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

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
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 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