Details

Social-Behavioral Modeling for Complex Systems


Social-Behavioral Modeling for Complex Systems


Stevens Institute Series on Complex Systems and Enterprises 1. Aufl.

von: Paul K. Davis, Angela O'Mahony, Jonathan Pfautz

134,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 13.03.2019
ISBN/EAN: 9781119484981
Sprache: englisch
Anzahl Seiten: 992

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Beschreibungen

This volume describes frontiers in social-behavioral modeling for contexts as diverse as national security, health, and on-line social gaming. Recent scientific and technological advances have created exciting opportunities for such improvements. However, the book also identifies crucial scientific, ethical, and cultural challenges to be met if social-behavioral modeling is to achieve its potential. Doing so will require new methods, data sources, and technology. The volume discusses these, including those needed to achieve and maintain high standards of ethics and privacy. The result should be a new generation of modeling that will advance science and, separately, aid decision-making on major social and security-related subjects despite the myriad uncertainties and complexities of social phenomena.  Intended to be relatively comprehensive in scope, the volume balances theory-driven, data-driven, and hybrid approaches. The latter may be rapidly iterative, as when artificial-intelligence methods are coupled with theory-driven insights to build models that are sound, comprehensible and usable in new situations. With the intent of being a milestone document that sketches a research agenda for the next decade, the volume draws on the wisdom, ideas and suggestions of many noted researchers who draw in turn from anthropology, communications, complexity science, computer science, defense planning, economics, engineering, health systems, medicine, neuroscience, physics, political science, psychology, public policy and sociology.  In brief, the volume discusses: Cutting-edge challenges and opportunities in modeling for social and behavioral science Special requirements for achieving high standards of privacy and ethics  New approaches for developing theory while exploiting both empirical and computational data Issues of reproducibility, communication, explanation, and validation Special requirements for models intended to inform decision making about complex social systems
Foreword xxvii List of Contributors xxxi About the Editors xli About the Companion Website xliii Part I Introduction and Agenda 1 1 Understanding and Improving the Human Condition: A Vision of the Future for Social-Behavioral Modeling 3Jonathan Pfautz, Paul K. Davis, and Angela O’Mahony Challenges 5 About This Book 10 References 13 2 Improving Social-Behavioral Modeling 15Paul K. Davis and Angela O’Mahony Aspirations 15 Classes of Challenge 17 Inherent Challenges 17 Selected Specific Issues and the Need for Changed Practices 20 Strategy for Moving Ahead 32 Social-Behavioral Laboratories 39 Conclusions 41 Acknowledgments 42 References 42 3 Ethical and Privacy Issues in Social-Behavioral Research 49Rebecca Balebako, Angela O’Mahony, Paul K. Davis, and Osonde Osoba Improved Notice and Choice 50 Usable and Accurate Access Control 52 Anonymization 53 Avoiding Harms by Validating Algorithms and Auditing Use 55 Challenge and Redress 56 Deterrence of Abuse 57 And Finally Thinking Bigger About What Is Possible 58 References 59 Part II Foundations of Social-Behavioral Science 63 4 Building on Social Science: Theoretic Foundations for Modelers 65Benjamin Nyblade, Angela O’Mahony, and Katharine Sieck Background 65 Atomistic Theories of Individual Behavior 66 Social Theories of Individual Behavior 75 Theories of Interaction 80 From Theory to Data and Data to Models 88 Building Models Based on Social Scientific Theories 92 Acknowledgments 94 References 94 5 How Big and How Certain? A New Approach to Defining Levels of Analysis for Modeling Social Science Topics 101Matthew E. Brashears Introduction 101 Traditional Conceptions of Levels of Analysis 102 Incompleteness of Levels of Analysis 104 Constancy as the Missing Piece 107 Putting It Together 111 Implications for Modeling 113 Conclusions 116 Acknowledgments 116 References 116 6 Toward Generative Narrative Models of the Course and Resolution of Conflict 121Steven R. Corman, Scott W. Ruston, and Hanghang Tong Limitations of Current Conceptualizations of Narrative 122 A Generative Modeling Framework 125 Application to a Simple Narrative 126 Real-World Applications 130 Challenges and Future Research 133 Conclusion 135 Acknowledgment 137 Locations, Events, Actions, Participants, and Things in the