| Foreword |
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xxvii | |
| List of Contributors |
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xxxi | |
| About the Editors |
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xli | |
| Part I: Introduction and Agenda |
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1 | (62) |
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1 Understanding and Improving the Human Condition: A Vision of the Future for Social-Behavioral Modeling |
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3 | (12) |
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5 | (5) |
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Challenge One: The Complexity of Human Issues |
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5 | (1) |
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Challenge Two: Fragmentation |
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6 | (1) |
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6 | (1) |
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7 | (1) |
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8 | (1) |
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9 | (1) |
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Challenge Three: Representations |
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9 | (1) |
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Challenge Four: Applications of Social-Behavioral Modeling |
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9 | (1) |
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10 | (3) |
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11 | (2) |
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13 | (2) |
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2 Improving Social-Behavioral Modeling |
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15 | (34) |
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15 | (2) |
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15 | (1) |
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16 | (1) |
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17 | (1) |
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17 | (3) |
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Individual Cognition and Behavior |
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17 | (1) |
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Social Systems as Complex Adaptive Systems (CAS) |
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18 | (1) |
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The Dynamic and Storytelling Character of People and Social Systems |
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19 | (1) |
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19 | (1) |
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Selected Specific Issues and the Need for Changed Practices |
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20 | (12) |
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Background on Fragmentation of SB Theories |
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20 | (1) |
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20 | (1) |
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Similarities and Differences |
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21 | (3) |
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Rebalancing the Portfolio of Models and Methods |
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24 | (1) |
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24 | (1) |
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Combination, Synthesis, and Integration |
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25 | (1) |
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Families of Multiresolution, Multiperspective Models |
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26 | (1) |
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27 | (1) |
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Connecting Theory with Evidence |
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28 | (1) |
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Rethinking Model Validity |
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28 | (1) |
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The Five Dimensions of Model Validity |
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28 | (1) |
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Assessing a Model's Validity in a Context |
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31 | (1) |
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Some General Criteria for Validation |
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32 | (1) |
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Strategy for Moving Ahead |
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32 | (7) |
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Tightening the Theory-Modeling-Experimentation Research Cycle |
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33 | (3) |
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Improving Theory and Related Modeling |
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36 | (3) |
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Social-Behavioral Laboratories |
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39 | (2) |
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41 | (1) |
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42 | (1) |
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42 | (7) |
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3 Ethical and Privacy Issues in Social-Behavioral Research |
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49 | (14) |
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Improved Notice and Choice |
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50 | (2) |
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50 | (1) |
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51 | (1) |
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Usable and Accurate Access Control |
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52 | (1) |
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52 | (1) |
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53 | (1) |
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53 | (2) |
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53 | (1) |
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54 | (1) |
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Avoiding Harms by Validating Algorithms and Auditing Use |
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55 | (1) |
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55 | (1) |
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55 | (1) |
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56 | (1) |
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56 | (1) |
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56 | (1) |
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57 | (1) |
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57 | (1) |
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57 | (1) |
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And Finally Thinking Bigger About What Is Possible |
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58 | (1) |
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59 | (4) |
