4 research outputs found
κ·Έλν μμμ νν΄μ§λ μ£μμ λλ λ§ κ²μ
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 물리·μ²λ¬ΈνλΆ(물리νμ 곡), 2013. 2. κ°λ³λ¨.λ€μν λΆμΌμμ νλ ₯μ΄ μΌμ΄λλ κΈ°μ λ₯Ό μ΄ν΄νκΈ° μν λκ΅¬λ‘ μ£μμ λλ λ§ κ²μμ μ¬μ©ν΄μλ€. μλ§μ μ°κ΅¬μμ νλ ₯μ μ€λͺ
νκΈ° μν λ€μν κ°μ€λ€μ΄ μ μλμλ€. μ±κ³΅μ μ΄λΌ νκ°λλ κ°μ€ μ€ νλλ μ§ν κ³Όμ κ³Ό κ³΅κ° κ΅¬μ‘°μ μ‘°ν©μ΄λ€. 첫 λ²μ§Έ μ₯μμ κ³΅κ° κ΅¬μ‘° μμ μ§νμ μ£μμ λλ λ§ κ²μμ κ°λ¨ν μ΄ν΄λ³΄κ² λ€. κ·Έ λ€μ λ ννΈμμ λ κ°μ§ μΈλΆμ μΈ μΈ‘λ©΄μμ κ³΅κ° κ΅¬μ‘° μμ μ§νμ μ£μμ λλ λ§ κ²μμ μ°κ΅¬ν κ²°κ³Όλ₯Ό μ μνμλ€.
λ λ²μ§Έ μ₯μ ν° μΈμ λ€νΈμν¬μμ μμ μΈμ λ€νΈμν¬λ‘ λ³νκ° κ°λ₯ν λ€νΈμν¬ μμμ μ§νλλ μ£μμ λλ λ§ κ²μμ λν μ°κ΅¬μ΄λ€. μ΄ μ°κ΅¬μμλ νΉν νλ ₯ μ λ΅μ μ μ§νλ νμμλ€μ΄ μ΄λ£¨λ μ§λ¨μ λν΄ μ΄ν΄λ³΄μλ€. νλΈλ€ κ°μ μ°κ²°μ΄ λ§μ μμ μΈμ λ€νΈμν¬μμλ λ¨ νλμ νλ ₯μ μ§λ¨μ΄ μμ±λλ©° μ 체μ μΈ νλ ₯ μμ€λ λλ€. λ°λ©΄, ν° μΈμ λ€νΈμν¬μμλ λ€μν ν¬κΈ°λ₯Ό κ°λ μλ§μ νλ ₯μ μ§λ¨μ΄ νμ±λλ©°, νλ ₯μ λΉμ¨μ μλμ μΌλ‘ λμ§ μλ€. ν° μΈμ λ€νΈμν¬μμ μμ μΈμ λ€νΈμν¬λ‘ λ€νΈμν¬λ₯Ό λ³νμν€λ©΄μ νλ ₯μ μ§λ¨μ ν¬κΈ° λΆν¬λ₯Ό μ‘°μ¬νμκ³ , μ μ΄μ μμλ ν¬κΈ° λΆν¬κ° λ©±ν¨μ κΌ΄μ λ°λ₯Έλ€λ μ μ νμΈνμλ€.
