Skip to main content


Social-Behavioral Modeling for Complex Systems

Paul K. Davis (Editor), Angela O'Mahony (Editor), Jonathan Pfautz (Editor)

ISBN: 978-1-119-48496-7 April 2019 1024 Pages


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 3
Jonathan Pfautz, Paul K. Davis, and Angela O’Mahony

Challenges 5

About This Book 10

References 13

2 Improving Social-Behavioral Modeling 15
Paul 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 49
Rebecca 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 65
Benjamin 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 101
Matthew 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 121
Steven 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 145
Stephen 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 163
Luke 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 187
Gene 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 205
Steven 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 231
Theodore 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 263
Matthew 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 281
Michael 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 311
Amy 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 337
William 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 359
Lynn 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 387
Kiran 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 407
Sean 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 443
Osonde 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 477
Prasanna 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 495
Emily 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 523
Ivan 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 545
Andreas 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 569
Levent 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 587
Osonde 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 617
Samarth 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 633
Gita 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 655
Mirsad Hadzikadic and Joseph Whitmeyer

Introduction 655


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 673
Davide 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 697
Mark G. Orr

Introduction 697

The Reciprocal Constraints Paradigm 699

Discussion 706

Acknowledgments 708

References 708

31 Multi-formalism Modeling of Complex Social-Behavioral Systems 711
Marco 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 741
Kathleen 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 753
Angela 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 791
William 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 809
Jason 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 833
Corey 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 849
Christopher 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 865
Katharine 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 885
Kent 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 899
Paul 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

Supplementary Material from Chapter 8