Social-Behavioral Modeling for Complex Systems

Social-Behavioral Modeling for Complex Systems

by Paul K. Davis, Angela O'Mahony, Jonathan Pfautz
Released April 2019
Publisher(s): Wiley
ISBN: 9781119484967

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Book description

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

Table of contents

  1. Cover
  2. Foreword
    1. References
  3. List of Contributors
  4. About the Editors
  5. About the Companion Website
  6. Part I: Introduction and Agenda
    1. 1 Understanding and Improving the Human Condition: A Vision of the Future for Social‐Behavioral Modeling
      1. Challenges
      2. About This Book
      3. References
    2. 2 Improving Social‐Behavioral Modeling
      1. Aspirations
      2. Classes of Challenge
      3. Inherent Challenges
      4. Selected Specific Issues and the Need for Changed Practices
      5. Strategy for Moving Ahead
      6. Social‐Behavioral Laboratories
      7. Conclusions
      8. Acknowledgments
      9. References
    3. 3 Ethical and Privacy Issues in Social‐Behavioral Research
      1. Improved Notice and Choice
      2. Usable and Accurate Access Control
      3. Anonymization
      4. Avoiding Harms by Validating Algorithms and Auditing Use
      5. Challenge and Redress
      6. Deterrence of Abuse
      7. And Finally Thinking Bigger About What Is Possible
      8. References
  7. Part II: Foundations of Social-Behavioral Science
    1. 4 Building on Social Science: Theoretic Foundations for Modelers
      1. Background
      2. Atomistic Theories of Individual Behavior
      3. Social Theories of Individual Behavior
      4. Theories of Interaction
      5. From Theory to Data and Data to Models
      6. Building Models Based on Social Scientific Theories
      7. Acknowledgments
      8. References
    2. 5 How Big and How Certain? A New Approach to Defining Levels of Analysis for Modeling Social Science Topics
      1. Introduction
      2. Traditional Conceptions of Levels of Analysis
      3. Incompleteness of Levels of Analysis
      4. Constancy as the Missing Piece
      5. Putting It Together
      6. Implications for Modeling
      7. Conclusions
      8. Acknowledgments
      9. References
    3. 6 Toward Generative Narrative Models of the Course and Resolution of Conflict
      1. Limitations of Current Conceptualizations of Narrative
      2. A Generative Modeling Framework
      3. Application to a Simple Narrative
      4. Real‐World Applications
      5. Challenges and Future Research
      6. Conclusion
      7. Acknowledgment
      8. Locations, Events, Actions, Participants, and Things in the Three Little Pigs
      9. Edges in the Three Little Pigs Graph
      10. References
    4. 7 A Neural Network Model of Motivated Decision‐Making in Everyday Social Behavior
      1. Introduction
      2. Overview
      3. Theoretical Background
      4. Neural Network Implementation
      5. Conclusion
      6. References
    5. 8 Dealing with Culture as Inherited Information
      1. Galton's Problem as a Core Feature of Cultural Theory
      2. How to Correct for Treelike Inheritance of Traits Across Groups
      3. Dealing with Nonindependence in Less Treelike Network Structures
      4. Future Directions for Formal Modeling of Culture
      5. Acknowledgments
      6. References
    6. 9 Social Media, Global Connections, and Information Environments: Building Complex Understandings of Multi‐Actor Interactions
      1. A New Setting of Hyperconnectivity
      2. The Information Environment
      3. Social Media in the Information Environment
      4. Integrative Approaches to Understanding Human Behavior
      5. The Ethnographic Examples
      6. Conclusion
      7. References
    7. 10 Using Neuroimaging to Predict Behavior: An Overview with a Focus on the Moderating Role of Sociocultural Context
      1. Introduction
      2. The Brain‐as‐Predictor Approach
      3. Predicting Individual Behaviors
      4. Interpreting Associations Between Brain Activation and Behavior
      5. Predicting Aggregate Out‐of‐Sample Group Outcomes
      6. Predicting Social Interactions and Peer Influence
      7. Sociocultural Context
      8. Future Directions
      9. Conclusion
      10. References
    8. 11 Social Models from Non-Human Systems
      1. Emergent Patterns in Groups of Behaviorally Flexible Individuals
      2. Model Systems for Understanding Group Competition
      3. Information Dynamics in Tightly Integrated Groups
      4. Conclusions
      5. Acknowledgments
      6. References
    9. 12 Moving Social‐Behavioral Modeling Forward: Insights from Social Scientists
      1. Why Do People Do What They Do?
      2. Everything Old Is New Again
      3. Behavior Is Social, Not Just Complex
      4. What is at Stake?
