Social-Behavioral Modeling for Complex Systems E-Book

Paul K. Davis

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Stevens Institute Series on Complex Systems and Enterprises
• Sprache: Englisch

Seitenzahl: 1812

Veröffentlichungsjahr: 2019

Beschreibung

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

Leseprobe

Table of Contents

Cover

Foreword

References

List of Contributors

About the Editors

About the Companion Website

Part I: Introduction and Agenda

1 Understanding and Improving the Human Condition: A Vision of the Future for Social‐Behavioral Modeling

Challenges

About This Book

References

2 Improving Social‐Behavioral Modeling

Aspirations

Classes of Challenge

Inherent Challenges

Selected Specific Issues and the Need for Changed Practices

Strategy for Moving Ahead

Social‐Behavioral Laboratories

Conclusions

Acknowledgments

References

3 Ethical and Privacy Issues in Social‐Behavioral Research

Improved Notice and Choice

Usable and Accurate Access Control

Anonymization

Avoiding Harms by Validating Algorithms and Auditing Use

Challenge and Redress

Deterrence of Abuse

And Finally

Thinking Bigger

About What Is Possible

References

Part II: Foundations of Social-Behavioral Science

4 Building on Social Science: Theoretic Foundations for Modelers

Background

Atomistic Theories of Individual Behavior

Social Theories of Individual Behavior

Theories of Interaction

From Theory to Data and Data to Models

Building Models Based on Social Scientific Theories

Acknowledgments

References

5 How Big and How Certain? A New Approach to Defining Levels of Analysis for Modeling Social Science Topics

Introduction

Traditional Conceptions of Levels of Analysis

Incompleteness of Levels of Analysis

Constancy as the Missing Piece

Putting It Together

Implications for Modeling

Conclusions

Acknowledgments

References

6 Toward Generative Narrative Models of the Course and Resolution of Conflict

Limitations of Current Conceptualizations of Narrative

A Generative Modeling Framework

Application to a Simple Narrative

Real‐World Applications

Challenges and Future Research

Conclusion

Acknowledgment

Locations, Events, Actions, Participants, and Things in the Three Little Pigs

Edges in the Three Little Pigs Graph

References

7 A Neural Network Model of Motivated Decision‐Making in Everyday Social Behavior

Introduction

Overview

Theoretical Background

Neural Network Implementation

Conclusion

References

8 Dealing with Culture as Inherited Information

Galton's Problem as a Core Feature of Cultural Theory

How to Correct for Treelike Inheritance of Traits Across Groups

Dealing with Nonindependence in Less Treelike Network Structures

Future Directions for Formal Modeling of Culture

Acknowledgments

References

9 Social Media, Global Connections, and Information Environments: Building Complex Understandings of Multi‐Actor Interactions

A New Setting of Hyperconnectivity

The Information Environment

Social Media in the Information Environment

Integrative Approaches to Understanding Human Behavior

The Ethnographic Examples

Conclusion

References

10 Using Neuroimaging to Predict Behavior: An Overview with a Focus on the Moderating Role of Sociocultural Context

Introduction

The Brain‐as‐Predictor Approach

Predicting Individual Behaviors

Interpreting Associations Between Brain Activation and Behavior

Predicting Aggregate Out‐of‐Sample Group Outcomes

Predicting Social Interactions and Peer Influence

Sociocultural Context

Future Directions

Conclusion

References

11 Social Models from Non-Human Systems

Emergent Patterns in Groups of Behaviorally Flexible Individuals

Model Systems for Understanding Group Competition

Information Dynamics in Tightly Integrated Groups

Conclusions

Acknowledgments

References

12 Moving Social‐Behavioral Modeling Forward: Insights from Social Scientists

Why Do People Do What They Do?

Everything Old Is New Again

Behavior Is Social, Not Just Complex

What is at Stake?

