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BL
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| Record Number
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860835
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| Main Entry
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Ye, Mengbin
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| Title & Author
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Opinion dynamics and the evolution of social power in social networks /\ Mengbin Ye.
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| Publication Statement
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Cham, Switzerland :: Springer,, [2019]
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| Series Statement
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Springer Theses
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| Page. NO
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1 online resource (xxiii, 209 pages)
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| ISBN
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3030106063
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: 9783030106065
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3030106055
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9783030106058
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| Notes
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"Doctoral thesis accepted by the Australian National University, Canberra, Australia."
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6.3.1 Extending the Results in Sect. 5.3
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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Intro; Supervisor's Foreword; Parts of this thesis have been published in the following articles; Acknowledgements; Contents; 1 Introduction; 1.1 Social Network Analysis; 1.2 Opinion Dynamics and Influence Networks; 1.2.1 The Fundamental French-Harary-DeGroot Model; 1.2.2 Beyond Consensus and Towards Complex Social Phenomena; 1.2.3 Social Phenomena of Relevance; 1.2.4 Motivations and Key Concepts; 1.2.5 Relation to Coordination of Multi-agent Systems; 1.3 Thesis Outline and Statements of Collaborations; References; 2 Preliminaries; 2.1 Notations and Definitions; 2.2 Graph Theory
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2.3 Basic Models of Opinion Dynamics2.3.1 The DeGroot Model; 2.3.2 The Friedkin-Johnsen Model; 2.3.3 Comments on the Models; References; Part I How Differences in Private and Expressed Opinions Arise; 3 A Novel Model for Opinion Dynamics Under Pressure to Conform; 3.1 Introduction; 3.1.1 Chapter Organization; 3.2 A Model with Expressed and Private Opinions; 3.2.1 The Opinion Dynamics Model; 3.2.2 Local Public Opinion; 3.2.3 Obtaining a Compact Form for the Influence Network; 3.3 Convergence Properties of the Model; 3.3.1 Causes of Persistent Disagreement and Differences in Opinions
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3.3.2 Estimating Disagreement in the Private Opinions3.3.3 An Individual's Resilience Affects Everyone; 3.3.4 Local Public Opinions; 3.3.5 Simulations; 3.4 Conclusion; 3.5 Appendix: Proofs and Simulations; 3.5.1 Proof of Lemma 3.1; 3.5.2 Proof of Lemma 3.2; 3.5.3 Proof of Lemma 3.3; 3.5.4 Simulation Counter-Example; 3.5.5 Proof of Corollary 3.2; References; 4 The EPO Model's Connections with Social Psychology Concepts; 4.1 Introduction; 4.1.1 Introduction to Asch's Experiments; 4.1.2 Pluralistic Ignorance in Social Networks; 4.1.3 Chapter Organization
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4.2 Investigation of Asch's Conformity Experiments4.2.1 Theoretical Analysis; 4.2.2 Simulations; 4.3 A Few Zealots Can Create Pluralistic Ignorance; 4.3.1 Base Simulation Set-Up; 4.3.2 Simulation Set-Up and Results For Small-World Networks; 4.3.3 Simulation Set-Up and Results For Scale-Free Networks; 4.3.4 Discussion for Small-World Networks; 4.3.5 Discussion for Scale-Free Networks; 4.3.6 Key Observations and Insights; 4.4 Conclusions; References; Part II Evolution of Individual Social Power; 5 Evolution of Social Power in Networks with Constant Topology; 5.1 Introduction
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5.1.1 The DeGroot-Friedkin Model5.1.2 Chapter Organization; 5.2 Exponential Convergence to Constant Social Power; 5.3 Further Analysis of Dynamical Behaviour; 5.3.1 A Contraction-Like Property; 5.3.2 Upper Bound on Individual's Social Power at Equilibrium; 5.3.3 Convergence Rates; 5.4 Conclusions; References; 6 Dynamic Social Networks: Exponential Forgetting of Perceived Social Power; 6.1 Introduction; 6.1.1 Motivating Examples for Issue-Varying Topology; 6.1.2 Chapter Organization; 6.2 The Dynamic Topology Model and Objective; 6.3 Exponential Convergence to a Unique Limiting Trajectory
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| Abstract
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This book uses rigorous mathematical analysis to advance opinion dynamics models for social networks in three major directions. First, a novel model is proposed to capture how a discrepancy between an individual's private and expressed opinions can develop due to social pressures that arise in group situations or through extremists deliberately shaping public opinion. Detailed theoretical analysis of the final opinion distribution is followed by use of the model to study Asch's seminal experiments on conformity, and the phenomenon of pluralistic ignorance. Second, the DeGroot-Friedkin model for evolution of an individual's social power (self-confidence) is developed in a number of directions. The key result establishes that an individual's initial social power is forgotten exponentially fast, even when the network changes over time; eventually, an individual's social power depends only on the (changing) network structure. Last, a model for the simultaneous discussion of multiple logically interdependent topics is proposed. To ensure that a consensus across the opinions of all individuals is achieved, it turns out that the interpersonal interactions must be weaker than an individual's introspective cognitive process for establishing logical consistency among the topics. Otherwise, the individual may experience cognitive overload and the opinion system becomes unstable. Conclusions of interest to control engineers, social scientists, and researchers from other relevant disciplines are discussed throughout the thesis with support from both social science and control literature.
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| Subject
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Online social networks.
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Public opinion-- Mathematical models.
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Social networks-- Mathematical models.
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Online social networks.
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Public opinion-- Mathematical models.
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| Subject
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Social networks-- Mathematical models.
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| Dewey Classification
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006.7/54
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| LC Classification
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HM742.Y46 2019
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