Riccardo Boero
Behavioral Computational Social Science
Riccardo Boero
Behavioral Computational Social Science
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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…mehr
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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
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Wiley Series in Computational and Quantitative Social Science
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 200
- Erscheinungstermin: 28. September 2015
- Englisch
- Abmessung: 231mm x 147mm x 15mm
- Gewicht: 386g
- ISBN-13: 9781118657300
- ISBN-10: 1118657306
- Artikelnr.: 42429755
- Wiley Series in Computational and Quantitative Social Science
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 200
- Erscheinungstermin: 28. September 2015
- Englisch
- Abmessung: 231mm x 147mm x 15mm
- Gewicht: 386g
- ISBN-13: 9781118657300
- ISBN-10: 1118657306
- Artikelnr.: 42429755
Riccardo Boero, Economics and Market Analysis Team, Energy and Infrastructure Analysis Group, Los Alasmos National Laboratory, USA
Preface ix 1 Introduction: Toward behavioral computational social science 1
1.1 Research strategies in CSS 2 1.2 Why behavioral CSS 3 1.3 Organization
of the book 4 Part i CONCEPTS AND METHODS 7 2 Explanation in computational
social science 9 2.1 Concepts 10 2.1.1 Causality 10 2.1.2 Data 18 2.2
Methods 19 2.2.1 ABMs 19 2.2.2 Statistical mechanics, system dynamics, and
cellular automata 22 2.3 Tools 25 2.4 Critical issues: Uncertainty, model
communication 27 3 Observation and explanation in behavioral sciences 31
3.1 Concepts 32 3.2 Observation methods 35 3.2.1 Naturalistic observation
and case studies 35 3.2.2 Surveys 36 3.2.3 Experiments and quasiexperiments
37 3.3 Tools 38 3.4 Critical issues: Induced responses, external validity,
and replicability 40 4 Reasons for integration 43 4.1 The perspective of
agent?]based modelers 44 4.2 The perspective of behavioral social
scientists 49 4.3 The perspective of social sciences in general 54 Part iI
BEHAVIORAL COMPUTATIONAL SOCIAL SCIENCE IN PRACTICE 57 5 Behavioral agents
59 5.1 Measurement scales of data 61 5.2 Model calibration 63 5.2.1 Single
decision variable and simple decision function 63 5.2.2 Multiple decision
variables and multilevel decision trees 65 5.3 Model classification 67 5.4
Critical issues: Validation, uncertainty modeling 70 6 Sophisticated agents
73 6.1 Common features of sophisticated agents 75 6.2 Cognitive processes
75 6.2.1 Reinforcement learning 76 6.2.2 Other models of bounded
rationality 80 6.2.3 Nature?]inspired algorithms 80 6.3 Cognitive
structures 84 6.3.1 Middle?]level structures 85 6.3.2 Rich cognitive models
86 6.4 Critical issues: Calibration, validation, robustness, social
interface 88 7 Social networks and other interaction structures 91 7.1
Essential elements of SNA 93 7.2 Models for the generation of social
networks 99 7.3 Other kinds of interaction structures 104 7.4 Critical
issues: Time and behavior 106 8 An example of application 109 8.1 The
social dilemma 110 8.1.1 The theory 111 8.1.2 Evidence 113 8.1.3 Our
research agenda 114 8.2 The original experiment 114 8.3 Behavioral agents
116 8.3.1 Fixed effects model 116 8.3.2 Random coefficients model 117 8.3.3
First differences model 118 8.3.4 Ordered probit model with individual
dummies 119 8.3.5 Multilevel decision trees 121 8.3.6 Classified heuristics
126 8.4 Learning agents 127 8.5 Interaction structures 127 8.6 Results:
Answers to a few research questions 128 8.6.1 Are all models of agents
capable of replicating the experiment? 129 8.6.2 Was the experiment
influenced by chance? 131 8.6.3 Do economic incentives work? 133 8.6.4 Why
does increasing group size generate more cooperation? 135 8.6.5 What
happens with longer interaction? 136 8.6.6 Does a realistic social network
promote cooperation? 137 8.7 Conclusions 138 Appendix Technical guide to
the example model 141 A.1 The interface 142 A.2 The code 145 A.2.1 Variable
declaration 146 A.2.2 Simulation setup 152 A.2.3 Running the simulation 157
A.2.4 Decision-making 157 A.2.5 Updating interaction structure and other
variables 165 References 173 Index 187
1.1 Research strategies in CSS 2 1.2 Why behavioral CSS 3 1.3 Organization
of the book 4 Part i CONCEPTS AND METHODS 7 2 Explanation in computational
social science 9 2.1 Concepts 10 2.1.1 Causality 10 2.1.2 Data 18 2.2