Sensemaking

Final Thoughts

References

Part III: Informing Models with Theory and Data

13 Integrating Computational Modeling and Experiments: Toward a More Unified Theory of Social Influence

Introduction

Social Influence Research

Opinion Network Modeling

Integrated Empirical and Computational Investigation of Group Polarization

Integrated Approach

Conclusion

Acknowledgments

References

14 Combining Data‐Driven and Theory‐Driven Models for Causality Analysis in Sociocultural Systems

Introduction

Understanding Causality

Ensembles of Causal Models

Case Studies: Integrating Data‐Driven and Theory‐Driven Ensembles

Conclusions

References

15 Theory‐Interpretable, Data‐Driven Agent‐Based Modeling

The Beauty and Challenge of Big Data

A Proposed Unifying Principle for Big Data and Social Science

Data‐Driven Agent‐Based Modeling

Conclusion and the Vision

Acknowledgments

References

16 Bringing the

Real World

into the Experimental Lab: Technology‐Enabling Transformative Designs

Understanding, Predicting, and Changing Behavior

Social Domains of Interest

The SOLVE Approach

Experimental Designs for

Real‐World

Simulations

Creating Representative Designs for Virtual Games

Applications in Three Domains of Interest

Conclusions

References

17 Online Games for Studying Human Behavior

Introduction

Online Games and Massively Multiplayer Online Games for Research

War Games and Data Gathering for Nuclear Deterrence Policy

MMOG Data to Test International Relations Theory

Analysis and Results

Games as Experiments: The Future of Research

Final Discussion

Acknowledgments

References

18 Using Sociocultural Data from Online Gaming and Game Communities

Introduction

Characterizing Social Behavior in Gaming

Game‐Based Data Sources

Case Studies of SBE Research in Game Environments

Conclusions and Future Recommendations

Acknowledgments

References

19 An Artificial Intelligence/Machine Learning Perspective on Social Simulation: New Data and New Challenges

Objectives and Background

Relevant Advances

Data and Theory for Behavioral Modeling and Simulation

Conclusion and Highlights

Acknowledgments

References

20 Social Media Signal Processing

Social Media as a Signal Modality

Interdisciplinary Foundations: Sensors, Information, and Optimal Estimation

Event Detection and Demultiplexing on the Social Channel

Conclusions

Acknowledgment

References

21 Evaluation and Validation Approaches for Simulation of Social Behavior: Challenges and Opportunities

Overview

Simulation Validation

Simulation Evaluation: Current Practices

Measurements, Metrics, and Their Limitations

Proposed Evaluation Approach

Conclusions

References

Part IV: Innovations in Modeling

22 The Agent‐Based Model Canvas: A Modeling

Lingua Franca

for Computational Social Science

Introduction

The Language Gap

The Agent‐Based Model Canvas

Conclusion

References

23 Representing Socio‐Behavioral Understanding with Models

Introduction

Philosophical Foundations

Simulation and Modeling Approaches for Computational Social Scientists

The Way Forward

Acknowledgment

Disclaimer

References

24 Toward Self‐Aware Models as Cognitive Adaptive Instruments for Social and Behavioral Modeling

Introduction

Perspective and Challenges

A Generic Architecture for Models as Cognitive Autonomous Agents

The Mediation Process

Coherence‐Driven Cognitive Model of Mediation

Conclusions

References

25 Causal Modeling with Feedback Fuzzy Cognitive Maps

Introduction

Overview of Fuzzy Cognitive Maps for Causal Modeling

Combining Causal Knowledge: Averaging Edge Matrices

Learning FCM Causal Edges

FCM Example: Public Support for Insurgency and Terrorism

US–China Relations: An FCM of Allison's Thucydides Trap

Conclusion

References

26 Simulation Analytics for Social and Behavioral Modeling

Introduction

What Are Behaviors?