      5. Sensemaking
      6. Final Thoughts
      7. References
  8. Part III: Informing Models with Theory and Data
    1. 13 Integrating Computational Modeling and Experiments: Toward a More Unified Theory of Social Influence
      1. Introduction
      2. Social Influence Research
      3. Opinion Network Modeling
      4. Integrated Empirical and Computational Investigation of Group Polarization
      5. Integrated Approach
      6. Conclusion
      7. Acknowledgments
      8. References
    2. 14 Combining Data‐Driven and Theory‐Driven Models for Causality Analysis in Sociocultural Systems
      1. Introduction
      2. Understanding Causality
      3. Ensembles of Causal Models
      4. Case Studies: Integrating Data‐Driven and Theory‐Driven Ensembles
      5. Conclusions
      6. References
    3. 15 Theory‐Interpretable, Data‐Driven Agent‐Based Modeling
      1. The Beauty and Challenge of Big Data
      2. A Proposed Unifying Principle for Big Data and Social Science
      3. Data‐Driven Agent‐Based Modeling
      4. Conclusion and the Vision
      5. Acknowledgments
      6. References
    4. 16 Bringing the Real World into the Experimental Lab: Technology‐Enabling Transformative Designs
      1. Understanding, Predicting, and Changing Behavior
      2. Social Domains of Interest
      3. The SOLVE Approach
      4. Experimental Designs for Real‐World Simulations
      5. Creating Representative Designs for Virtual Games
      6. Applications in Three Domains of Interest
      7. Conclusions
      8. References
    5. 17 Online Games for Studying Human Behavior
      1. Introduction
      2. Online Games and Massively Multiplayer Online Games for Research
      3. War Games and Data Gathering for Nuclear Deterrence Policy
      4. MMOG Data to Test International Relations Theory
      5. Analysis and Results
      6. Games as Experiments: The Future of Research
      7. Final Discussion
      8. Acknowledgments
      9. References
    6. 18 Using Sociocultural Data from Online Gaming and Game Communities
      1. Introduction
      2. Characterizing Social Behavior in Gaming
      3. Game‐Based Data Sources
      4. Case Studies of SBE Research in Game Environments
      5. Conclusions and Future Recommendations
      6. Acknowledgments
      7. References
    7. 19 An Artificial Intelligence/Machine Learning Perspective on Social Simulation: New Data and New Challenges
      1. Objectives and Background
      2. Relevant Advances
      3. Data and Theory for Behavioral Modeling and Simulation
      4. Conclusion and Highlights
      5. Acknowledgments
      6. References
    8. 20 Social Media Signal Processing
      1. Social Media as a Signal Modality
      2. Interdisciplinary Foundations: Sensors, Information, and Optimal Estimation
      3. Event Detection and Demultiplexing on the Social Channel
      4. Conclusions
      5. Acknowledgment
      6. References
    9. 21 Evaluation and Validation Approaches for Simulation of Social Behavior: Challenges and Opportunities
      1. Overview
      2. Simulation Validation
      3. Simulation Evaluation: Current Practices
      4. Measurements, Metrics, and Their Limitations
      5. Proposed Evaluation Approach
      6. Conclusions
      7. References
  9. Part IV: Innovations in Modeling
    1. 22 The Agent‐Based Model Canvas: A Modeling Lingua Franca for Computational Social Science
      1. Introduction
      2. The Language Gap
      3. The Agent‐Based Model Canvas
      4. Conclusion
      5. References
    2. 23 Representing Socio‐Behavioral Understanding with Models
      1. Introduction
      2. Philosophical Foundations
      3. Simulation and Modeling Approaches for Computational Social Scientists
      4. The Way Forward
      5. Acknowledgment
      6. Disclaimer
      7. References
    3. 24 Toward Self‐Aware Models as Cognitive Adaptive Instruments for Social and Behavioral Modeling
      1. Introduction
      2. Perspective and Challenges
      3. A Generic Architecture for Models as Cognitive Autonomous Agents
      4. The Mediation Process
      5. Coherence‐Driven Cognitive Model of Mediation
      6. Conclusions
      7. References
    4. 25 Causal Modeling with Feedback Fuzzy Cognitive Maps
      1. Introduction
      2. Overview of Fuzzy Cognitive Maps for Causal Modeling
      3. Combining Causal Knowledge: Averaging Edge Matrices
      4. Learning FCM Causal Edges
      5. FCM Example: Public Support for Insurgency and Terrorism
      6. US–China Relations: An FCM of Allison's Thucydides Trap
      7. Conclusion
      8. References
    5. 26 Simulation Analytics for Social and Behavioral Modeling
      1. Introduction
      2. What Are Behaviors?
      3. Simulation Analytics for Social and Behavioral Modeling
      4. Conclusion