Methods 19 2.2.1 ABMs 19 2.2.2 Statistical mechanics, system dynamics, and
cellular automata 22 2.3 Tools 25 2.4 Critical issues: Uncertainty, model
communication 27 3 Observation and explanation in behavioral sciences 31
3.1 Concepts 32 3.2 Observation methods 35 3.2.1 Naturalistic observation
and case studies 35 3.2.2 Surveys 36 3.2.3 Experiments and quasiexperiments
37 3.3 Tools 38 3.4 Critical issues: Induced responses, external validity,
and replicability 40 4 Reasons for integration 43 4.1 The perspective of
agent?]based modelers 44 4.2 The perspective of behavioral social
scientists 49 4.3 The perspective of social sciences in general 54 Part iI
BEHAVIORAL COMPUTATIONAL SOCIAL SCIENCE IN PRACTICE 57 5 Behavioral agents
59 5.1 Measurement scales of data 61 5.2 Model calibration 63 5.2.1 Single
decision variable and simple decision function 63 5.2.2 Multiple decision
variables and multilevel decision trees 65 5.3 Model classification 67 5.4
Critical issues: Validation, uncertainty modeling 70 6 Sophisticated agents
73 6.1 Common features of sophisticated agents 75 6.2 Cognitive processes
75 6.2.1 Reinforcement learning 76 6.2.2 Other models of bounded
rationality 80 6.2.3 Nature?]inspired algorithms 80 6.3 Cognitive
structures 84 6.3.1 Middle?]level structures 85 6.3.2 Rich cognitive models
86 6.4 Critical issues: Calibration, validation, robustness, social
interface 88 7 Social networks and other interaction structures 91 7.1
Essential elements of SNA 93 7.2 Models for the generation of social
networks 99 7.3 Other kinds of interaction structures 104 7.4 Critical
issues: Time and behavior 106 8 An example of application 109 8.1 The
social dilemma 110 8.1.1 The theory 111 8.1.2 Evidence 113 8.1.3 Our
research agenda 114 8.2 The original experiment 114 8.3 Behavioral agents
116 8.3.1 Fixed effects model 116 8.3.2 Random coefficients model 117 8.3.3
First differences model 118 8.3.4 Ordered probit model with individual
dummies 119 8.3.5 Multilevel decision trees 121 8.3.6 Classified heuristics
126 8.4 Learning agents 127 8.5 Interaction structures 127 8.6 Results:
Answers to a few research questions 128 8.6.1 Are all models of agents
capable of replicating the experiment? 129 8.6.2 Was the experiment
influenced by chance? 131 8.6.3 Do economic incentives work? 133 8.6.4 Why
does increasing group size generate more cooperation? 135 8.6.5 What
happens with longer interaction? 136 8.6.6 Does a realistic social network
promote cooperation? 137 8.7 Conclusions 138 Appendix Technical guide to
the example model 141 A.1 The interface 142 A.2 The code 145 A.2.1 Variable
declaration 146 A.2.2 Simulation setup 152 A.2.3 Running the simulation 157
A.2.4 Decision-making 157 A.2.5 Updating interaction structure and other
variables 165 References 173 Index 187
Preface ix 1 Introduction: Toward behavioral computational social science 1
1.1 Research strategies in CSS 2 1.2 Why behavioral CSS 3 1.3 Organization
of the book 4 Part i CONCEPTS AND METHODS 7 2 Explanation in computational
social science 9 2.1 Concepts 10 2.1.1 Causality 10 2.1.2 Data 18 2.2
Methods 19 2.2.1 ABMs 19 2.2.2 Statistical mechanics, system dynamics, and
cellular automata 22 2.3 Tools 25 2.4 Critical issues: Uncertainty, model
communication 27 3 Observation and explanation in behavioral sciences 31
3.1 Concepts 32 3.2 Observation methods 35 3.2.1 Naturalistic observation
and case studies 35 3.2.2 Surveys 36 3.2.3 Experiments and quasiexperiments
37 3.3 Tools 38 3.4 Critical issues: Induced responses, external validity,
and replicability 40 4 Reasons for integration 43 4.1 The perspective of
agent?]based modelers 44 4.2 The perspective of behavioral social
scientists 49 4.3 The perspective of social sciences in general 54 Part iI
BEHAVIORAL COMPUTATIONAL SOCIAL SCIENCE IN PRACTICE 57 5 Behavioral agents
59 5.1 Measurement scales of data 61 5.2 Model calibration 63 5.2.1 Single
decision variable and simple decision function 63 5.2.2 Multiple decision
variables and multilevel decision trees 65 5.3 Model classification 67 5.4
Critical issues: Validation, uncertainty modeling 70 6 Sophisticated agents
73 6.1 Common features of sophisticated agents 75 6.2 Cognitive processes
75 6.2.1 Reinforcement learning 76 6.2.2 Other models of bounded
rationality 80 6.2.3 Nature?]inspired algorithms 80 6.3 Cognitive
structures 84 6.3.1 Middle?]level structures 85 6.3.2 Rich cognitive models
86 6.4 Critical issues: Calibration, validation, robustness, social