| Part II: Foundations of Social-Behavioral Science |
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63 | (216) |
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4 Building on Social Science: Theoretic Foundations for Modelers |
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65 | (36) |
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65 | (1) |
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Atomistic Theories of Individual Behavior |
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66 | (9) |
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66 | (3) |
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69 | (2) |
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71 | (1) |
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72 | (2) |
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Alternative Atomistic Theories of Individual Behavior |
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74 | (1) |
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Social Theories of Individual Behavior |
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75 | (5) |
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75 | (1) |
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76 | (1) |
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Norms as Social Expectation |
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77 | (1) |
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Norms as Moral and Ethical Obligations |
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78 | (1) |
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The Relationship between Normative and Rationalist Explanations of Behavior |
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79 | (1) |
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80 | (8) |
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From Individual Behavior to Social Interaction |
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80 | (1) |
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Social Dilemmas and Collective Decision-Making with Common Interests |
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81 | (2) |
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Bargaining over Conflicting Interests |
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83 | (1) |
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Social Interaction and the Dynamics of Beliefs |
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84 | (2) |
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Social Interaction and the Dynamics of Identity and Culture |
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86 | (2) |
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From Theory to Data and Data to Models |
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88 | (4) |
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Building Models Based on Social Scientific Theories |
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92 | (2) |
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94 | (1) |
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94 | (7) |
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5 How Big and How Certain? A New Approach to Defining Levels of Analysis for Modeling Social Science Topics |
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101 | (20) |
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101 | (1) |
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Traditional Conceptions of Levels of Analysis |
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102 | (2) |
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Incompleteness of Levels of Analysis |
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104 | (3) |
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Constancy as the Missing Piece |
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107 | (4) |
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111 | (2) |
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Implications for Modeling |
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113 | (3) |
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116 | (1) |
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116 | (1) |
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116 | (5) |
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6 Toward Generative Narrative Models of the Course and Resolution of Conflict |
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121 | (24) |
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Limitations of Current Conceptualizations of Narrative |
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122 | (3) |
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A Generative Modeling Framework |
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125 | (1) |
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Application to a Simple Narrative |
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126 | (4) |
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130 | (3) |
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Challenges and Future Research |
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133 | (2) |
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133 | (1) |
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134 | (1) |
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135 | (1) |
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135 | (2) |
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137 | (1) |
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Locations, Events, Actions, Participants, and Things in the Three Little Pigs |
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137 | (2) |
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Edges in the Three Little Pigs Graph |
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139 | (3) |
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142 | (3) |
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7 A Neural Network Model of Motivated Decision-Making in Everyday Social Behavior |
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145 | (18) |
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145 | (1) |
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146 | (1) |
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Constraint Satisfaction Processing |
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147 | (1) |
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147 | (4) |
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148 | (1) |
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149 | (1) |
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Interoceptive or Bodily State |
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150 | (1) |
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150 | (1) |
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Competition Among Motives |
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151 | (1) |
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Motivation Changes Dynamically |
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151 | (1) |
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Neural Network Implementation |
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151 | (8) |
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General Processing in the Network |
