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Introduction to Computational Social Science: Principles and Applications 2014 ed. [Kõva köide]

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  • Formaat: Hardback, 320 pages, kõrgus x laius: 235x155 mm, kaal: 733 g, 12 Tables, black and white; 21 Illustrations, color; 38 Illustrations, black and white; XXXIII, 320 p. 59 illus., 21 illus. in color., 1 Hardback
  • Sari: Texts in Computer Science
  • Ilmumisaeg: 15-Jan-2014
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1447156609
  • ISBN-13: 9781447156604
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Introduction to Computational Social Science: Principles and Applications 2014 ed.Suurem pilt
  • Formaat: Hardback, 320 pages, kõrgus x laius: 235x155 mm, kaal: 733 g, 12 Tables, black and white; 21 Illustrations, color; 38 Illustrations, black and white; XXXIII, 320 p. 59 illus., 21 illus. in color., 1 Hardback
  • Sari: Texts in Computer Science
  • Ilmumisaeg: 15-Jan-2014
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1447156609
  • ISBN-13: 9781447156604
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781447156604.html
Märksõnad:
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies. This reader-friendly textbook/reference is the first work of its kind to provide a comprehensive and unified Introduction to Computational Social Science. Four distinct methodological approaches are examined in particular detail, namely automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. The coverage of each of these approaches is supported by a discussion of the historical context and motivations, as well as by a list of recommended texts for further reading. Topics and features: Describes the scope and content of each area of CSS, covering topics on information extraction, social networks, complexity theory, and social simulations ; Highlights the main theories of the CSS paradigm as causal explanatory frameworks that shed new light on the nature of human and social dynamics ; Explains how to distinguish and analyze the different levels of analysis of social complexity using computational approaches ; Discusses a number of methodological tools, including extracting entities from text, computing social network indices, and building an agent-based model ; Presents the main classes of entities, objects, and relations common to the computational analysis of social complexity ; Examines the interdisciplinary integration of knowledge in the context of social phenomena.This unique, clearly-written textbook is essential reading for graduate and advanced undergraduate students planning on embarking on a course on computational social science, or wishing to refresh their knowledge of the fundamental aspects of this exciting field.

This effective introduction to the key concepts in computational social science includes formal definitions and a glossary, covers topics such as information extraction, social networks and complexity theory, and discusses a range of methodological tools.



This reader-friendly textbook is the first work of its kind to provide a unified Introduction to Computational Social Science (CSS). Four distinct methodological approaches are examined in detail, namely automated social information extraction, social network analysis, social complexity theory and social simulation modeling. The coverage of these approaches is supported by a discussion of the historical context, as well as by a list of texts for further reading. Features: highlights the main theories of the CSS paradigm as causal explanatory frameworks that shed new light on the nature of human and social dynamics; explains how to distinguish and analyze the different levels of analysis of social complexity using computational approaches; discusses a number of methodological tools; presents the main classes of entities, objects and relations common to the computational analysis of social complexity; examines the interdisciplinary integration of knowledge in the context of social phenomena.

Arvustused

From the book reviews:

This 300 pages book a very good introduction to CSS, as it covers all relevant aspects that are fundamental to start a CSS research. the book could be used for teaching methodological aspects of modeling in the social sciences . Claudio-Cioffis book is a must read for any beginner of CSS. It is a great synthesis of the main achievements in this field that could be fruitfully adopted by anyone teaching CSS as an introductory guide. (Frantiek Kalvas, jasss.soc.surrey.ac.uk, February, 2015)

It is a nice introduction for beginners to grasp the fundamentals of CSS. This well-organized book provides a timely and comprehensive systematic introduction to CSS. The chapters are relatively independent. Although this book is intended as a CSS textbook for graduate students anyone who is interested in the fundamentals of CSS would benefit from reading it. (Chenyi Hu, Computing Reviews, August, 2014)