      5. Acknowledgments
      6. References
    6. 27 Using Agent‐Based Models to Understand Health‐Related Social Norms
      1. Introduction
      2. Related Work
      3. Lightweight Normative Architecture (LNA)
      4. Cognitive Social Learners (CSL) Architecture
      5. Smoking Model
      6. Agent‐Based Model
      7. Data
      8. Experiments
      9. Conclusion
      10. Acknowledgments
      11. References
    7. 28 Lessons from a Project on Agent‐Based Modeling
      1. Introduction
      2. ACSES
      3. Verification and Validation
      4. Self‐Organization and Emergence
      5. Trust
      6. Summary
      7. References
    8. 29 Modeling Social and Spatial Behavior in Built Environments: Current Methods and Future Directions
      1. Introduction
      2. Simulating Human Behavior – A Review
      3. Modeling Social and Spatial Behavior with MAS
      4. Discussion and Future Directions
      5. Acknowledgments
      6. References
    9. 30 Multi‐Scale Resolution of Human Social Systems: A Synergistic Paradigm for Simulating Minds and Society
      1. Introduction
      2. The Reciprocal Constraints Paradigm
      3. Discussion
      4. Acknowledgments
      5. References
    10. 31 Multi‐formalism Modeling of Complex Social‐Behavioral Systems
      1. Prologue
      2. Introduction
      3. On Multi‐formalism
      4. Issues in Multi‐formalism Modeling and Use
      5. Issues in Multi‐formalism Modeling and Simulation
      6. Conclusions
      7. Epilogue
      8. References
    11. 32 Social‐Behavioral Simulation: Key Challenges
      1. Introduction
      2. Key Communication Challenges
      3. Key Scientific Challenges
      4. Toward a New Science of Validation
      5. Conclusion
      6. References
    12. 33 Panel Discussion: Moving Social‐Behavioral Modeling Forward
      1. Simulation and Emergence
      2. Relating Models Across Levels
      3. Going Beyond Rational Actors
      4. References
  10. Part V: Models for Decision-Makers
    1. 34 Human‐Centered Design of Model‐Based Decision Support for Policy and Investment Decisions
      1. Introduction
      2. Modeler as User
      3. Modeler as Advisor
      4. Modeler as Facilitator
      5. Modeler as Integrator
      6. Modeler as Explorer
      7. Validating Models
      8. Modeling Lessons Learned
      9. Observations on Problem‐Solving
      10. Conclusions
      11. References
    2. 35 A Complex Systems Approach for Understanding the Effect of Policy and Management Interventions on Health System Performance
      1. Introduction
      2. Understanding Health System Performance
      3. Method
      4. Model Narrative
      5. Policy Scenario Simulation
      6. Results
      7. Discussion
      8. Conclusions
      9. References
    3. 36 Modeling Information and Gray Zone Operations
      1. Introduction
      2. The Technological Transformation of War: Counterintuitive Consequences
      3. Modeling Information Operations: Representing Complexity
      4. Modeling Gray Zone Operations: Extending Analytic Capability
      5. Conclusion
      6. References
    4. 37 Homo Narratus (The Storytelling Species): The Challenge (and Importance) of Modeling Narrative in Human Understanding
      1. The Challenge
      2. What Are Narratives?
      3. What Is Important About Narratives?
      4. What Can Commands Try to Accomplish with Narratives in Support of Operations?
      5. Moving Forward in Fighting Against, with, and Through Narrative in Support of Operations
      6. Conclusion: Seek Modeling and Simulation Improvements That Will Enable Training and Experience with Narrative
      7. References
    5. 38 Aligning Behavior with Desired Outcomes: Lessons for Government Policy from the Marketing World
      1. Technique 1: Identify the Human Problem
      2. Technique 2: Rethinking Quantitative Data
      3. Technique 3: Rethinking Qualitative Research
      4. Summary
      5. References
    6. 39 Future Social Science That Matters for Statecraft
      1. Perspective
      2. Recent Observations
      3. Interactions with the Intelligence Community
      4. Phronetic Social Science
      5. Cognitive Domain
      6. Reflexive Processes
      7. Conclusion
      8. References
    7. 40 Lessons on Decision Aiding for Social‐Behavioral Modeling
      1. Strategic Planning Is Not About Simply Predicting and Acting
      2. Characteristics Needed for Good Decision Aiding
      3. Implications for Social‐Behavioral Modeling
      4. Acknowledgments
      5. References
  11. Index
  12. End User License Agreement

Product information

  • Title: Social-Behavioral Modeling for Complex Systems
  • Author(s): Paul K. Davis, Angela O'Mahony, Jonathan Pfautz
  • Release date: April 2019
  • Publisher(s): Wiley
  • ISBN: 9781119484967