Simulation Analytics for Social and Behavioral Modeling

Conclusion

Acknowledgments

References

27 Using Agent‐Based Models to Understand Health‐Related Social Norms

Introduction

Related Work

Lightweight Normative Architecture (LNA)

Cognitive Social Learners (CSL) Architecture

Smoking Model

Agent‐Based Model

Data

Experiments

Conclusion

Acknowledgments

References

28 Lessons from a Project on Agent‐Based Modeling

Introduction

ACSES

Verification and Validation

Self‐Organization and Emergence

Trust

Summary

References

29 Modeling Social and Spatial Behavior in Built Environments: Current Methods and Future Directions

Introduction

Simulating Human Behavior – A Review

Modeling Social and Spatial Behavior with MAS

Discussion and Future Directions

Acknowledgments

References

30 Multi‐Scale Resolution of Human Social Systems: A Synergistic Paradigm for Simulating Minds and Society

Introduction

The Reciprocal Constraints Paradigm

Discussion

Acknowledgments

References

31 Multi‐formalism Modeling of Complex Social‐Behavioral Systems

Prologue

Introduction

On Multi‐formalism

Issues in Multi‐formalism Modeling and Use

Issues in Multi‐formalism Modeling and Simulation

Conclusions

Epilogue

References

32 Social‐Behavioral Simulation: Key Challenges

Introduction

Key Communication Challenges

Key Scientific Challenges

Toward a New Science of Validation

Conclusion

References

33 Panel Discussion: Moving Social‐Behavioral Modeling Forward

Simulation and Emergence

Relating Models Across Levels

Going Beyond Rational Actors

References

Part V: Models for Decision-Makers

34 Human‐Centered Design of Model‐Based Decision Support for Policy and Investment Decisions

Introduction

Modeler as User

Modeler as Advisor

Modeler as Facilitator

Modeler as Integrator

Modeler as Explorer

Validating Models

Modeling Lessons Learned

Observations on Problem‐Solving

Conclusions

References

35 A Complex Systems Approach for Understanding the Effect of Policy and Management Interventions on Health System Performance

Introduction

Understanding Health System Performance

Method

Model Narrative

Policy Scenario Simulation

Results

Discussion

Conclusions

References

36 Modeling Information and Gray Zone Operations

Introduction

The Technological Transformation of War: Counterintuitive Consequences

Modeling Information Operations: Representing Complexity

Modeling Gray Zone Operations: Extending Analytic Capability

Conclusion

References

37 Homo Narratus (The Storytelling Species): The Challenge (and Importance) of Modeling Narrative in Human Understanding

The Challenge

What Are Narratives?

What Is Important About Narratives?

What Can Commands Try to Accomplish with Narratives in Support of Operations?

Moving Forward in Fighting Against, with, and Through Narrative in Support of Operations

Conclusion: Seek Modeling and Simulation Improvements That Will Enable Training and Experience with Narrative

References

38 Aligning Behavior with Desired Outcomes: Lessons for Government Policy from the Marketing World

Technique 1: Identify the Human Problem

Technique 2: Rethinking Quantitative Data

Technique 3: Rethinking Qualitative Research

Summary

References

39 Future Social Science That Matters for Statecraft

Perspective

Recent Observations

Interactions with the Intelligence Community

Phronetic Social Science

Cognitive Domain

Reflexive Processes

Conclusion

References

40 Lessons on Decision Aiding for Social‐Behavioral Modeling

Strategic Planning Is Not About Simply Predicting and Acting

Characteristics Needed for Good Decision Aiding

Implications for Social‐Behavioral Modeling

Acknowledgments

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 A view of the book's composition.

Chapter 2

Table 2.1 Comparisons.

Table 2.2 A syntax for discussing model validity.

Table 2.3 Priorities for improving theory and modeling.

Table 2.4 Improving computational and empirical experimentation.

Chapter 5

Table 5.1 Levels of analysis as scale by constancy combinations with published e...

Chapter 6

Table 6.1 LEAP coding example.

Table 6.2 Dual coding of things and locations.

Table 6.3 Nodes in Figure 6.3 with highest current flow betweenness.

Table 6.4 Links recovered using random walk with restart.

Chapter 8

Table 8.1 False positive rates for simulations of treelike inheritance on the In...

Table 8.2 False positive rates for simulations of diffusion of trait variation I...

Chapter 16

Table 16.1 Guidance for developing a systematic representative design.

Chapter 17

Table 17.1 Mapping between MIDs variables and Game X variables.

Table 17.2 Game X conflict data summary.

Table 17.3 Model results for all guilds, full‐time period.