Three Little Pigs 137 Edges in the Three Little Pigs Graph 139 References 142 7 A Neural Network Model of Motivated Decision-Making in Everyday Social Behavior 145Stephen J. Read and Lynn C. Miller Introduction 145 Overview 146 Theoretical Background 147 Neural Network Implementation 151 Conclusion 159 References 160 8 Dealing with Culture as Inherited Information 163Luke J. Matthews Galton’s Problem as a Core Feature of Cultural Theory 163 How to Correct for Treelike Inheritance of Traits Across Groups 167 Dealing with Non independence in Less Treelike Network Structures 173 Future Directions for Formal Modeling of Culture 178 Acknowledgments 181 References 181 9 Social Media, Global Connections, and Information Environments: Building Complex Understandings of Multi-Actor Interactions 187Gene Cowherd and Daniel Lende A New Setting of Hyperconnectivity 187 The Information Environment 188 Social Media in the Information Environment 189 Integrative Approaches to Understanding Human Behavior 190 The Ethnographic Examples 192 Conclusion 202 References 204 10 Using Neuroimaging to Predict Behavior: An Overview with a Focus on the Moderating Role of Sociocultural Context 205Steven H. Tompson, Emily B. Falk, Danielle S. Bassett, and Jean M. Vettel Introduction 205 The Brain-as-Predictor Approach 206 Predicting Individual Behaviors 208 Interpreting Associations Between Brain Activation and Behavior 210 Predicting Aggregate Out-of-Sample Group Outcomes 211 Predicting Social Interactions and Peer Influence 214 Sociocultural Context 215 Future Directions 219 Conclusion 221 References 222 11 Social Models from Non-Human Systems 231Theodore P. Pavlic Emergent Patterns in Groups of Behaviorally Flexible Individuals 232 Model Systems for Understanding Group Competition 239 Information Dynamics in Tightly Integrated Groups 246 Conclusions 254 Acknowledgments 255 References 255 12 Moving Social-Behavioral Modeling Forward: Insights from Social Scientists 263Matthew Brashears, Melvin Konner, Christian Madsbjerg, Laura McNamara, and Katharine Sieck Why Do People Do What They Do? 264 Everything Old Is New Again 264 Behavior Is Social, Not Just Complex 267 What is at Stake? 270 Sensemaking 272 Final Thoughts 275 References 276 Part III Informing Models with Theory and Data 279 13 Integrating Computational Modeling and Experiments: Toward a More Unified Theory of Social Influence 281Michael Gabbay Introduction 281 Social Influence Research 283 Opinion Network Modeling 284 Integrated Empirical and Computational Investigation of Group Polarization 286 Integrated Approach 299 Conclusion 305 Acknowledgments 307 References 308 14 Combining Data-Driven and Theory-Driven Models for Causality Analysis in Sociocultural Systems 311Amy Sliva, Scott Neal Reilly, David Blumstein, and Glenn Pierce Introduction 311 Understanding Causality 312 Ensembles of Causal Models 317 Case Studies: Integrating Data-Driven and Theory-Driven Ensembles 321 Conclusions 332 References 333 15 Theory-Interpretable, Data-Driven Agent-Based Modeling 337William Rand The Beauty and Challenge of Big Data 337 A Proposed Unifying Principle for Big Data and Social Science 340 Data-Driven Agent-Based Modeling 342 Conclusion and the Vision 353 Acknowledgments 354 References 355 16 Bringing the Real World into the Experimental Lab: Technology-Enabling Transformative Designs 359Lynn C. Miller, Liyuan Wang, David C. Jeong, and Traci K. Gillig Understanding, Predicting, and Changing Behavior 359 Social Domains of Interest 360 The SOLVE Approach 365 Experimental Designs for Real-World Simulations 368 Creating Representative Designs for Virtual Games 371 Applications in Three Domains of Interest 375 Conclusions 376 References 380 17 Online Games for Studying Human Behavior 387Kiran Lakkaraju, Laura Epifanovskaya, Mallory Stites, Josh Letchford, Jason Reinhardt, and Jon Whetzel Introduction 387 Online Games and Massively Multiplayer Online Games for Research 388 War Games and Data Gathering for Nuclear Deterrence Policy 390 MMOG Data to Test International Relations Theory 393 Analysis and Results 397 Games as Experiments: The Future of Research 403 Final Discussion 405 Acknowledgments 405 References 405 18 Using Sociocultural Data from Online Gaming and Game Communities 407Sean Guarino, Leonard Eusebi, Bethany Bracken, and Michael Jenkins Introduction 407 Characterizing Social Behavior in Gaming 409 Game-Based Data Sources 412 Case Studies of SBE Research in Game Environments 422 Conclusions and Future Recommendations 437 Acknowledgments 438 References 438 19 An Artificial Intelligence/Machine Learning Perspective on Social Simulation: New Data and New Challenges 443Osonde Osoba