μΈ λ²μ§Έ μ₯μμλ μ§νμ μ£μμ λλ λ§ κ²μμ νΌν© μ λ΅μ λμ
νλ€. μ£μμ λλ λ§ κ²μμμ νΌν© μ λ΅μ νμμμ νλ ₯ νλ₯ λ‘μ¨ νν κ°λ₯νλ€. μ μ© μ¬λ‘λ‘μ, λ κ·€λ¬ κ·Έλν μμμ λ κ°μ§ νΌν© μ λ΅λ§μΌλ‘ μ§νλλ μ£μμ λλ λ§ κ²μμμ μ§νμ μμ μ±μ μ‘°μ¬νλ€. λ€λ₯Έ μ λ΅μ μΉ¨μ
μ νμ©νμ§ μλ μ λ΅μ μ§νμ μΌλ‘ μμ ν μ λ΅μ΄λΌκ³ νλ€. κ²°μ λ‘ μ μΈ κ²μ λ²μΉ νμμλ νμ μ§νμ μΌλ‘ μμ ν μ λ΅μ΄ μ‘΄μ¬νλ€λ μ μ νμΈνλ€. μ΄λ¬ν μ λ΅μ κ°μ§ μ§λ¨μ λ€λ₯Έ μ λ΅μ μΉ¨μ
μλμλ λΆκ΅¬νκ³ λ³Έλμ νλ ₯ μμ€μ μ μ§ν μ μλ€. μ£μμ λλ λ§ κ²μμ νΌν© μ λ΅μ λμ
ν μ΄ μ°κ΅¬λ λ³΄λ€ νμ€μ κ°κΉμ΄ κ²μμ κΈ°μ΄κ° λ μ μμ κ²μ΄λ€.Prisoner's dilemma(PD) game has been used widely in various disciplines as a tool to understand the mechanisms to evoke the cooperation although a player's favorable choice is not cooperative. Among a variety of explanations for the emergence of cooperation, the combination of evolutionary process and spatial structure is one of the successful hypotheses. In the first chapter, we review the spatial evolutionary PD games shortly. In the next two parts, we study the spatial evolutionary PD games in two detailed aspects.
In the second chapter, we study the PD games on several scale-free networks bridging between large-world and small-world types. Especially, we focus on the clusters of permanent cooperators. In small-world networks where the hubs are interconnected, one cooperator cluster is formed, and overall cooperation level is relatively high. On the other hand, in large-world networks where the hubs are separated, the clusters of cooperators with diverse sizes are formed, and the fraction of cooperators is not high. We investigate the cluster size distribution, changing networks from large-world ones to small-world ones, and find that the cluster size follows a power law at the transition point.
In the third chapter, we introduce mixed strategies into spatial evolutionary PD games. The probability of cooperation is used to represent the mixed strategies. As an application, we investigate the evolutionary stability in PD games with two mixed strategies on several types of regular graphs. A strategy which doesn't allow the invasion of other strategy is called an evolutionarily stable strategy. We find that under the deterministic game rules, there always exist evolutionarily stable strategies. These strategies can maintain the cooperation level against the invasion of other strategies. The introduction of mixed strategies in PD games can be the basis of more realistic PD games.Abstract i
Contents iii
List of Figures vii
List of Tables xiii
1. Introduction to spatial evolutionary prisoners dilemma games 1
1.1 Prisoners dilemma 1
1.2 Spatial evolutionary prisoners dilemma game 4
1.3 Spatial evolutionary prisoners dilemma game on scale-free networks 5
1.4 Rules for spatial evolutionary PD games 6
1.4.1 Typical processes of games 6
1.4.2 Payoff matrix 8
1.4.3 Fitness 9
1.4.4 Synchronous update vsasynchronous update 9
1.4.5 Selection of candidate players for updating strategies 10
1.4.6 Selection of a neighbor for a reference 10
1.4.7 Adoption probability 11
2. Prisoners dilemma games on hierarchical model 13
2.1 Introduction 13
2.2 Hierarchical network model 14
2.2.1 Construction rule 14
2.2.2 Network characteristics 15
2.3 Rules for evolutionary prisoners dilemma games 16
2.4 Simulation results and discussions 17
2.4.1 Results on hierarchical networks 17
2.4.2 Results on rewired hierarchical networks 24
2.4.3 Results on the WWW network 27
2.5 Summary 30
3. Evolutionary stability in the spatial evolutionary PD games with mixed-strategies 33