Table 17.4 Game X M4 analysis compared with Barbieri's full model results.

Table 17.5 Model results for large guilds, full‐time period.

Table 17.6 Model results for large guilds, interwar period.

Chapter 18

Table 18.1 Contextual comparisons between live SBE research environments and gam...

Table 18.2 Observable strategies in League of Legends.

Table 18.3 Muxy data characteristics.

Table 18.4 Breakdown of clusters identified through linguistic analysis, identif...

Chapter 20

Table 20.1 Examples of demultiplexed protests.

Table 20.2 Statements presented to participants for propagation analysis.

Table 20.3 Separating support from opposition to a person or cause.

Chapter 22

Table 22.1 A comparison of existing languages for knowledge and flow representat...

Chapter 24

Table 24.1 Abstraction to assist cognition.

Chapter 25

Table 25.1 Table of factors in the public support for insurgency and terrorism (...

Table 25.2 Factors in Thucydides' trap for relations between the United States a...

Chapter 26

Table 26.1 Top results for the query.

Chapter 27

Table 27.1 Example payoff matrix for smoking (S=Smoke, NS=not smoke).

Table 27.2 Payoff matrix governing the diffusion process in the friendship netwo...

Table 27.3 Q‐learning definitions for state, actions, and rewards.

Table 27.4 Experimental settings for smoking value (sv).

Table 27.5 Standard coefficient (beta) values of the applied linear regression t...

Chapter 28

Table 28.1 Theories reflected in ACSES.

Chapter 31

Table 31.1 Enriched ontology derived from the refactored ontologies for Timed In...

Chapter 32

Table 32.1 Summary of scientific challenges for social‐behavioral modeling.

Chapter 33

Table 33.1 The Axtell challenge for moving from simplistic to realistic social s...

Chapter 40

Table 40.1 Dimensions of scenario space.

Table 40.2 Illustrative use of the XLRM framework.

Table 40.3 An illustrative policy scorecard.

Table 40.4 Illustrative policy scorecard with criteria being effectiveness in al...

Table 40.5 Implications for social‐behavioral modeling.

List of Illustrations

Chapter 2

Figure 2.1 A pyramid of military simulations.

Figure 2.2 Spider chart of model validity by five criteria (adapted fro...

Figure 2.3 The ecology to respond to national challenges. Source: Davis...

Figure 2.4 An idealized system view of theory, modeling, and experiment...

Figure 2.5 An SBML for a particular national challenge.

Chapter 6

Figure 6.1 An illustration of the narrative complexity of the Syria co...

Figure 6.2 A notional generative network.

Figure 6.3 Narrative network of the

Three Little Pigs

story.

Figure 6.4 Original graph with link weights replaced by link graph betw...

Chapter 7

Figure 7.1 Diagram of neural network implementation of the motivated d...

Figure 7.2 Graph of output of Leabra activation function.

Figure 7.3 Simple model of a college student.

Figure 7.4 Graph of affordances, interoceptive state, motivation, and b...

Chapter 8

Figure 8.1 Galton's problem in a regression of cross‐cultural genetic ...

Figure 8.2 Inflated variance of slope estimates without and with correc...

Figure 8.3 Example of the standard cross‐cultural sample approach to de...

Figure 8.4 Tree of Indo‐European languages based on Bayesian phylogenet...

Figure 8.5 Nonindependence in a simple social network. Network metrics ...

Figure 8.6 Linguistic network for Indo‐European countries based on Baye...

Chapter 10

Figure 10.1 Brain activation. Brain activation implicated in processin...

Figure 10.2 Physical activity before and after health messages. Falk an...

Figure 10.3 Brain activation. Brain activation in subregion of vmPFC id...

Figure 10.4 Brain networks and social networks. Recent work shows that ...

Chapter 11

Figure 11.1 The nutmeg mannikin,

Lonchura punctulata

. (a) Immature nut...

Figure 11.2 The social cobweb spider,

Anelosimus studiosus

. (a) Individ...

Figure 11.3

Temnothorax longispinosus

host and

Temnothorax americanus

s...