interface 88 7 Social networks and other interaction structures 91 7.1
Essential elements of SNA 93 7.2 Models for the generation of social
networks 99 7.3 Other kinds of interaction structures 104 7.4 Critical
issues: Time and behavior 106 8 An example of application 109 8.1 The
social dilemma 110 8.1.1 The theory 111 8.1.2 Evidence 113 8.1.3 Our
research agenda 114 8.2 The original experiment 114 8.3 Behavioral agents
116 8.3.1 Fixed effects model 116 8.3.2 Random coefficients model 117 8.3.3
First differences model 118 8.3.4 Ordered probit model with individual
dummies 119 8.3.5 Multilevel decision trees 121 8.3.6 Classified heuristics
126 8.4 Learning agents 127 8.5 Interaction structures 127 8.6 Results:
Answers to a few research questions 128 8.6.1 Are all models of agents
capable of replicating the experiment? 129 8.6.2 Was the experiment
influenced by chance? 131 8.6.3 Do economic incentives work? 133 8.6.4 Why
does increasing group size generate more cooperation? 135 8.6.5 What
happens with longer interaction? 136 8.6.6 Does a realistic social network
promote cooperation? 137 8.7 Conclusions 138 Appendix Technical guide to
the example model 141 A.1 The interface 142 A.2 The code 145 A.2.1 Variable
declaration 146 A.2.2 Simulation setup 152 A.2.3 Running the simulation 157
A.2.4 Decision-making 157 A.2.5 Updating interaction structure and other
variables 165 References 173 Index 187
1.1 Research strategies in CSS 2 1.2 Why behavioral CSS 3 1.3 Organization
of the book 4 Part i CONCEPTS AND METHODS 7 2 Explanation in computational
social science 9 2.1 Concepts 10 2.1.1 Causality 10 2.1.2 Data 18 2.2
Methods 19 2.2.1 ABMs 19 2.2.2 Statistical mechanics, system dynamics, and
cellular automata 22 2.3 Tools 25 2.4 Critical issues: Uncertainty, model
communication 27 3 Observation and explanation in behavioral sciences 31
3.1 Concepts 32 3.2 Observation methods 35 3.2.1 Naturalistic observation
and case studies 35 3.2.2 Surveys 36 3.2.3 Experiments and quasiexperiments
37 3.3 Tools 38 3.4 Critical issues: Induced responses, external validity,
and replicability 40 4 Reasons for integration 43 4.1 The perspective of
agent?]based modelers 44 4.2 The perspective of behavioral social
scientists 49 4.3 The perspective of social sciences in general 54 Part iI
BEHAVIORAL COMPUTATIONAL SOCIAL SCIENCE IN PRACTICE 57 5 Behavioral agents
59 5.1 Measurement scales of data 61 5.2 Model calibration 63 5.2.1 Single
decision variable and simple decision function 63 5.2.2 Multiple decision
variables and multilevel decision trees 65 5.3 Model classification 67 5.4
Critical issues: Validation, uncertainty modeling 70 6 Sophisticated agents
73 6.1 Common features of sophisticated agents 75 6.2 Cognitive processes
75 6.2.1 Reinforcement learning 76 6.2.2 Other models of bounded
rationality 80 6.2.3 Nature?]inspired algorithms 80 6.3 Cognitive
structures 84 6.3.1 Middle?]level structures 85 6.3.2 Rich cognitive models
86 6.4 Critical issues: Calibration, validation, robustness, social
interface 88 7 Social networks and other interaction structures 91 7.1
Essential elements of SNA 93 7.2 Models for the generation of social
networks 99 7.3 Other kinds of interaction structures 104 7.4 Critical
issues: Time and behavior 106 8 An example of application 109 8.1 The
social dilemma 110 8.1.1 The theory 111 8.1.2 Evidence 113 8.1.3 Our
research agenda 114 8.2 The original experiment 114 8.3 Behavioral agents
116 8.3.1 Fixed effects model 116 8.3.2 Random coefficients model 117 8.3.3
First differences model 118 8.3.4 Ordered probit model with individual
dummies 119 8.3.5 Multilevel decision trees 121 8.3.6 Classified heuristics
126 8.4 Learning agents 127 8.5 Interaction structures 127 8.6 Results:
Answers to a few research questions 128 8.6.1 Are all models of agents
capable of replicating the experiment? 129 8.6.2 Was the experiment
influenced by chance? 131 8.6.3 Do economic incentives work? 133 8.6.4 Why
does increasing group size generate more cooperation? 135 8.6.5 What
happens with longer interaction? 136 8.6.6 Does a realistic social network
promote cooperation? 137 8.7 Conclusions 138 Appendix Technical guide to
the example model 141 A.1 The interface 142 A.2 The code 145 A.2.1 Variable
declaration 146 A.2.2 Simulation setup 152 A.2.3 Running the simulation 157
A.2.4 Decision-making 157 A.2.5 Updating interaction structure and other
variables 165 References 173 Index 187