and Paul K. Davis Objectives and Background 443 Relevant Advances 443 Data and Theory for Behavioral Modeling and Simulation 454 Conclusion and Highlights 470 Acknowledgments 472 References 472 20 Social Media Signal Processing 477Prasanna Giridhar and Tarek Abdelzaher Social Media as a Signal Modality 477 Interdisciplinary Foundations: Sensors, Information, and Optimal Estimation 479 Event Detection and Demultiplexing on the Social Channel 481 Conclusions 492 Acknowledgment 492 References 492 21 Evaluation and Validation Approaches for Simulation of Social Behavior: Challenges and Opportunities 495Emily Saldanha, Leslie M. Blaha, Arun V. Sathanur, Nathan Hodas, Svitlana Volkova, and Mark Greaves Overview 495 Simulation Validation 498 Simulation Evaluation: Current Practices 499 Measurements, Metrics, and Their Limitations 500 Proposed Evaluation Approach 507 Conclusions 515 References 515 Part IV Innovations in Modeling 521 22 The Agent-Based Model Canvas: A Modeling Lingua Franca for Computational Social Science 523Ivan Garibay, Chathika Gunaratne, Niloofar Yousefi, and Steve Scheinert Introduction 523 The Language Gap 527 The Agent-Based Model Canvas 530 Conclusion 540 References 541 23 Representing Socio-Behavioral Understanding with Models 545Andreas Tolk and Christopher G. Glazner Introduction 545 Philosophical Foundations 546 The Way Forward 562 Acknowledgment 563 Disclaimer 563 References 564 24 Toward Self-Aware Models as Cognitive Adaptive Instruments for Social and Behavioral Modeling 569Levent Yilmaz Introduction 569 Perspective and Challenges 571 A Generic Architecture for Models as Cognitive Autonomous Agents 575 The Mediation Process 578 Coherence-Driven Cognitive Model of Mediation 581 Conclusions 584 References 585 25 Causal Modeling with Feedback Fuzzy Cognitive Maps 587Osonde Osoba and Bart Kosko Introduction 587 Overview of Fuzzy Cognitive Maps for Causal Modeling 588 Combining Causal Knowledge: Averaging Edge Matrices 592 Learning FCM Causal Edges 594 FCM Example: Public Support for Insurgency and Terrorism 597 US–China Relations: An FCM of Allison’s Thucydides Trap 603 Conclusion 611 References 612 26 Simulation Analytics for Social and Behavioral Modeling 617Samarth Swarup, Achla Marathe, Madhav V. Marathe, and Christopher L. Barrett Introduction 617 What Are Behaviors? 619 Simulation Analytics for Social and Behavioral Modeling 624 Conclusion 628 Acknowledgments 630 References 630 27 Using Agent-Based Models to Understand Health-Related Social Norms 633Gita Sukthankar and Rahmatollah Beheshti Introduction 633 Related Work 634 Lightweight Normative Architecture (LNA) 634 Cognitive Social Learners (CSL) Architecture 635 Smoking Model 639 Agent-Based Model 641 Data 645 Experiments 646 Conclusion 652 Acknowledgments 652 References 652 28 Lessons from a Project on Agent-Based Modeling 655Mirsad Hadzikadic and Joseph Whitmeyer Introduction 655 ACSES 656 Verification and Validation 661 Self-Organization and Emergence 665 Trust 668 Summary 669 References 670 29 Modeling Social and Spatial Behavior in Built Environments: Current Methods and Future Directions 673Davide Schaumann and Mubbasir Kapadia Introduction 673 Simulating Human Behavior – A Review 675 Modeling Social and Spatial Behavior with MAS 678 Discussion and Future Directions 685 Acknowledgments 687 References 687 30 Multi-Scale Resolution of Human Social Systems: A Synergistic Paradigm for Simulating Minds and Society 697Mark G. Orr Introduction 697 The Reciprocal Constraints Paradigm 699 Discussion 706 Acknowledgments 708 References 708 31 Multi-formalism Modeling of Complex Social-Behavioral Systems 711Marco Gribaudo, Mauro Iacono, and Alexander H. Levis Prologue 711 Introduction 713 On Multi-formalism 718 Issues in Multi-formalism Modeling and Use 719 Issues in Multi-formalism Modeling and Simulation 734 Conclusions 736 Epilogue 736 References 737 32 Social-Behavioral Simulation: Key Challenges 741Kathleen M. Carley Introduction 741 Key Communication Challenges 742 Key Scientific Challenges 743 Toward a New Science of Validation 748 Conclusion 749 References 750 33 Panel Discussion:Moving Social-Behavioral Modeling Forward 753Angela O’Mahony, Paul K. Davis, Scott Appling, Matthew E. Brashears, Erica Briscoe, Kathleen M. Carley, Joshua M. Epstein, Luke J. Matthews, Theodore P. Pavlic, William Rand, Scott Neal Reilly, William B. Rouse, Samarth Swarup, Andreas Tolk, Raffaele Vardavas, and Levent Yilmaz Simulation and Emergence 754 Relating Models Across Levels 765 Going Beyond Rational Actors 776 References 784 Part V Models for Decision-Makers 789 34 Human-Centered Design of Model-Based Decision Support for Policy and Investment Decisions 