3.1 Introduction to mixed strategies 33
3.1.1 Payoffs in mixed-strategy PD games 35
3.2 Evolutionary stability in PD game with mixed strategies 36
3.3 Rules of games 38
3.4 Fitnesses of players 40
3.4.1 Fitnesses in regular graphs 41
3.5 Evolutionary stability on complete graphs 42
3.6 Evolutionary stability on regular graph with degree 2 42
3.6.1 Comparison between fitnesses of two players with different strategies 43
3.6.2 Simulation results and discussions 47
3.7 Evolutionary stability on regular graph with degree 3 50
3.7.1 Comparison between fitnesses of two players with different strategies 50
3.7.2 Simulation results and discussions 50
3.8 Evolutionary stability on regular graph with degree 4 59
3.8.1 Comparison between fitnesses of two players with different strategies 59
3.8.2 Simulation results and discussions 59
3.9 Discussions 71
3.10 Summary 73
4. Conclusion 77
Appendices 81
Appendix A. Propagation of strategies on cycle graph 83
A.1 Propagation of strategies under RuleI 83
A.1.1 Section 1,2,3,4 at b=3 83
A.1.2 Section 5,6,7,8 at b=3 84
A.2 Propagation of strategies under Rule II 84
A.2.1 Section 1,2,3,4 at b=3 86
A.2.2 Section 5,6,7 at b=3 86
A.2.3 Section 8 at b=3 88
Appendix B. More detailed results on mixed-strategy PD games on honeycomb lattice 89
B.1 Propagation of strategies on honeycomb lattice under Rule I 89
B.2 Propagation of strategies on honeycomb lattice under Rule II 90
Appendix C. More detailed results on mixed-strategy PD games on square lattice 97
C.1 Propagation of strategies on square lattice under Rule I 97
C.2 Propagation of strategies on square lattice under Rule II 98
Appendix D. More detailed results on mixed-strategy PD games on random graphs with degree 3 103
D.1 Size dependency of fraction of B-type players on random regular graphs with degree 3 under Rule I 103
D.2 Size dependency of fraction of B-type players on random regular graphs with degree 3 under Rule II 104
Appendix E. More detailed results on mixed-strategy PD games on random graphs with degree 4 107
E.1 Size dependency of fraction of B-type players on random regular graphs with degree 4 under Rule I 107
E.2 Size dependency of fraction of B-type players on random regular graphs with degree 4 under Rule II 108
Bibliography 113
Abstract in Korean 119Docto
A Study on the Shift from Passive Representation to Active Representation: A Focus on Critical Mass and the Effect of Discretion on Active Representation
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Όμμ λΉλ―Έλ₯Ό μ 곡νκΈ°λ νλ€. λ³Έ μ°κ΅¬λ νκ΅μ μ€μλΆμ² μ¬μ± 곡무μμ μ€μ¬μΌλ‘ μκ·Ήμ κ±° λνμ±κ³Ό μ κ·Ήμ λνμ±μ ꡬλΆνκ³ μμ κ°μ κ΄κ³ μ νμ λΆμνκ³ μ νμλ€. νΉν μ€μμ λΆλΆμ²μ μ¬μ±κ³΅λ¬΄μμ λμμΌλ‘ μκ·Ήμ λνμ±κ³Ό μ κ·Ήμ λνμ±μ κ΄κ³ μ νμ μ€μ¦ λΆμνμλ€. Many previous studies on representative bureaucracy have examined passive representation and active representation. Building on previous studies this study attempts to show how passive representation shifts to active representation of female interests. It is argued that passive representation reflecting demograpic composition is very limited in represnting interests of minority groups, particularly in organization like bureaucracy with a strong conventional cultural tradition. Active representation is central to true representation of the voices of a minority group.λ³Έ μ°κ΅¬λ λΆλΆμ μΌλ‘ νκ΅μ°κ΅¬μ¬λ¨μ ν΅ν΄ κ΅μ‘κ³ΌνκΈ°μ λΆμ μΈκ³μμ€ μ°κ΅¬μ€μ¬λνμ‘μ±μ¬μ
(WCU)μΌλ‘λΆν° μ§μλ°μ μνλμμ΅λλ€. (μ μ²κ³Όμ λ²νΈ R32-2008-000-20002-0