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153 | (6) |
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159 | (1) |
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160 | (3) |
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8 Dealing with Culture as Inherited Information |
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163 | (24) |
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Galton's Problem as a Core Feature of Cultural Theory |
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163 | (4) |
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How to Correct for Treelike Inheritance of Traits Across Groups |
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167 | (6) |
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Early Attempts to Correct Galton's Problem |
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167 | (2) |
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More Recent Attempts to Correct Galton's Problem |
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169 | (4) |
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173 | (1) |
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Dealing with Nonindependence in Less Treelike Network Structures |
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173 | (5) |
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Determining Which Network Is Most Important for a Cultural Trait |
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174 | (2) |
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Correcting for Network Nonindependence When Testing Trait-Trait Correlations |
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176 | (1) |
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176 | (2) |
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Future Directions for Formal Modeling of Culture |
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178 | (3) |
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Improved Network Autoregression Implementations |
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178 | (1) |
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A Global Data Set of Expected Nonindependence to Solve Galton's Problem |
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179 | (1) |
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Better Collection of Behavioral Trait Variation Across Populations |
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180 | (1) |
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181 | (1) |
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181 | (6) |
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9 Social Media, Global Connections, and Information Environments: Building Complex Understandings of Multi-Actor Interactions |
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187 | (18) |
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A New Setting of Hyperconnectivity |
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187 | (1) |
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The Information Environment |
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188 | (1) |
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Social Media in the Information Environment |
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189 | (1) |
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Integrative Approaches to Understanding Human Behavior |
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190 | (2) |
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192 | (1) |
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192 | (1) |
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192 | (1) |
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The Ethnographic Examples |
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192 | (10) |
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Muddying the Waters: The Case of Cassandra |
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193 | (3) |
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Missing It: The Case of SSgt Michaels |
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196 | (2) |
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Wag the Dog: The Case of Fedor the Troll |
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198 | (4) |
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202 | (2) |
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204 | (1) |
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10 Using Neuroimaging to Predict Behavior: An Overview with a Focus on the Moderating Role of Sociocultural Context |
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205 | (26) |
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205 | (1) |
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The Brain-as-Predictor Approach |
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206 | (2) |
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Predicting Individual Behaviors |
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208 | (2) |
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Interpreting Associations Between Brain Activation and Behavior |
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210 | (1) |
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Predicting Aggregate Out-of-Sample Group Outcomes |
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211 | (3) |
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Predicting Social Interactions and Peer Influence |
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214 | (1) |
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215 | (4) |
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219 | (2) |
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221 | (1) |
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222 | (9) |
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11 Social Models from Non-Human Systems |
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231 | (32) |
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Emergent Patterns in Groups of Behaviorally Flexible Individuals |
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232 | (7) |
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From Bird Motivations to Human Applications |
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234 | (1) |
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Game-Theoretic Model of Frequency-Dependent Tactic Choice |
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234 | (1) |
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Mathematical Model as Behavioral Microscope on Carefully Prepared Birds |
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235 | (2) |
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Transferable Insights from Behavioral Games to Human Groups |
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237 | (2) |
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Model Systems for Understanding Group Competition |
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239 | (7) |
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Social Spiders as Model Systems for Understanding Personality in Groups |
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240 | (2) |
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Ants as Model Systems for Understanding the Costs and Benefits of Specialization |
