Muutke küpsiste eelistusi

Introduction to Computational Social Science: Principles and Applications Softcover reprint of the original 2nd ed. 2017 [Pehme köide]

  • Formaat: Paperback / softback, 607 pages, kõrgus x laius: 235x155 mm, 21 Illustrations, color; 38 Illustrations, black and white; XXXVI, 607 p. 59 illus., 21 illus. in color., 1 Paperback / softback
  • Sari: Texts in Computer Science
  • Ilmumisaeg: 13-May-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319843249
  • ISBN-13: 9783319843247
Teised raamatud teemal:
  • Pehme köide
  • Hind: 81,12 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 95,44 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
Introduction to Computational Social Science: Principles and Applications Softcover reprint of the original 2nd ed. 2017Suurem pilt
  • Formaat: Paperback / softback, 607 pages, kõrgus x laius: 235x155 mm, 21 Illustrations, color; 38 Illustrations, black and white; XXXVI, 607 p. 59 illus., 21 illus. in color., 1 Paperback / softback
  • Sari: Texts in Computer Science
  • Ilmumisaeg: 13-May-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319843249
  • ISBN-13: 9783319843247
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319843247.html
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 textbook provides a comprehensive and reader-friendly introduction to the field of computational social science (CSS). Presenting a unified treatment, the text examines in detail the four key methodological approaches of automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. This updated new edition has been enhanced with numerous review questions and exercises to test what has been learned, deepen understanding through problem-solving, and to practice writing code to implement ideas. Topics and features: contains more than a thousand questions and exercises, together with a list of acronyms and a glossary; examines the similarities and differences between computers and social systems; presents a focus on automated information extraction; discusses the measurement, scientific laws, and generative theories of social complexity in CSS; reviews the methodology of social simulations, covering both variable- and object-oriented models.

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.

Arvustused

This book is organized in a rigorous manner: each chapter includes an introductory abstract, a short chronology of the main achievements related to the chapters topic, well-balanced formalized-intuitive knowledge content, a significant number of questions and finally a list of future readings. I think Claudio Cioffi-Revillas work hits its assumed target: to be an affordable textbook for students and, at the same time, a useful support manual for instructors interested in learning or teaching computational social science. (Valentin V. Inceu, Computing Reviews, February, 2019)

This well-organized book provides a timely and comprehensive systematic introduction to CSS. The chapters are relatively independent. Therefore, readers may quickly grasp related information by reading chapters selectively. this book is intended as a CSS textbook for graduate students . (Chenyi Hu, Computing Reviews, August 11, 2014)









