Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction: Toward behavioral computational social science -- 1.1 Research strategies in CSS -- 1.2 Why behavioral CSS -- 1.3 Organization of the book -- PART I CONCEPTS AND METHODS -- Chapter 2 Explanation in computational social science -- 2.1 Concepts -- 2.1.1 Causality -- 2.1.2 Data -- 2.2 Methods -- 2.2.1 ABMs -- 2.2.2 Statistical mechanics, system dynamics, and cellular automata -- 2.3 Tools -- 2.4 Critical issues: Uncertainty, model communication -- Chapter 3 Observation and explanation in behavioral sciences -- 3.1 Concepts -- 3.2 Observation methods -- 3.2.1 Naturalistic observation and case studies -- 3.2.2 Surveys -- 3.2.3 Experiments and quasiexperiments -- 3.3 Tools -- 3.4 Critical issues: Induced responses, external validity, and replicability -- Chapter 4 Reasons for integration -- 4.1 The perspective of agent-based modelers -- 4.2 The perspective of behavioral social scientists -- 4.3 The perspective of social sciences in general -- PART II BEHAVIORAL COMPUTATIONAL SOCIAL SCIENCE IN PRACTICE -- Chapter 5 Behavioral agents -- 5.1 Measurement scales of data -- 5.2 Model calibration -- 5.2.1 Single decision variable and simple decision function -- 5.2.2 Multiple decision variables and multilevel decision trees -- 5.3 Model classification -- 5.4 Critical issues: Validation, uncertainty modeling -- Chapter 6 Sophisticated agents -- 6.1 Common features of sophisticated agents -- 6.2 Cognitive processes -- 6.2.1 Reinforcement learning -- 6.2.2 Other models of bounded rationality -- 6.2.3 Nature-inspired algorithms -- 6.3 Cognitive structures -- 6.3.1 Middle-level structures -- 6.3.2 Rich cognitive models -- 6.4 Critical issues: Calibration, validation, robustness, social interface -- Chapter 7 Social networks and other interaction structures.

7.1 Essential elements of SNA -- 7.2 Models for the generation of social networks -- 7.3 Other kinds of interaction structures -- 7.4 Critical issues: Time and behavior -- Chapter 8 An example of application -- 8.1 The social dilemma -- 8.1.1 The theory -- 8.1.2 Evidence -- 8.1.3 Our research agenda -- 8.2 The original experiment -- 8.3 Behavioral agents -- 8.3.1 Fixed effects model -- 8.3.2 Random coefficients model -- 8.3.3 First differences model -- 8.3.4 Ordered probit model with individual dummies -- 8.3.5 Multilevel decision trees -- 8.3.6 Classified heuristics -- 8.4 Learning agents -- 8.5 Interaction structures -- 8.6 Results: Answers to a few research questions -- 8.6.1 Are all models of agents capable of replicating the experiment? -- 8.6.2 Was the experiment influenced by chance? -- 8.6.3 Do economic incentives work? -- 8.6.4 Why does increasing group size generate more cooperation? -- 8.6.5 What happens with longer interaction? -- 8.6.6 Does a realistic social network promote cooperation? -- 8.7 Conclusions -- Appendix Technical guide to the example model -- A.1 The interface -- A.2 The code -- A.2.1 Variable declaration -- A.2.2 Simulation setup -- A.2.3 Running the simulation -- A.2.4 Decision-making -- A.2.5 Updating interaction structure and other variables -- References -- Index -- EULA.