Parallel Population and Parallel Human: A Cyber-Physical Social Approach

Description

Parallel Population and Parallel Human Proposes a new paradigm to investigate an individual’s cognitive deliberation in dynamic human-machine interactions Today, intelligent machines enable people to interact remotely with friends, family, romantic partners, colleagues, competitors, organizations, and others. Virtual reality (VR), augmented reality (AR), artificial intelligence (AI), mobile social media, and other technologies have been driving these interactions to an unprecedented level. As the complexity in system control and management with human participants increases, engineers are facing challenges that arise from the uncertainty of operators or users. Parallel Population and Parallel Human: A Cyber-Physical Social Approach presents systemic solutions for modeling, analysis, computation, and management of individuals’ cognition and decision-making in human-participated systems, such as the MetaVerse. With a virtual-real behavioral approach that seeks to actively prescribe user behavior through cognitive and dynamic learning, the authors present a parallel population/human model for optimal prescriptive control and management of complex systems that leverages recent advances in artificial intelligence. Throughout the book, the authors address basic theory and methodology for modeling, describe various implementation techniques, highlight potential acceleration technologies, discuss application cases from different fields, and more. In addition, the text: Considers how an individual’s behavior is formed and how to prescribe their behavioral modesDescribes agent-based computation for complex social systems based on a synthetic population from realistic individual groupsProposes a universal algorithm applicable to a wide range of social organization typesExtends traditional cognitive modeling by utilizing a dynamic approach to investigate cognitive deliberation in highly time-variant tasksPresents a new method that can be used for both large-scale social systems and real-time human-machine interactions without extensive experiments for modeling Parallel Population and Parallel Human: A Cyber-Physical Social Approach is a must-read for researchers, engineers, scientists, professionals, and graduate students who work on systems engineering, human-machine interaction, cognitive computing, and artificial intelligence. Cover Title Page Copyright Contents Preface Acknowledgments Chapter 1 From Behavioral Analysis to Prescription 1.1 Social Intelligence 1.2 Human–Machine Interaction 1.3 From Behavior Analysis to Prescription 1.4 Parallel Population and Parallel Human 1.5 Central Themes and Structure of this Book References Chapter 2 Basic Population Synthesis 2.1 Problem Statement and Data Sources 2.1.1 Cross‐Classification Table 2.1.2 Sample 2.1.3 Long Table 2.2 Sample‐Based Method 2.2.1 Iterative Proportional Fitting Synthetic Reconstruction 2.2.2 Combinatorial Optimization 2.2.3 Copula‐Based Synthesis 2.3 Sample‐Free Method 2.4 Experiment Results 2.4.1 Copula‐Based Population Synthesis 2.4.2 Joint Distribution Inference 2.5 Conclusions and Discussions References Chapter 3 Synthetic Population with Social Relationships 3.1 Household Integration in Synthetic Population 3.2 Individual Assignment 3.2.1 Heuristic Allocation 3.2.2 Iterative Allocation 3.3 Heuristic Search 3.4 Joint Distribution Fitting 3.5 Deep Generative Models 3.6 Population Synthesis with Multi‐social Relationships 3.6.1 Limitations of IPU Algorithm 3.6.2 Population with Multi‐social Relationships 3.7 Conclusions and Discussions References Chapter 4 Architecture for Agent Decision Cycle 4.1 Parallel Humans in Human–Machine Interactive Systems 4.2 Why and What Is the Cognitive Architecture? 4.3 Architecture for Artificial General Intelligence 4.4 Architecture for Control 4.5 Architecture for Knowledge Discovery 4.6 Architecture for Computational Neuroscience 4.7 Architecture for Pattern Recognition 4.8 Other Representative Architecture 4.9 TiDEC: A Two‐Layered Integrated Cycle for Agent Decision 4.10 Conclusions and Discussions References Chapter 5 Evolutionary Reasoning 5.1 Knowledge Representation 5.2 Evolutionary Reasoning Using Causal Inference 5.3 Learning Fitness Function from Expert Decision Chains 5.4 Conclusions and Discussions References Chapter 6 Knowledge Acquisition by Learning 6.1 Foundation of Knowledge Repository Learning 6.2 Knowledge Acquisition Based on Self‐Supervised Learning 6.3 Adaptive Knowledge Extraction for Data Stream 6.3.1 Neural‐Symbolic Learning 6.3.2 Explanation of Deep Learning 6.4 Experiment on Travel Behavior Learning 6.5 Conclusions and Discussions References Chapter 7 Agent Calibration and Validation 7.1 Model Calibration for Agent 7.2 Calibration Based on Optimization 7.3 Calibration Based on Machine Learning 7.4 Calibration Based on Cybernetics 7.5 Calibration Using Variational Auto‐Encoder 7.6 Conclusions and Discussions References Chapter 8 High‐Performance Computing for Computational Deliberation Experiments 8.1 Computational Acceleration Using High‐Performance Computing 8.1.1 Spark with Hadoop 8.1.2 MPI/OpenMP on Supercomputing 8.2 Computational Deliberation Experiments in Cloud Computing 8.3 Computational Deliberation Experiments in Supercomputing 8.4 Conclusions and Discussions References Chapter 9 Interactive Strategy Prescription 9.1 Hierarchical Behavior Prescription System 9.2 Dynamic Community Discovery for Group Prescription 9.3 Strategy Prescription Based on Content Match 9.4 Active Learning in Strategy Prescription 9.5 Conclusions and Discussions References Chapter 10 Applications for Parallel Population/Human 10.1 Population Evolution 10.2 Computational Experiments for Travel Behavior 10.3 Parallel Travel Behavioral Prescription 10.4 Travel Behavioral Prescription for Sports Event 10.5 Conclusions and Discussions References Chapter 11 Ethical and Legal Issues of Parallel Population/Human 11.1 Relationships Between the Parallel Population/Human and Its Individual Users 11.2 Authority of the Parallel Population/Human System 11.3 Risk Management and Responsibility Identification 11.4 Conclusions and Discussions References Appendix A Convergence for Multivariate IPF References Index EULA