791William B. Rouse Introduction 791 Modeler as User 792 Modeler as Advisor 792 Modeler as Facilitator 793 Modeler as Integrator 797 Modeler as Explorer 799 Validating Models 800 Modeling Lessons Learned 801 Observations on Problem-Solving 804 Conclusions 806 References 807 35 A Complex Systems Approach for Understanding the Effect of Policy and Management Interventions on Health System Performance 809Jason Thompson, Rod McClure, and Andrea de Silva Introduction 809 Understanding Health System Performance 811 Method 813 Model Narrative 815 Policy Scenario Simulation 817 Results 817 Discussion 824 Conclusions 826 References 827 36 Modeling Information and Gray Zone Operations 833Corey Lofdahl Introduction 833 The Technological Transformation of War: Counterintuitive Consequences 835 Modeling Information Operations: Representing Complexity 838 Modeling Gray Zone Operations: Extending Analytic Capability 842 Conclusion 845 References 847 37 Homo Narratus (The Storytelling Species): The Challenge (and Importance) of Modeling Narrative in Human Understanding 849Christopher Paul The Challenge 849 What Are Narratives? 850 What Is Important About Narratives? 851 What Can Commands Try to Accomplish with Narratives in Support of Operations? 856 Moving Forward in Fighting Against, with, and Through Narrative in Support of Operations 857 Conclusion: Seek Modeling and Simulation Improvements That Will Enable Training and Experience with Narrative 861 References 862 38 Aligning Behavior with Desired Outcomes: Lessons for Government Policy from the Marketing World 865Katharine Sieck Technique 1: Identify the Human Problem 867 Technique 2: Rethinking Quantitative Data 869 Technique 3: Rethinking Qualitative Research 876 Summary 882 References 882 39 Future Social Science That Matters for Statecraft 885Kent C. Myers Perspective 885 Recent Observations 885 Interactions with the Intelligence Community 887 Phronetic Social Science 888 Cognitive Domain 891 Reflexive Processes 893 Conclusion 895 References 896 40 Lessons on Decision Aiding for Social-Behavioral Modeling 899Paul K. Davis Strategic Planning Is Not About Simply Predicting and Acting 899 Characteristics Needed for Good Decision Aiding 901 Implications for Social-Behavioral Modeling 918 Acknowledgments 921 References 923 Index 927
Paul K. Davis, PhD, is a senior principal researcher at the RAND Corporation and a professor of policy analysis at the Pardee RAND Graduate School. Angela O'Mahony, PhD, is a senior political scientist at the RAND Corporation and a professor at the Pardee RAND Graduate School. Jonathan Pfautz, PhD, is a Program Manager at DARPA.
This volume describes frontiers in social-behavioral modeling for contexts as diverse as national security, health, and on-line social gaming. Recent scientific and technological advances have created exciting opportunities for such improvements. However, the book also identifies crucial scientific, ethical, and cultural challenges to be met if social-behavioral modeling is to achieve its potential. Doing so will require new methods, data sources, and technology. The volume discusses these, including those needed to achieve and maintain high standards of ethics and privacy. The result should be a new generation of modeling that will advance science and, separately, aid decision-making on major social and security-related subjects despite the myriad uncertainties and complexities of social phenomena. Intended to be relatively comprehensive in scope, the volume balances theory-driven, data- driven, and hybrid approaches. The latter may be rapidly iterative, as when artificial- intelligence methods are coupled with theory-driven insights to build models that are sound, comprehensible and usable in new situations. With the intent of being a milestone document that sketches a research agenda for the next decade, the volume draws on the wisdom, ideas and suggestions of many noted researchers who draw in turn from anthropology, communications, complexity science, computer science, defense planning, economics, engineering, health systems, medicine, neuroscience, physics, political science, psychology, public policy and sociology. In brief, the volume discusses: Cutting-edge challenges and opportunities in modeling for social and behavioral science Special requirements for achieving high standards of privacy and ethics New approaches for developing theory while exploiting both empirical and computational data Issues of reproducibility, communication, explanation, and validation Special requirements for models intended to inform decision making about complex social systems

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