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242 | (2) |
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Personality and Specialization: From Nonhuman to Human Groups |
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244 | (2) |
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Information Dynamics in Tightly Integrated Groups |
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246 | (8) |
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Linear and Nonlinear Recruitment Dynamics |
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247 | (2) |
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Herd Behavior and Information Cascades in Ants |
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249 | (2) |
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From Ants to Human Decision Support Systems |
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251 | (1) |
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Additional Examples: Rationality and Memory |
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252 | (2) |
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254 | (1) |
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255 | (1) |
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255 | (8) |
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12 Moving Social-Behavioral Modeling Forward: Insights from Social Scientists |
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263 | (16) |
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Why Do People Do What They Do? |
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264 | (1) |
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Everything Old Is New Again |
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264 | (3) |
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Behavior Is Social, Not Just Complex |
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267 | (3) |
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270 | (2) |
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272 | (3) |
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275 | (1) |
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276 | (3) |
| Part III: Informing Models with Theory and Data |
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279 | (242) |
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13 Integrating Computational Modeling and Experiments: Toward a More Unified Theory of Social Influence |
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281 | (30) |
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281 | (2) |
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Social Influence Research |
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283 | (1) |
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284 | (2) |
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Integrated Empirical and Computational Investigation of Group Polarization |
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286 | (13) |
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Group Polarization Theory |
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286 | (2) |
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Frame-Induced Polarization Theory |
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288 | (5) |
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Accept-Shift-Constrict Model of Opinion Dynamics |
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293 | (2) |
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295 | (4) |
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299 | (6) |
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305 | (2) |
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307 | (1) |
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308 | (3) |
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14 Combining Data-Driven and Theory-Driven Models for Causality Analysis in Sociocultural Systems |
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311 | (26) |
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311 | (1) |
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312 | (5) |
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Ensembles of Causal Models |
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317 | (4) |
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Case Studies: Integrating Data-Driven and Theory-Driven Ensembles |
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321 | (11) |
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Letting the Data Speak: Additive Noise Ensembles |
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321 | (1) |
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Choosing Data-Driven Approaches Using Theory |
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322 | (2) |
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Parameterizing Theory-Driven Models Using Data |
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324 | (5) |
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329 | (3) |
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332 | (1) |
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333 | (4) |
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15 Theory-Interpretable, Data-Driven Agent-Based Modeling |
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337 | (22) |
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The Beauty and Challenge of Big Data |
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337 | (3) |
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A Proposed Unifying Principle for Big Data and Social Science |
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340 | (2) |
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Data-Driven Agent-Based Modeling |
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342 | (11) |
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343 | (1) |
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345 | (1) |
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348 | (1) |
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349 | (1) |
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350 | (1) |
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351 | (2) |
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Conclusion and the Vision |
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353 | (1) |
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354 | (1) |
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355 | (4) |
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16 Bringing the Real World into the Experimental Lab: Technology-Enabling Transformative Designs |
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359 | (28) |
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Understanding, Predicting, and Changing Behavior |
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359 | (1) |
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Social Domains of Interest |
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360 | (5) |
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360 | (1) |
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Harm Mitigation in Crises |
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361 | (1) |
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Terrorism Reduction and Lone Actors |