Preface to the Second Edition vii
Preface to the First Edition ix
Acknowledgements xiii
Acronyms xxiii
List of Figures
xxvii
List of Tables
xxxv
1 Introduction
1(34)
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 Versus 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(2)
1.5.2 Tripartite Ontology of Natural, Human, and Artificial Systems
9(1)
1.5.3 Simon's Theory of Artifacts: Explaining Basic Social Complexity
10(1)
1.5.4 Civilization, Complexity, and Quality of Life: Role of Artificial Systems
11(1)
1.6 Main Areas of CSS: An Overview
12(6)
1.6.1 Automated Social Information Extraction
13(1)
1.6.2 Social Networks
14(1)
1.6.3 Social Complexity
14(1)
1.6.4 Social Simulation Modeling
15(3)
1.7 A Brief History of CSS
18(2)
1.8 Main Learning Objectives
20(1)
Problems
21(5)
Exercises
26(6)
Recommended Readings
32(3)
2 Computation and Social Science
35(68)
2.1 Introduction and Motivation
35(1)
2.2 History and First Pioneers
36(1)
2.3 Computers and Programs
37(3)
2.3.1 Structure and Functioning of a Computer
37(3)
2.3.2 Compilers and Interpreters
40(1)
2.4 Computer Languages
40(5)
2.5 Operators, Statements, and Control Flow
45(2)
2.6 Coding Style
47(1)
2.7 Abstraction, Representation, and Notation
48(5)
2.8 Objects, Classes, and Dynamics in Unified Modeling Language (UML)
53(21)
2.8.1 Ontology
53(4)
2.8.2 The Unified Modeling Language (UML)
57(11)
2.8.3 Attributes
68(3)
2.8.4 Operations
71(3)
2.9 Data Structures
74(2)
2.10 Modules and Modularization
76(1)
2.11 Computability and Complexity
77(1)
2.12 Algorithms
78(2)
Problems
80(13)
Exercises
93(8)
Recommended Readings
101(2)
3 Automated Information Extraction
103(38)
3.1 Introduction and Motivation
103(1)
3.2 History and First Pioneers
104(3)
3.3 Linguistics and Principles of Content Analysis: Semantics and Syntax
107(2)
3.4 Semantic Dimensions of Meaning: From Osgood to Heise
109(3)
3.4.1 EPA-Space and the Structure of Human Information Processing and Meaning
109(2)
3.4.2 Cross-Cultural Universality of Meaning
111(1)
3.5 Data Mining: Overview
112(2)
3.6 Data Mining: Methodological Process
114(10)
3.6.1 Research Questions
115(1)
3.6.2 Source Data: Selection and Procurement
116(1)
3.6.3 Preprocessing Preparations
116(1)
3.6.4 Analysis
117(7)
3.6.5 Communication
124(1)
Problems
124(9)
Exercises
133(6)
Recommended Readings
139(2)
4 Social Networks
141(52)
4.1 Introduction and Motivation
141(1)
4.2 History and First Pioneers
142(5)
4.3 Definition of a Network
147(5)
4.3.1 A Social Network as a Class Object
148(1)
4.3.2 Relational Types of Social Networks
149(1)
4.3.3 Level of Analysis
150(1)
4.3.4 Dynamic Networks
151(1)
4.4 Elementary Social Network Structures
152(3)
4.5 The Network Matrix
155(1)
4.6 Quantitative Measures of a Social Network
155(3)
4.6.1 Nodal Measures: Micro Level
156(1)
4.6.2 Network Measures: Macro-Level
157(1)
4.7 Dynamic (Actually, Kinetic) Networks as Ternary Associations
158(1)
4.8 Applications
159(9)
4.8.1 Human Cognition and Belief Systems
159(4)
4.8.2 Decision-Making Models
163(1)
4.8.3 Organizations and Meta-Models
163(2)
4.8.4 Supply Chains
165(2)
4.8.5 The Social Structure of Small Worlds
167(1)
4.8.6 International Relations
167(1)
4.9 Software for SNA
168(2)
Problems
170(12)
Exercises
182(9)
Recommended Readings
191(2)
5 Social Complexity I: Origins and Measurement
193(54)
5.1 Introduction and Motivation
193(1)
5.2 History and First Pioneers
193(3)
5.3 Origins and Evolution of Social Complexity
196(9)
5.3.1 Sociogenesis: The "Big Four" Primary Polity Networks
197(4)
5.3.2 Social Complexity Elsewhere: Secondary Polity Networks
201(1)
5.3.3 Contemporary Social Complexity: Globalization
202(1)
5.3.4 Future Social Complexity
203(2)
5.4 Conceptual Foundations
205(4)
5.4.1 What Is Social Complexity?
205(1)
5.4.2 Defining Features of Social Complexity
206(3)
5.5 Measurement of Social Complexity
209(10)
5.5.1 Qualitative Indicators: Lines of Evidence
210(2)
5.5.2 Quantitative Indicators
212(7)
Problems
219(15)
Exercises
234(11)
Recommended Readings
245(2)
6 Social Complexity II: Laws
247(44)
6.1 Introduction and Motivation
247(1)
6.2 History and First Pioneers
247(2)
