250.
Dridi and Lehmann (2015): “A Model for the Evolution of Reinforcement Learning in Fluctuating Games,” Animal Behavior, 104, 87-114.
Ellison and Fudenberg (1998): “Rules of Thumbs for Social Learning,” Journal of Political Economy, 101(4), 612-643.
Fudenberg and Levine (1998): The Theory of Learning in Games. MIT press.
Frongillo, Schoenebeck and Tamuz (2011): “Social Learning in a Changing World,” In: Chen N., Elkind E., Koutsoupias E. (eds) Internet and Network Economics. WINE 2011. Lecture Notes in Computer Science, vol 7090. Springer, Berlin, Heidelberg.
Friedman (1991): “Evolutionary Games in Economics,” Econometrica, 59(3), 637-666.
Gomes (2012): “Discrete Dynamics in Evolutionary Games,” Discrete Dynamics in Nature and Society.
Heller (2004): “An Evolutionary Approach to Learning in a Changing Environment,” Journal of Economic Theory, 114, 31-55.
Mohlin (2012): “Evolution of Theories of Mind,” Games and Economic Behavior, 75, 299-318.
Mengel (2012): “Learning across Games,” Game and Economic Behavior, 74,601-619.
Stahl (1993): “The Evolution of Smart_n Players,” Game and Economic Behavior, 5, 604-617.

Reference of Chapter 4
Bakshy, Messing and Adamic (2015), “Exposure to ideologically diverse news and opinion on Facebook”, Science. Vol. 348, Issue 6239, 1130-1132.
Barbera et. al. (2015), “Tweeting from left to right: Is online political communication more than an echo chamber”, Psychological Science, Vol. 26(10) 1531–1542.
DeGroot (1974), “Reaching a Consensus, ” Journal of the American Statistical Association, 118-121.
Flaxman, Goel and Rao (2016), “Filter bubbles, echo chambers and online news consumption”, Public Opinion Quaterly, Vol. 80, Special Issue, 298–320.
Gentzkow and Shapiro (2011), “ideological segregation online and offline”, Quarterly Journal of Economics (126), 1799-1839.
Jackson (2008), Social and Economic Network, Princeton Press.
Lorenz (2005), “A Stabilization Theorem for Dynamics of Continuous Opinions, ” Physica A (355), 217-223.
Pariser (2011), The Filter Bubble: What The Internet Is Hiding From You, Penguin UK.
Sunstein (2009), Going to Extremes: How Like Minds Unite and Divide, Oxford University Press.
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