Figure 11.4 Workers of the black garden ant,

Lasius niger

, tending to a...

Figure 11.5 Comparison of nonlinear and linear recruitment. The dashed ...

Chapter 13

Figure 13.1 Illustration of distribution reshaping, RPM process, and r...

Figure 13.2 Evolution of policy positions and uncertainties for complet...

Figure 13.3 Comparison of experimental data and simulations of ASC and ...

Figure 13.4 Overview of integrated modeling‐experiment approach.

Chapter 14

Figure 14.1 Graphical causal model illustrating the causal chain from ...

Figure 14.2 The building blocks of ensemble causal reasoning. (a) Data ...

Figure 14.3 The hypothesized causal relationship between conflict and p...

Figure 14.4 Shadow attractor manifolds for CCM neighborhoods of

X

to ne...

Figure 14.5 Results from CCM analysis illustrating convergence indicati...

Figure 14.6 Israeli support for the peace process (squares) declines wi...

Figure 14.7 Palestinian support for the peace process (squares) decline...

Figure 14.8 Simulation run where the overall commitment to continued co...

Figure 14.9 Causal model of the impact of socioeconomic and demographic...

Chapter 15

Figure 15.1 An illustration of the parameter optimization process.

Figure 15.2 The eventual news network generated from the underlying dat...

Figure 15.3 A description of the framework in the Lu model.

Figure 15.4 An illustration of the four most common CSM structures in t...

Chapter 16

Figure 16.1 SOLVE intervention.

Chapter 18

Figure 18.1 Information on popularity and performance of common decks (...

Figure 18.2 Popularity for a variety of specific types within each clas...

Figure 18.3 Mentions over time for selected Hearthstone decks on Reddit...

Figure 18.4 Activity features of clusters of channels. Figures are form...

Chapter 19

Figure 19.1 A similarity network of modeling methods.

Figure 19.2 Standard reinforcement learning framework.

Figure 19.3 Population proportion of belief as a meme propagates (assum...

Figure 19.4 The multiple networks to which agents belong.

Figure 19.5 A factor tree for public support for insurgency and terrori...

Figure 19.6 An illustrative outcome map showing public support vs. five...

Figure 19.7 A fuzzy cognitive map adding dynamics to a factor‐tree mode...

Figure 19.8 Fusing or combining fuzzy cognitive maps.

Chapter 20

Figure 20.1 The error correction problem formulation.

Chapter 22

Figure 22.1 The Agent‐Based Model Canvas.

Figure 22.2 The Agent‐Based Model Canvas of the Schelling's segregation...

Figure 22.3 The Agent‐Based Model Canvas applied to the Artificial Anas...

Chapter 23

Figure 23.1 Principles of various degrees of collaboration between dis...

Chapter 24

Figure 24.1 Models as mediators.

Figure 24.2 Reference architecture.

Figure 24.3 FeatureSim component architecture.

Figure 24.4 The goal structure of mediation.

Figure 24.5 Connectionist constraint network.

Chapter 25

Figure 25.1 Fragment of a predator–prey fuzzy cognitive map that descr...

Figure 25.2 FCM knowledge combination or fusion by averaging weighted F...

Figure 25.3 Learning FCM causal edge values

with Google Trends time...

Figure 25.4 PSOT factor‐tree model. The figure shows the directed relat...

Figure 25.5 Two fuzzy cognitive maps of the PSOT factor‐tree model. Pan...

Figure 25.6 FCM implementation of Allison's

Thucydides' trap

as it ...

Figure 25.7 Spreading activation time slices in Thucydides' trap FCM. E...

Figure 25.8 Average node activations for input scenarios that converge ...

Chapter 27

Figure 27.1 A schematic representation of the LNA architecture.

Figure 27.2 Cognitive social learners (CSL) architecture.

Figure 27.3 CSL pseudo‐code (blf, beliefs; des, desires; pln, plans; re...

Figure 27.4 Screenshot of the agent‐based model. The advertisements (pe...

Figure 27.5 Comparison between the performances of different normative ...

Figure 27.6 Predicted percentage of smokers for future years.

