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Behavioral Computational Social Science [Kõva köide]

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(Economics and Market Analysis Team, Energy and Infrastructure Analysis Group, Los Alasmos National Laboratory, USA)
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Behavioral Computational Social ScienceSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781118657300.html
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This book is organized in two parts: the first part introduces the reader to all the concepts, tools and references that are required to start conducting research in behavioral computational social science. The methodological reasons for integrating the two approaches are also presented from the individual and separated viewpoints of the two approaches.The second part of the book, presents all the advanced methodological and technical aspects that are relevant for the proposed integration. Several contributions which effectively merge the computational and the behavioral approaches are presented and discussed throughout
Preface ix
1 Introduction: Toward behavioral computational social science
1(6)
1.1 Research strategies in CSS
2(1)
1.2 Why behavioral CSS
3(1)
1.3 Organization of the book
4(3)
PART I CONCEPTS AND METHODS
7(50)
2 Explanation in computational social science
9(22)
2.1 Concepts
10(9)
2.1.1 Causality
10(8)
2.1.2 Data
18(1)
2.2 Methods
19(6)
2.2.1 ABMs
19(3)
2.2.2 Statistical mechanics, system dynamics, and cellular automata
22(3)
2.3 Tools
25(2)
2.4 Critical issues: Uncertainty, model communication
27(4)
3 Observation and explanation in behavioral sciences
31(12)
3.1 Concepts
32(3)
3.2 Observation methods
35(3)
3.2.1 Naturalistic observation and case studies
35(1)
3.2.2 Surveys
36(1)
3.2.3 Experiments and quasiexperiments
37(1)
3.3 Tools
38(2)
3.4 Critical issues: Induced responses, external validity, and replicability
40(3)
4 Reasons for integration
43(14)
4.1 The perspective of agent-based modelers
44(5)
4.2 The perspective of behavioral social scientists
49(5)
4.3 The perspective of social sciences in general
54(3)
PART II BEHAVIORAL COMPUTATIONAL SOCIAL SCIENCE IN PRACTICE
57(84)
5 Behavioral agents
59(14)
5.1 Measurement scales of data
61(2)
5.2 Model calibration
63(4)
5.2.1 Single decision variable and simple decision function
63(2)
5.2.2 Multiple decision variables and multilevel decision trees
65(2)
5.3 Model classification
67(3)
5.4 Critical issues: Validation, uncertainty modeling
70(3)
6 Sophisticated agents
73(18)
6.1 Common features of sophisticated agents
75(1)
6.2 Cognitive processes
75(9)
6.2.1 Reinforcement learning
76(4)
6.2.2 Other models of bounded rationality
80(1)
6.2.3 Nature-inspired algorithms
80(4)
6.3 Cognitive structures
84(4)
6.3.1 Middle-level structures
85(1)
6.3.2 Rich cognitive models
86(2)
6.4 Critical issues: Calibration, validation, robustness, social interface
88(3)
7 Social networks and other interaction structures
91(18)
7.1 Essential elements of SNA
93(6)
7.2 Models for the generation of social networks
99(5)
7.3 Other kinds of interaction structures
104(2)
7.4 Critical issues: Time and behavior
106(3)
8 An example of application
109(32)
8.1 The social dilemma
110(4)
8.1.1 The theory
111(2)
8.1.2 Evidence
113(1)
8.1.3 Our research agenda
114(1)
8.2 The original experiment
114(2)
8.3 Behavioral agents
116(11)
8.3.1 Fixed effects model
116(1)
8.3.2 Random coefficients model
117(1)
8.3.3 First differences model
118(1)
8.3.4 Ordered probit model with individual dummies
119(2)
8.3.5 Multilevel decision trees
121(5)
8.3.6 Classified heuristics
126(1)
8.4 Learning agents
127(1)
8.5 Interaction structures
127(1)
8.6 Results: Answers to a few research questions
128(10)
8.6.1 Are all models of agents capable of replicating the experiment?
129(2)
8.6.2 Was the experiment influenced by chance?
131(2)
8.6.3 Do economic incentives work?
133(2)
8.6.4 Why does increasing group size generate more cooperation?
135(1)
8.6.5 What happens with longer interaction?
136(1)
8.6.6 Does a realistic social network promote cooperation?
137(1)
8.7 Conclusions
138(3)
Appendix Technical guide to the example model
141(32)
A.1 The interface
142(3)
A.2 The code
145(28)
A.2.1 Variable declaration
146(6)
A.2.2 Simulation setup
152(5)
A.2.3 Running the simulation
157(1)
A.2.4 Decision-making
157(8)
A.2.5 Updating interaction structure and other variables
165(8)
References 173(14)
Index 187
Riccardo Boero, Economics and Market Analysis Team, Energy and Infrastructure Analysis Group, Los Alasmos National Laboratory, USA