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362 | (3) |
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365 | (3) |
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365 | (1) |
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Shame Reduction as a Key Intervention |
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366 | (1) |
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Intelligent Agents in Games |
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367 | (1) |
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Generalizing Approach: Understanding and Changing Behavior Across Domains |
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367 | (1) |
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Experimental Designs for Real-World Simulations |
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368 | (3) |
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Standard Systematic Designs and Representative Designs: A Primer |
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368 | (1) |
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Systematic Representative Virtual Game Designs |
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369 | (1) |
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What Is a Default Control Condition? |
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370 | (1) |
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What Are Hooks and Experimental Alternatives? |
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370 | (1) |
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Creating Representative Designs for Virtual Games |
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371 | (4) |
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Measuring Occurrence of the Behavior of Interest (BoI) in Time |
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371 | (1) |
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Beyond the When of BoI: Identifying Challenges and Preconditions |
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372 | (1) |
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Creating a Sampling Frame of Challenges |
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372 | (1) |
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Coding/Structuring Sequences as in Everyday Life |
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372 | (2) |
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Naturally Covarying Factors/Cues in Situations |
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374 | (1) |
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Options Available in the Game |
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374 | (1) |
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Determining When and How Things Go Differently to Produce Riskier or Safer Choices |
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374 | (1) |
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More Detail Regarding Precipitating Cues |
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375 | (1) |
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Evaluations of the Effectiveness in Creating Representative Designs |
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375 | (1) |
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Default Control and Experimental Condition Alternatives |
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375 | (1) |
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Applications in Three Domains of Interest |
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375 | (1) |
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376 | (4) |
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380 | (7) |
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17 Online Games for Studying Human Behavior |
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387 | (20) |
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387 | (1) |
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Online Games and Massively Multiplayer Online Games for Research |
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388 | (2) |
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390 | (1) |
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War Games and Data Gathering for Nuclear Deterrence Policy |
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390 | (3) |
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MMOG Data to Test International Relations Theory |
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393 | (10) |
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397 | (1) |
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Analysis 1: All Guilds, Full-Time Period |
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397 | (1) |
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Analysis 2: Large Guilds, Full-Time Period |
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398 | (1) |
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Large Guilds, Interwar Period |
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398 | (1) |
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400 | (1) |
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Operationalizing MMOG Data |
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400 | (3) |
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Games as Experiments: The Future of Research |
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403 | (2) |
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404 | (1) |
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404 | (1) |
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404 | (1) |
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405 | (1) |
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405 | (1) |
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405 | (2) |
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18 Using Sociocultural Data from Online Gaming and Game Communities |
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407 | (36) |
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407 | (2) |
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Characterizing Social Behavior in Gaming |
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409 | (3) |
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412 | (10) |
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412 | (3) |
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415 | (3) |
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Asynchronous Community Data Sources |
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418 | (2) |
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Synchronous Community Data Sources (Streaming Sources) |
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420 | (2) |
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Case Studies of SBE Research in Game Environments |
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422 | (15) |
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Case Study 1: Extracting Player Behavior from League of Legends Data |
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422 | (4) |
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Case Study 2: Extracting Popularity Patterns from Hearthstone Community Data |