Figure 27.7 Sensitivity analysis of the values for five coefficient val...

Figure 27.8 Sensitivity analysis of the effects of the two threshold va...

Figure 27.9 The percentage of smoker students in LNA (a) and in CSL (b)...

Chapter 28

Figure 28.1 Schematic of agent's allegiance choice in ACSES model. Shad...

Chapter 29

Figure 29.1 A comprehensive human behavior simulation framework.

Chapter 30

Figure 30.1 The four components of the

RCP

are a cognitive system with...

Chapter 31

Figure 31.1 Workflow using models developed with different formalisms....

Figure 31.2 Modeling hierarchy.

Figure 31.3 A notional representation of the three dimensions for model...

Figure 31.4 Concept map of Social Network modeling constructs.

Figure 31.5 Concept map of Social Network analyses (Rafi 2010).

Figure 31.6 Social Network foundational ontology.

Figure 31.7 Social Network refactored ontology (Rafi 2010).

Figure 31.8 Enriched ontology construction.

Figure 31.9 Graphical representation of the enriched ontology of Table ...

Figure 31.10 Domain identification process.

Figure 31.11 Elements of a domain‐specific multi‐formalism modeling wor...

Chapter 34

Figure 34.1 Knowledge structure of product planning advisor.

Figure 34.2 The advisor series of planning tools.

Figure 34.3 Policy flight simulator for New York City health ecosystem....

Chapter 35

Figure 35.1 Conceptual model of health system performance.

Figure 35.2 Number of patients in the simulated health system from time...

Figure 35.3 Number of

active

patients that had received services in the...

Figure 35.4 Estimated forward liabilities of the simulated health syste...

Figure 35.5 Mean mental health scores of patients within the simulated ...

Figure 35.6 Mean physical health scores of patients within the simulate...

Figure 35.7 Mean satisfaction scores of patients within the simulated h...

Figure 35.8 Mean recovery duration among patients in the simulated heal...

Figure 35.9 Standardized and aggregated overall performance of the simu...

Figure 35.10 Interactive workshop. The workshop was held with system ma...

Chapter 36

Figure 36.1 Kinetic operations reduce the insurgent population.

Figure 36.2

Insurgent math

: insurgent influence operations reverse the ...

Figure 36.3 Influence operations (IOs) counter insurgent influence.

Figure 36.4 Influence operations (IOs) alone without kinetic operations...

Figure 36.5 Causal loop diagram (CLD) of Figure 36.3 IO model.

Figure 36.6 The gray zone exists in the tension between the diplomacy a...

Figure 36.7 System dynamics (SD) stocks and flows can be disaggregated ...

Figure 36.8 Distributed networks can be mapped to regions or

coverages

...

Chapter 38

Figure 38.1 Decomposing the dimensions of symbols.

Figure 38.2 A heat map depiction of how different brands' communities r...

Figure 38.3 Heat map used to identify potential customers for an insura...

Chapter 39

Figure 39.1 Positivity ratio and level of flourishing for 123 national...

Chapter 40

Figure 40.1 An image of analysis aiding decision‐making.

Figure 40.2 A case (scenario) as a point in scenario space.

Figure 40.3 Illustrative region plot: capability to deal with potential...

Figure 40.4 Two‐dimensional projection of raw exploration outcomes. Fic...

Figure 40.5 Processed results revealing pattern.

Figure 40.6 Flagging instability regions when considering interventions...

Figure 40.7 Schematic of adaptive pathways strategy.

Figure 40.8 Different tools for different strengths.

