Preface xi
Acknowledgments xv
1 From Behavioral Analysis to Prescription 1
1.1 Social Intelligence 1
1.2 Humanâ"Machine Interaction 4
1.3 From Behavior Analysis to Prescription 6
1.4 Parallel Population and Parallel Human 9
1.5 Central Themes and Structure of this Book 11
References 13
2 Basic Population Synthesis 17
2.1 Problem Statement and Data Sources 18
2.1.1 Cross-Classification Table 18
2.1.2 Sample 21
2.1.3 Long Table 22
2.2 Sample-Based Method 23
2.2.1 Iterative Proportional Fitting Synthetic Reconstruction 23
2.2.2 Combinatorial Optimization 24
2.2.3 Copula-Based Synthesis 26
2.3 Sample-Free Method 30
2.4 Experiment Results 37
2.4.1 Copula-Based Population Synthesis 37
2.4.2 Joint Distribution Inference 43
2.5 Conclusions and Discussions 52
References 53
3 Synthetic Population with Social Relationships 55
3.1 Household Integration in Synthetic Population 56
3.2 Individual Assignment 60
3.2.1 Heuristic Allocation 62
3.2.2 Iterative Allocation 65
3.3 Heuristic Search 67
3.4 Joint Distribution Fitting 70
3.5 Deep Generative Models 75
3.6 Population Synthesis with Multi-social Relationships 78
3.6.1 Limitations of IPU Algorithm 78
3.6.2 Population with Multi-social Relationships 85
3.7 Conclusions and Discussions 93
References 95
4 Architecture for Agent Decision Cycle 97
4.1 Parallel Humans in Humanâ"Machine Interactive Systems 98
4.2 Why and What Is the Cognitive Architecture? 100
4.3 Architecture for Artificial General Intelligence 103
4.4 Architecture for Control 110
4.5 Architecture for Knowledge Discovery 113
4.6 Architecture for Computational Neuroscience 116
4.7 Architecture for Pattern Recognition 121
4.8 Other Representative Architecture 123
4.9 TiDEC: A Two-Layered Integrated Cycle for Agent Decision 129
4.10 Conclusions and Discussions 134
References 135
5 Evolutionary Reasoning 141
5.1 Knowledge Representation 142
5.2 Evolutionary Reasoning Using Causal Inference 147
5.3 Learning Fitness Function from Expert Decision Chains 156
5.4 Conclusions and Discussions 158
References 158
6 Knowledge Acquisition by Learning 163
6.1 Foundation of Knowledge Repository Learning 164
6.2 Knowledge Acquisition Based on Self-Supervised Learning 167
6.3 Adaptive Knowledge Extraction for Data Stream 170
6.3.1 Neural-Symbolic Learning 171
6.3.2 Explanation of Deep Learning 176
6.4 Experiment on Travel Behavior Learning 183
6.5 Conclusions and Discussions 194
References 195
7 Agent Calibration and Validation 199
7.1 Model Calibration for Agent 199
7.2 Calibration Based on Optimization 202
7.3 Calibration Based on Machine Learning 209
7.4 Calibration Based on Cybernetics 214
7.5 Calibration Using Variational Auto-Encoder 227
7.6 Conclusions and Discussions 233
References 233
8 High-Performance Computing for Computational Deliberation Experiments 237
8.1 Computational Acceleration Using High-Performance Computing 237
8.1.1 Spark with Hadoop 238
8.1.2 MPI/OpenMP on Supercomputing 244
8.2 Computational Deliberation Experiments in Cloud Computing 249
8.3 Computational Deliberation Experiments in Supercomputing 258
8.4 Conclusions and Discussions 262
References 263
9 Interactive Strategy Prescription 265
9.1 Hierarchical Behavior Prescription System 266
9.2 Dynamic Community Discovery for Group Prescription 270
9.3 Strategy Prescription Based on Content Match 273
9.4 Active Learning in Strategy Prescription 278
9.5 Conclusions and Discussions 284
References 285
10 Applications for Parallel Population/Human 287
10.1 Population Evolution 287
10.2 Computational Experiments for Travel Behavior 292
10.3 Parallel Travel Behavioral Prescription 295
10.4 Travel Behavioral Prescription for Sports Event 301
10.5 Conclusions and Discussions 305
References 306
11 Ethical and Legal Issues of Parallel Population/Human 307
11.1 Relationships Between the Parallel Population/Human and Its Individual Users 308
11.2 Authority of the Parallel Population/Human System 309
11.3 Risk Management and Responsibility Identification 310
11.4 Conclusions and Discussions 311
References 311
Appendix A Convergence for Multivariate IPF 313
Index 321