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426 | (6) |
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Case Study 3: Investigating Linguistic Indicators of Subcultures in Twitch |
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432 | (5) |
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Conclusions and Future Recommendations |
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437 | (1) |
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438 | (1) |
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438 | (5) |
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19 An Artificial Intelligence/Machine Learning Perspective on Social Simulation: New Data and New Challenges |
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443 | (34) |
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Objectives and Background |
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443 | (1) |
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443 | (11) |
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443 | (2) |
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Advances in Data Infrastructure |
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445 | (1) |
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445 | (1) |
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Evaluating the Data Ecosystem |
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447 | (1) |
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448 | (1) |
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449 | (1) |
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449 | (1) |
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Natural Language Processing (NLP) |
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450 | (1) |
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Adversarial Training for Unsupervised Learning |
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451 | (1) |
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452 | (1) |
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Emulating Human Biases and Bounded Rationality |
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453 | (1) |
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453 | (1) |
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Data and Theory for Behavioral Modeling and Simulation |
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454 | (16) |
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Prefacing Comments on Fundamentals |
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454 | (1) |
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For Want of Good Theory... |
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455 | (1) |
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The Scope of Theory and Laws for Behavioral Models |
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456 | (3) |
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The Scope of Data for Behavioral Models |
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459 | (1) |
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Bridging the Theory-Data Gap |
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460 | (1) |
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460 | (1) |
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Example 1: Modeling Belief Transmission: Memes and Related Issues at the Micro Level |
|
|
461 | (1) |
|
Example 2: Static Factor-Tree Modeling of Public Support for Terrorism |
|
|
465 | (5) |
|
Evaluating the PSOT Models |
|
|
470 | (1) |
|
Conclusion and Highlights |
|
|
470 | (2) |
|
|
|
472 | (1) |
|
|
|
472 | (5) |
|
20 Social Media Signal Processing |
|
|
477 | (18) |
|
|
|
|
|
Social Media as a Signal Modality |
|
|
477 | (2) |
|
Interdisciplinary Foundations: Sensors, Information, and Optimal Estimation |
|
|
479 | (2) |
|
Event Detection and Demultiplexing on the Social Channel |
|
|
481 | (11) |
|
|
|
484 | (3) |
|
Human Bias, Opinions, and Polarization |
|
|
487 | (1) |
|
Modeling Signal Propagation |
|
|
487 | (1) |
|
Opinion Separation and Polarization Detection |
|
|
489 | (1) |
|
|
|
490 | (2) |
|
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|
492 | (1) |
|
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|
492 | (1) |
|
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|
492 | (3) |
|
21 Evaluation and Validation Approaches for Simulation of Social Behavior: Challenges and Opportunities |
|
|
495 | (26) |
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495 | (3) |
|
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|
495 | (2) |
|
Online Communication in Particular: A Valuable Venue for Validation |
|
|
497 | (1) |
|
|
|
498 | (1) |
|
Simulation Evaluation: Current Practices |
|
|
499 | (1) |
|
Measurements, Metrics, and Their Limitations |
|
|
500 | (7) |
|
|
|
501 | (1) |
|
Selection of Appropriate Measurements and Metrics |
|
|
502 | (1) |
|
Correlations, Causation, and Transfer Entropy |
|
|
503 | (1) |
|
Initial Conditions and Model Assumptions |
|
|
504 | (1) |
|
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|
505 | (1) |
|
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|
506 | (1) |
|
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|
506 | (1) |
|
Proposed Evaluation Approach |
|
|
507 | (8) |
|
Considering the Goal of the Simulation |
|
|
507 | (1) |
|
|
|
508 | (2) |
|
Modeling Assumptions and Specifications |
|
|
510 | (1) |
|
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511 | (1) |
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512 | (1) |
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|
512 | (1) |
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512 | (1) |
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512 | (1) |
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513 | (1) |
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514 | (1) |
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515 | (1) |
|
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|
515 | (6) |
| Part IV: Innovations in Modeling |
|
521 | (268) |
|
22 The Agent-Based Model Canvas: A Modeling Lingua Franca for Computational Social Science |
|
|
523 | (22) |
|
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|
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|
|
|
|
|
|
523 | (4) |
|
|
|
524 | (1) |
|
|
|
525 | (1) |
|
The Agent-Based Model Canvas |
|
|
526 | (1) |
|
|
|
527 | (3) |
|
|
|
527 | (1) |
|
Social Scientific Languages in CSS |
|
|
528 | (1) |
|
|
|
529 | (1) |
|
A Comparison of Existing Languages |
|
|
530 | (1) |
|
The Agent-Based Model Canvas |
|
|
530 | (10) |
|
From Theory to Hypothesis: Human-Aided Data-Driven Hypothesis Building |
|
|
532 | (1) |
|
From Hypothesis to Model: Data-Driven Calibration and Model Discovery |
|
|
533 | (2) |
|
|
|
535 | (1) |
|
Schelling's Segregation Model |
|
|
535 | (1) |
|
|
|
537 | (3) |
|
|
|
540 | (1) |
|
|
|
541 | (4) |
|
23 Representing Socio-Behavioral Understanding with Models |
|
|
545 | (24) |
|
|
|
|
|
|
|
545 | (1) |
|
Philosophical Foundations |
|
|
546 | (9) |
|
Modeling in Support of Scientific Work |
|
|
546 | (2) |
|
Epistemological Constraints for Computational Science |
|
|
548 | (3) |
|
Multi-, Inter-, and Transdisciplinary Research |
|
|
551 | (4) |
|
Simulation and Modeling Approaches for Computational Social Scientists |
|
|
555 | (7) |
|