1 Introduction 1(22)
1.1 What Is Computational Social Science?
1(1)
1.2 A Computational Paradigm of Society
2(1)
1.3 CSS as an Instrument-Enabled Science
3(1)
1.4 Examples of CSS Investigations: Pure Scientific Research vs. Applied Policy Analysis
4(3)
1.5 Society as a Complex Adaptive System
7(5)
1.5.1 What Is a CAS in CSS?
7(1)
1.5.2 Tripartite Ontology of Natural, Human, and Artificial Systems
8(1)
1.5.3 Simon's Theory of Artifacts: Explaining Basic Social Complexity
9(2)
1.5.4 Civilization, Complexity, and Quality of Life: Role of Artificial Systems
11(1)
1.6 Main Areas of CSS: An Overview
12(5)
1.6.1 Automated Social Information Extraction
12(1)
1.6.2 Social Networks
13(1)
1.6.3 Social Complexity
14(1)
1.6.4 Social Simulation Modeling
15(2)
1.7 A Brief History of CSS
17(2)
1.8 Main Learning Objectives
19(1)
Recommended Readings
20(3)
2 Computation and Social Science 23(44)
2.1 Introduction and Motivation
23(1)
2.2 History and First Pioneers
24(1)
2.3 Computers and Programs
25(3)
2.3.1 Structure and Functioning of a Computer
25(2)
2.3.2 Compilers and Interpreters
27(1)
2.4 Computer Languages
28(4)
2.5 Operators, Statements, and Control Flow
32(2)
2.6 Coding Style
34(1)
2.7 Abstraction, Representation, and Notation
35(4)
2.8 Objects, Classes, and Dynamics in Unified Modeling Language (UML)
39(21)
2.8.1 Ontology
40(4)
2.8.2 The Unified Modeling Language (UML)
44(11)
2.8.3 Attributes
55(2)
2.8.4 Operations
57(3)
2.9 Data Structures
60(2)
2.10 Modules and Modularization
62(1)
2.11 Computability and Complexity
63(1)
2.12 Algorithms
64(2)
Recommended Readings
66(1)
3 Automated Information Extraction 67(22)
3.1 Introduction and Motivation
67(1)
3.2 History and First Pioneers
67(4)
3.3 Linguistics and Principles of Content Analysis: Semantics and Syntax
71(1)
3.4 Semantic Dimensions of Meaning: From Osgood to Heise
72(4)
3.4.1 EPA-Space and the Structure of Human Information Processing and Meaning
73(1)
3.4.2 Cross-Cultural Universality of Meaning
74(2)
3.5 Data Mining: Overview
76(1)
3.6 Data Mining: Methodological Process
77(10)
3.6.1 Research Questions
78(1)
3.6.2 Source Data: Selection and Procurement
79(1)
3.6.3 Preprocessing Preparations
80(1)
3.6.4 Analysis
80(7)
3.6.5 Communication
87(1)
Recommended Readings
87(2)
4 Social Networks 89(30)
4.1 Introduction and Motivation
89(1)
4.2 History and First Pioneers
90(4)
4.3 Definition of a Network
94(5)
4.3.1 A Social Network as a Class Object
95(1)
4.3.2 Relational Types of Social Networks
96(1)
4.3.3 Level of Analysis
97(2)
4.3.4 Dynamic Networks
99(1)
4.4 Elementary Social Network Structures
99(3)
4.5 The Network Matrix
102(1)
4.6 Quantitative Measures of a Social Network
103(2)
4.6.1 Nodal Measures: Micro Level
103(1)
4.6.2 Network Measures: Macro Level
104(1)
4.7 Dynamic (Actually, Kinetic) Networks as Ternary Associations
105(1)
4.8 Applications
106(9)
4.8.1 Human Cognition and Belief Systems
106(3)
4.8.2 Decision-Making Models
109(1)
4.8.3 Organizations and Meta-Models
110(1)
4.8.4 Supply Chains
110(3)
4.8.5 The Social Structure of Small Worlds
113(1)
4.8.6 International Relations
114(1)
4.9 Software for SNA
115(2)
Recommended Readings
117(2)
5 Social Complexity I: Origins and Measurement 119(26)
5.1 Introduction and Motivation
119(1)
5.2 History and First Pioneers
119(2)
5.3 Origins and Evolution of Social Complexity
121(9)
5.3.1 Sociogenesis: The "Big Four" Primary Polity Networks
122(5)
5.3.2 Social Complexity Elsewhere: Secondary Polity Networks
127(1)
5.3.3 Contemporary Social Complexity: Globalization
127(2)
5.3.4 Future Social Complexity
129(1)
5.4 Conceptual Foundations
130(5)
5.4.1 What Is Social Complexity?
131(1)
5.4.2 Defining Features of Social Complexity
131(4)
5.5 Measurement of Social Complexity
135(9)
5.5.1 Qualitative Indicators: Lines of Evidence
135(2)
5.5.2 Quantitative Indicators
137(7)
Recommended Readings
144(1)
6 Social Complexity II: Laws 145(26)
6.1 Introduction and Motivation
145(1)
6.2 History and First Pioneers
145(2)
6.3 Laws of Social Complexity: Descriptions
147(14)
6.3.1 Structural Laws: Serial, Parallel, and Hybrid Complexity