6.3 Laws of Social Complexity: Descriptions
249(15)
6.3.1 Structural Laws: Serial, Parallel, and Hybrid Complexity
249(6)
6.3.2 Distributional Laws: Scaling and Nonequilibrium Complexity
255(9)
6.4 Power Law Analysis
264(8)
6.4.1 Empirical Analysis: Estimation and Assessing Goodness of Fit
264(3)
6.4.2 Theoretical Analysis: Deriving Implications
267(5)
6.5 Universality in Laws of Social Complexity
272(1)
Problems
272(8)
Exercises
280(8)
Recommended Readings
288(3)
7 Social Complexity HI: Theories
291(84)
7.1 Introduction and Motivation
291(1)
7.2 History and First Pioneers
291(3)
7.3 Theories of Social Complexity: Elements of Explanation
294(10)
7.3.1 Sequentiality: Modeling Processes. Forward Logic
295(4)
7.3.2 Conditionality: Modeling Causes. Backward Logic
299(4)
7.3.3 Hybrid Bimodal Social Complexity: Several-Among-Some Causes
303(1)
7.4 Explaining Initial Social Complexity
304(24)
7.4.1 Emergence of Chiefdoms
310(9)
7.4.2 Emergence of States
319(9)
7.5 General Theories of Social Complexity
328(13)
7.5.1 Theory of Collective Action
328(3)
7.5.2 Simon's Theory of Adaptation via Artifacts
331(4)
7.5.3 Canonical Theory as a Unified Framework
335(6)
Problems
341(19)
Exercises
360(11)
Recommended Readings
371(4)
8 Simulations I: Methodology
375(40)
8.1 Introduction and Motivation
375(1)
8.2 History and First Pioneers
376(1)
8.3 Purpose of Simulation: Investigating Social Complexity Via Virtual Worlds
377(2)
8.4 Basic Simulation Terminology
379(3)
8.5 Fidelity of Representation and Implications
382(1)
8.6 Types of Social Simulation: From System Dynamics to Agent-Based Models
383(1)
8.7 Development Methodology of Social Simulations
384(7)
8.7.1 Motivation: What Are the Research Questions Addressed by a Given Model?
384(2)
8.7.2 Conceptual Design: What Does the Abstraction Look Like?
386(1)
8.7.3 Implementation: How Is the Abstracted Model Written in Code?
387(1)
8.7.4 Verification: Does the Simulation Perform as Intended?
388(1)
8.7.5 Validation: Can We Trust the Results?
389(1)
8.7.6 Virtual Experiments and Scenario Analyses: What New Information Does the Simulation Generate?
390(1)
8.8 Assessing the Quality of a Social Simulation
391(5)
8.8.1 General Principles for Social Modeling Assessment
391(2)
8.8.2 Dimensions of Quality in Social Simulation Models
393(3)
8.9 Methodology of Complex Social Simulations
396(2)
8.10 Comparing Simulations: How Are Computational Models Compared?
398(2)
Problems
400(8)
Exercises
408(5)
Recommended Readings
413(2)
9 Simulations II: Variable-Oriented Models
415(40)
9.1 Introduction and Motivation
415(1)
9.2 History and First Pioneers
415(2)
9.3 System Dynamics Models
417(12)
9.3.1 Motivation: Research Questions
419(1)
9.3.2 Design: Abstracting Conceptual and Formal Models
419(6)
9.3.3 Implementation: System Dynamics Software
425(1)
9.3.4 Verification
426(1)
9.3.5 Validation
426(1)
9.3.6 Analysis
427(2)
9.4 Queueing Models
429(8)
9.4.1 Motivation: Research Questions
429(3)
9.4.2 Design: Abstracting Conceptual and Formal Models
432(3)
9.4.3 Implementation: Queuing Systems Software
435(1)
9.4.4 Verification
435(1)
9.4.5 Validation
436(1)
9.4.6 Analysis
436(1)
Problems
437(8)
Exercises
445(8)
Recommended Readings
453(2)
10 Simulations HI: Object-Oriented Models
455(58)
10.1 Introduction and Motivation
455(1)
10.2 History and First Pioneers
455(4)
10.3 Cellular Automata Models
459(11)
10.3.1 Motivation: Research Questions
463(1)
10.3.2 Design: Abstracting Conceptual and Formal Models
463(3)
10.3.3 Implementation: Cellular Automata Software
466(2)
10.3.4 Verification
468(1)
10.3.5 Validation
468(1)
10.3.6 Analysis
469(1)
10.4 Agent-Based Models
470(14)
10.4.1 Motivation: Research Questions
473(3)
10.4.2 Design: Abstracting Conceptual and Formal Models
476(3)
10.4.3 Implementation: Agent-Based Simulation Systems
479(3)
10.4.4 Verification
482(1)
10.4.5 Validation
482(1)
10.4.6 Analysis
483(1)
Problems
484(12)
Exercises
496(12)
Recommended Readings
508(5)
Answers to Problems 513(30)
Glossary 543(42)
References 585(8)
Author Index 593(8)
Subject Index 601
Dr. Claudio Cioffi-Revilla is University Professor and 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.