Simulation OF Social Systems |
|
|
556 | (1) |
|
Simulation of Social Systems from the Top Down |
|
|
557 | (1) |
|
Simulation of Social Systems from the Bottom Up |
|
|
558 | (3) |
|
Simulation FOR Social Systems |
|
|
561 | (1) |
|
|
|
562 | (1) |
|
|
|
563 | (1) |
|
|
|
563 | (1) |
|
|
|
564 | (5) |
|
24 Toward Self-Aware Models as Cognitive Adaptive Instruments for Social and Behavioral Modeling |
|
|
569 | (18) |
|
|
|
|
|
569 | (2) |
|
Perspective and Challenges |
|
|
571 | (4) |
|
Models as Dynamic Data and Theory-Driven Mediating Instruments |
|
|
571 | (1) |
|
|
|
572 | (1) |
|
|
|
573 | (1) |
|
Cognitive Assistance in Modeling |
|
|
573 | (2) |
|
A Generic Architecture for Models as Cognitive Autonomous Agents |
|
|
575 | (3) |
|
|
|
578 | (3) |
|
|
|
579 | (1) |
|
|
|
580 | (1) |
|
|
|
580 | (1) |
|
Coherence-Driven Cognitive Model of Mediation |
|
|
581 | (3) |
|
|
|
584 | (1) |
|
|
|
585 | (2) |
|
25 Causal Modeling with Feedback Fuzzy Cognitive Maps |
|
|
587 | (30) |
|
|
|
|
|
|
|
587 | (1) |
|
Overview of Fuzzy Cognitive Maps for Causal Modeling |
|
|
588 | (4) |
|
|
|
589 | (1) |
|
Comparison with Other Methods |
|
|
589 | (2) |
|
|
|
591 | (1) |
|
Combining Causal Knowledge: Averaging Edge Matrices |
|
|
592 | (2) |
|
Learning FCM Causal Edges |
|
|
594 | (3) |
|
FCM Example: Public Support for Insurgency and Terrorism |
|
|
597 | (6) |
|
US-China Relations: An FCM of Allison's Thucydides Trap |
|
|
603 | (8) |
|
|
|
611 | (1) |
|
|
|
612 | (5) |
|
26 Simulation Analytics for Social and Behavioral Modeling |
|
|
617 | (16) |
|
|
|
|
|
|
|
|
|
|
|
617 | (2) |
|
|
|
619 | (5) |
|
Simulation Analytics for Social and Behavioral Modeling |
|
|
624 | (4) |
|
Identifying Causal Connections Between Behaviors and Outcomes |
|
|
625 | (3) |
|
|
|
628 | (2) |
|
|
|
630 | (1) |
|
|
|
630 | (3) |
|
27 Using Agent-Based Models to Understand Health-Related Social Norms |
|
|
633 | (22) |
|
|
|
|
|
|
|
633 | (1) |
|
|
|
634 | (1) |
|
Lightweight Normative Architecture (LNA) |
|
|
634 | (1) |
|
Cognitive Social Learners (CSL) Architecture |
|
|
635 | (4) |
|
Belief, Desire, and Intention |
|
|
635 | (1) |
|
Game-Theoretic Interaction |
|
|
636 | (1) |
|
Norm Recognition Using RL |
|
|
637 | (1) |
|
|
|
637 | (2) |
|
|
|
639 | (2) |
|
|
|
639 | (1) |
|
|
|
640 | (1) |
|
|
|
640 | (1) |
|
|
|
641 | (4) |
|
|
|
642 | (2) |
|
|
|
644 | (1) |
|
|
|
645 | (1) |
|
|
|
646 | (6) |
|
|
|
647 | (5) |
|
|
|
652 | (1) |
|
|
|
652 | (1) |
|
|
|
652 | (1) |
|
|
|
652 | (3) |
|
28 Lessons from a Project on Agent-Based Modeling |
|
|
655 | (18) |
|
|
|
|
|
|
|
655 | (1) |
|
|
|
656 | (5) |
|
|
|
657 | (4) |
|
|
|
661 | (1) |
|
|
|
661 | (1) |
|
Verification and Validation |
|
|
661 | (4) |
|
|
|
663 | (1) |
|
|
|
664 | (1) |
|
Self-Organization and Emergence |
|
|
665 | (3) |
|
|
|
665 | (1) |
|
|
|
666 | (2) |
|
|
|
668 | (1) |
|
|
|
669 | (1) |
|
|
|
670 | (3) |
|
29 Modeling Social and Spatial Behavior in Built Environments: Current Methods and Future Directions |
|
|
673 | (24) |
|
|
|
|
|
|
|
673 | (2) |
|
Simulating Human Behavior-A Review |
|
|
675 | (3) |
|
|
|
675 | (1) |
|
|
|
676 | (1) |
|
|
|
677 | (1) |
|
|
|
677 | (1) |
|
|
|
677 | (1) |
|
Modeling Social and Spatial Behavior with MAS |
|
|
678 | (7) |
|
|
|
678 | (1) |
|
|
|
679 | (1) |
|
|
|
680 | (1) |
|
|
|
680 | (1) |
|
Semantics and Affordances |
|
|
680 | (1) |
|
|
|
680 | (1) |
|
|
|
681 | (1) |
|
Perceptual and Cognitive Abilities |
|
|
681 | (1) |
|
|
|
681 | (1) |
|
|
|
682 | (1) |
|
|
|
682 | (1) |
|
Reactive Planning Approaches |
|
|
682 | (1) |
|
Predictive Planning Approaches |
|
|
682 | (1) |
|
|
|
682 | (1) |
|
|
|
683 | (1) |
|
Centralized Scheduling Systems |
|
|
683 | (1) |
|
|
|
684 | (1) |
|
|
|
684 | (1) |
|
Discussion and Future Directions |
|
|
685 | (2) |
|
Creating Heterogeneous Agents |
|
|
685 | (1) |
|
Improving Agents' Multi-Modal Perception and Cognition |
|
|
685 | (1) |
|
Using Human Behavior Simulation as a Decision-Support System in Architectural Design |
|
|
686 | (1) |
|
|
|
687 | (1) |
|
|
|
687 | (10) |
|
30 Multi-Scale Resolution of Human Social Systems: A Synergistic Paradigm for Simulating Minds and Society |
|
|
697 | (14) |
|
|
|
|
|
697 | (2) |
|
The Reciprocal Constraints Paradigm |
|
|
699 | (7) |
|
Applying the Reciprocal Constraints Paradigm |
|
|
701 | (1) |
|
|
|
701 | (1) |
|
|
|
703 | (3) |
|
|
|
706 | (2) |
|
|
|
708 | (1) |
|
|
|
708 | (3) |
|
31 Multi-formalism Modeling of Complex Social-Behavioral Systems |
|
|
711 | (30) |
|
|
|
|
|
|
|
|
|
711 | (2) |
|
|
|
713 | (5) |
|
Social Entity or Granularity |
|
|
716 | (1) |
|
|
|
716 | (1) |
|
|
|
717 | (1) |
|
|
|
718 | (1) |
|
Issues in Multi-formalism Modeling and Use |
|
|
719 | (15) |
|
The Physical Layer and the Syntactic Layer |
|
|
719 | (2) |
|
|
|
721 | (8) |
|
|
|
729 | (5) |
|
Issues in Multi-formalism Modeling and Simulation |
|
|
734 | (2) |
|
The Representation Problem: Information Consistency, Representability, and Sharing |
|
|
734 | (1) |
|
The Simulation Level: Process Representation and Enactment |
|
|
735 | (1) |
|
The Results Level: Local and Global Results Representation, Traceability, Handling, and Reuse of Intermediate Results |
|
|
735 | (1) |
|
|
|
736 | (1) |
|
|
|
736 | (1) |
|
|
|
737 | (4) |
|
32 Social-Behavioral Simulation: Key Challenges |
|
|
741 | (12) |
|
|
|
|
|
741 | (1) |
|
Key Communication Challenges |
|
|
742 | (1) |
|
Key Scientific Challenges |
|
|
743 | (5) |
|
Toward a New Science of Validation |
|
|
748 | (1) |
|
|
|
749 | (1) |
|
|
|
750 | (3) |
|
33 Panel Discussion: Moving Social-Behavioral Modeling Forward |
|
|
753 | (36) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
754 | (11) |
|
Andreas Tolk: Epistemological, Not Ontological Emergence |
|
|
755 | (1) |
|
Kathleen M. Carley: Emergence Does Not Happen Magically in Simulations or the Real World |
|
|
756 | (1) |
|
Joshua M. Epstein: Of Course, Emergent Phenomena Are Baked into Computer Models |
|
|
757 | (1) |
|
Levent Yilmaz: Emergent Behavior May Have a Higher-Level Ontology |
|
|
758 | (1) |
|
Samarth Swarup: The Promise of Clever Agents for True Emergence in Simulations |
|
|
759 | (1) |
|
Luke J. Matthews: Examples of True Emergence in Current Agent-Based Models |
|
|
760 | (1) |
|
Raffaele Vardavas: Importance of Nonlinearity for Emergence |
|
|
761 | (1) |
|
Bill Rand: The Difficulty in Simulating Emergence |
|
|
761 | (1) |
|
Paul K. Davis: Reproducing Emergence Through Simulation Is a Valuable Hard Problem to Tackle |
|
|
762 | (1) |
|
Scott Neal Reilly: Simulations Can Explore How Emergent Behavior Might Occur |
|
|
762 | (1) |
|
Ted Pavlic: Simulaions Can Serve as Existential Witnesses for Emergent Phenomena |
|
|
763 | (2) |
|
Relating Models Across Levels |
|
|
765 | (11) |
|
Matthew E. Brashears: Interpretation Is Crucial in Cross-Level Modeling |
|
|
766 | (3) |
|
Erica Briscoe and Scott Appling: Multi-Scale Modeling Can Exploit Both Data-and Theory-Driven Insights 768 Scott Neal Reilly: A Combination of Theory-Driven and |
|
|
|
Data-Driven Inquiry Is Best |
|
|
769 | (1) |
|
Corey Lofdahl: Decomposition Is Sometimes Necessary But Creates Issues |
|
|
769 | (2) |
|
Ted Pavlic: Detailed Models Are Only Sometimes Desirable 770 William B. Rouse: Top-Down or Bottom-Up Modeling Serve Different Purposes |
|
|
771 | (1) |
|