147(5)
6.3.2 Distributional Laws: Scaling and Non-equilibrium Complexity
152(9)
6.4 Power Law Analysis
161(7)
6.4.1 Empirical Analysis: Estimation and Assessing Goodness of Fit
161(3)
6.4.2 Theoretical Analysis: Deriving Implications
164(4)
6.5 Universality in Laws of Social Complexity
168(1)
Recommended Readings
169(2)
7 Social Complexity III: Theories 171(52)
7.1 Introduction and Motivation
171(1)
7.2 History and First Pioneers
171(3)
7.3 Theories of Social Complexity: Elements of Explanation
174(10)
7.3.1 Sequentiality: Modeling Processes. Forward Logic
175(4)
7.3.2 Conditionality: Modeling Causes. Backward Logic
179(4)
7.3.3 Hybrid Bimodal Social Complexity: Several-Among-Some Causes
183(1)
7.4 Explaining Initial Social Complexity
184(23)
7.4.1 Emergence of Chiefdoms
190(9)
7.4.2 Emergence of States
199(8)
7.5 General Theories of Social Complexity
207(13)
7.5.1 Theory of Collective Action
207(3)
7.5.2 Simon's Theory of Adaptation Via Artifacts
210(4)
7.5.3 Canonical Theory as a Unified Framework
214(6)
Recommended Readings
220(3)
8 Simulations I: Methodology 223(26)
8.1 Introduction and Motivation
223(1)
8.2 History and First Pioneers
223(2)
8.3 Purpose of Simulation: Investigating Social Complexity Via Virtual Worlds
225(1)
8.4 Basic Simulation Terminology
226(3)
8.5 Fidelity of Representation and Implications
229(2)
8.6 Types of Social Simulation: From System Dynamics to Agent-Based Models
231(1)
8.7 Development Methodology of Social Simulations
232(6)
8.7.1 Motivation: What Are the Research Questions Addressed by a Given Model?
232(1)
8.7.2 Conceptual Design: What Does the Abstraction Look Like?
233(1)
8.7.3 Implementation: How Is the Abstracted Model Written in Code?
234(1)
8.7.4 Verification: Does the Simulation Perform as Intended?
235(1)
8.7.5 Validation: Can We Trust the Results?
236(1)
8.7.6 Virtual Experiments and Scenario Analyses: What New Information Does the Simulation Generate?
237(1)
8.8 Assessing the Quality of a Social Simulation
238(5)
8.8.1 General Principles for Social Modeling Assessment
238(2)
8.8.2 Dimensions of Quality in Social Simulation Models
240(3)
8.9 Methodology of Complex Social Simulations
243(2)
8.10 Comparing Simulations: How Are Computational Models Compared?
245(1)
Recommended Readings
246(3)
9 Simulations II: Variable-Oriented Models 249(24)
9.1 Introduction and Motivation
249(1)
9.2 History and First Pioneers
249(2)
9.3 System Dynamics Models
251(11)
9.3.1 Motivation: Research Questions
253(1)
9.3.2 Design: Abstracting Conceptual and Formal Models
253(5)
9.3.3 Implementation: System Dynamics Software
258(1)
9.3.4 Verification
259(1)
9.3.5 Validation
260(1)
9.3.6 Analysis
261(1)
9.4 Queueing Models
262(8)
9.4.1 Motivation: Research Questions
262(3)
9.4.2 Design: Abstracting Conceptual and Formal Models
265(3)
9.4.3 Implementation: Queuing Systems Software
268(1)
9.4.4 Verification
269(1)
9.4.5 Validation
269(1)
9.4.6 Analysis
270(1)
Recommended Readings
270(3)
On System Dynamics (and Dynamical Systems)
270(1)
On Queueing Systems
271(2)
10 Simulations III: Object-Oriented Models 273(32)
10.1 Introduction and Motivation
273(1)
10.2 History and First Pioneers
273(4)
10.3 Cellular Automata Models
277(10)
10.3.1 Motivation: Research Questions
279(2)
10.3.2 Design: Abstracting Conceptual and Formal Models
281(2)
10.3.3 Implementation: Cellular Automata Software
283(1)
10.3.4 Verification
284(1)
10.3.5 Validation
284(2)
10.3.6 Analysis
286(1)
10.4 Agent-Based Models
287(13)
10.4.1 Motivation: Research Questions
290(3)
10.4.2 Design: Abstracting Conceptual and Formal Models
293(2)
10.4.3 Implementation: Agent-Based Simulation Systems
295(2)
10.4.4 Verification
297(1)
10.4.5 Validation
297(2)
10.4.6 Analysis
299(1)
Recommended Readings
300(5)
On Cellular Automata
300(1)
On Agent-Based Models
301(4)
References 305(8)
Index 313
Dr. Claudio Cioffi-Revilla is Professor of Computational Social Science, founding and former Chair of the Department of Computational Social Science and founding and current Director of the Center for Social Complexity at George Mason University, VA, USA.