Paul K. Davis: Aggregation and Disaggregation Functions Need To Be Contextual |
|
|
772 | (1) |
|
Raffaele Vardavas: Bottom-Up Modeling Need Not Be All or Nothing |
|
|
772 | (1) |
|
Kent Myers: Meso-Modeling Is a Good Fit for Addressing Concrete Human Problems |
|
|
773 | (1) |
|
Levent Yilmaz: Improved Development of Hybrid Models Is Possible |
|
|
774 | (1) |
|
Kathleen M. Carley: Distinguishing Challenges of Multilevel and Hybrid Simulation |
|
|
775 | (1) |
|
Going Beyond Rational Actors |
|
|
776 | (8) |
|
Joshua M. Epstein: Inverse Generative Social Science-What Machine Learning Can Do for Agent-Based Modeling |
|
|
779 | (1) |
|
Raffaele Vardavas: Evidence-Based Models Need to be General Enough to be Realistic Under Alternative Specifications |
|
|
780 | (1) |
|
Kathleen M. Carley: Agent-Based Dynamic Network Models Produce More Realistic Agents |
|
|
781 | (1) |
|
Levent Yilmaz: Realistic Models Must Include Cognitive Biases and Limitations |
|
|
781 | (1) |
|
Scott Neal Reilly: High Degree Realism Entails Costs That May Not Be Outweighed by Their Benefits |
|
|
782 | (1) |
|
Ted Pavlic: With Additional Realism Comes Additional Liability |
|
|
782 | (2) |
|
|
|
784 | (5) |
| Part V: Models for Decision-Makers |
|
789 | (138) |
|
34 Human-Centered Design of Model-Based Decision Support for Policy and Investment Decisions |
|
|
791 | (18) |
|
|
|
|
|
791 | (1) |
|
|
|
792 | (1) |
|
|
|
792 | (1) |
|
|
|
793 | (4) |
|
|
|
797 | (2) |
|
|
|
799 | (1) |
|
|
|
800 | (1) |
|
|
|
801 | (3) |
|
Observations on Problem-Solving |
|
|
804 | (2) |
|
|
|
804 | (1) |
|
|
|
804 | (1) |
|
|
|
805 | (1) |
|
|
|
806 | (1) |
|
|
|
807 | (2) |
|
35 A Complex Systems Approach for Understanding the Effect of Policy and Management Interventions on Health System Performance |
|
|
809 | (24) |
|
|
|
|
|
|
|
|
|
809 | (2) |
|
Understanding Health System Performance |
|
|
811 | (2) |
|
|
|
813 | (2) |
|
|
|
813 | (1) |
|
Rehabilitation Coordinators |
|
|
814 | (1) |
|
Physical and Mental Health Treatment Services |
|
|
814 | (1) |
|
|
|
815 | (1) |
|
|
|
815 | (2) |
|
Seeking Treatment Service Approval from the Health System |
|
|
815 | (1) |
|
Seeking Healthcare Services |
|
|
815 | (1) |
|
Exiting the Health System |
|
|
816 | (1) |
|
Policy Scenario Simulation |
|
|
817 | (1) |
|
|
|
817 | (7) |
|
|
|
824 | (2) |
|
|
|
826 | (1) |
|
|
|
827 | (6) |
|
36 Modeling Information and Gray Zone Operations |
|
|
833 | (16) |
|
|
|
|
|
833 | (2) |
|
The Technological Transformation of War: Counterintuitive Consequences |
|
|
835 | (3) |
|
|
|
835 | (1) |
|
|
|
836 | (2) |
|
Modeling Information Operations: Representing Complexity |
|
|
838 | (4) |
|
Modeling Gray Zone Operations: Extending Analytic Capability |
|
|
842 | (3) |
|
|
|
845 | (2) |
|
|
|
847 | (2) |
|
37 Homo Narratus (The Storytelling Species): The Challenge (and Importance) of Modeling Narrative in Human Understanding |
|
|
849 | (16) |
|
|
|
|
|
849 | (1) |
|
|
|
850 | (1) |
|
What Is Important About Narratives? |
|
|
851 | (5) |
|
People Use Narratives to Make Sense of the World |
|
|
851 | (2) |
|
Compelling Narratives Have Consistency, Familiarity, and Proof |
|
|
853 | (1) |
|
Narratives Already Exist and Cannot Always Be Changed or Replaced |
|
|
853 | (3) |
|
What Can Commands Try to Accomplish with Narratives in Support of Operations? |
|
|
856 | (1) |
|
Moving Forward in Fighting Against, with, and Through Narrative in Support of Operations |
|
|
857 | (4) |
|
|
|
857 | (2) |
|
Developing a Command's Mission Narrative |
|
|
859 | (1) |
|
Developing a Command's External Narrative |
|
|
859 | (1) |
|
Developing and Promoting Desired Narratives Among Relevant Audiences |
|
|
860 | (1) |
|
Conclusion: Seek Modeling and Simulation Improvements That Will Enable Training and Experience with Narrative |
|
|
861 | (1) |
|
|
|
862 | (3) |
|
38 Aligning Behavior with Desired Outcomes: Lessons for Government Policy from the Marketing World |
|
|
865 | (20) |
|
|
|
Technique 1: Identify the Human Problem |
|
|
867 | (2) |
|
Technique 2: Rethinking Quantitative Data |
|
|
869 | (7) |
|
Technique 3: Rethinking Qualitative Research |
|
|
876 | (6) |
|
|
|
882 | (1) |
|
|
|
882 | (3) |
|
39 Future Social Science That Matters for Statecraft |
|
|
885 | (14) |
|
|
|
|
|
885 | (1) |
|
|
|
885 | (2) |
|
Interactions with the Intelligence Community |
|
|
887 | (1) |
|
|
|
888 | (3) |
|
|
|
891 | (2) |
|
|
|
893 | (2) |
|
|
|
895 | (1) |
|
|
|
896 | (3) |
|
40 Lessons on Decision Aiding for Social-Behavioral Modeling |
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899 | (28) |
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Strategic Planning Is Not About Simply Predicting and Acting |
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899 | (2) |
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Characteristics Needed for Good Decision Aiding |
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901 | (17) |
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Systems Thinking for a Strategic View |
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902 | (1) |
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902 | (1) |
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903 | (1) |
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904 | (1) |
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How Much Detail Is Needed? |
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904 | (1) |
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905 | (1) |
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905 | (1) |
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Confronting Uncertainty and Disagreement |
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906 | (1) |
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Normal and Deep Uncertainty |
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907 | (1) |
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Exploring Uncertainty in Scenario Space |
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907 | (1) |
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Exploration Guided by the XLRM Framework |
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909 | (1) |
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The Nuts and Bolts of Coping with Dimensional Explosion |
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910 | (1) |
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Finding Strategy to Cope with Uncertainty |
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911 | (1) |
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Planning for Adaptiveness with a Portfolio of Capabilities |
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911 | (1) |
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Finding Adaptive Strategies by Minimizing Regret |
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917 | (1) |
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Planning Adaptive Pathways |
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918 | (1) |
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Implications for Social-Behavioral Modeling |
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918 | (3) |
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921 | (2) |
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923 | (4) |
| Index |
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927 | |