Guide

Cover

Table of Contents

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Stevens Institute Series on Complex Systems and Enterprises

Series Editor: William B. Rouse

Universities as Complex Enterprises: How Academia Works, Why It Works These Ways, and Where the University Enterprise Is Headed • William B. Rouse

Modeling Human‐System Interaction: Philosophical and Methodological Considerations, with Examples • Thomas B. Sheridan

Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach • Saurabh Mittal, Saikou Diallo, and Andreas Tolk

Social-Behavioral Modeling for Complex Systems • Paul K. Davis, Angela O'Mahony, and Jonathan Pfautz

Complexity Challenges in Cyber Physical Systems: Using Modeling and Simulation (M&S) to Support Intelligence, Adaptation and Autonomy • Saurabh Mittal and Andreas Tolk

Healthcare System Engineering Access: Measurement, Inference, and Intervention • Nicoleta Serban

Social-Behavioral Modeling for Complex Systems

Edited by

Paul K. DavisAngela O′MahonyJonathan Pfautz

Copyright

This edition first published 2019

© 2019 John Wiley & Sons, Inc.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Dr. Paul K.Davis, Dr. Angela O'Mahony and Dr. Jonathan Pfautz are to be identified as the editors of the editorial material in this work has been asserted in accordance with law.

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Library of Congress Cataloging‐in‐Publication Data

Names: Davis, Paul K., 1943- editor. | O'Mahony, Angela, editor. | Pfautz, Jonathan, editor.Title: Social-behavioral modeling for complex systems / edited by Dr. Paul K. Davis, Dr. Angela O'Mahony, Dr. Jonathan Pfautz.Description: Hoboken, NJ, USA : Wiley, [2019] | Includes bibliographical references and index. | Identifiers: LCCN 2018046976 (print) | LCCN 2018047389 (ebook) | ISBN 9781119484981 (Adobe PDF) | ISBN 9781119484974 (ePub) | ISBN 9781119484967 (hardcover)Subjects: LCSH: Social psychology–Data processing. | Collective behavior–Simulation methods. | System analysis.Classification: LCC HA29 (ebook) | LCC HA29 .S6736 2019 (print) | DDC 302/.011–dc23LC record available at https://lccn.loc.gov/2018046976

Cover Design: Wiley

Cover Image: © agsandrew/Shutterstock

Foreword

Trying to understand human behavior has probably been a human passion since the first cavewoman tried to figure out why her mate could never remember to wipe his feet before coming inside. We're still at it, working with ever more sophisticated approaches and for increasingly important outcomes.

My own experience in this area started out when I was working at NASA to help design an autopilot for the Space Shuttle vehicle. It worked great when there was no astronaut in the loop, but when they started wrangling the controls themselves, the astronauts were not exactly enamored with my design and the way it behaved. Dismay would be a word that might apply here, but I went back to school to figure out why and soon became enmeshed in trying to understand human self‐motion perception and control, by bringing together established theory, controlled experimentation, and computational modeling. It was an eye‐opener on how complicated even the simplest of human behaviors could be, as well as the beginning of a long foray into developing and using computational models in this arena.

Of course, as I later discovered, there's a long history of this, perhaps going as far back as the 1880s with Ernst Mach and his pioneering work in visual and vestibular psychophysics and certainly at least the 1940s to Norbert Weiner with his introduction of cybernetics and the mathematical modeling of both humans and machines. Since then, many other disciplines have contributed and elaborated on this idea, from the basic sciences of neurophysiology and cognitive science to the more applied efforts in human systems engineering and robotics. Moving into the domain of computational representation has forced many of us to sharpen our approach to describing behaviors, formalizing our theories, and validating them against real data. As one of my mentors once told me (and I paraphrase a bit here): “The rubber hits the road when you start hacking code.”

This movement by the research community has been documented in a number of efforts. In 1998, the National Research Council (NRC) published a review of potential models that might be usefully embedded in existing military simulations to provide greater realism by including the human element (National Research Council 1998). The report concluded that there was no single framework or architecture that could meet all the simulation needs of the services, but it did provide an extensive review of computational behavioral models, in‐depth discussions of different functional areas (e.g. attention, memory, learning, etc.), and considerations for small unit representation (i.e. groups of individuals). A follow‐on NRC study in 2008 provided a somewhat broader review, covering models of not only individuals but also organizations and societies (National Research Council 2008). This study also discussed different categories of models – both formal and informal – and common challenges across the community (e.g. interoperability, inconsistent frameworks, verification, etc.). And, like almost all NRC reports of this ilk, there were a number of recommendations proposed, in this case, covering areas from basic theory development to data collection methods and tools for model building.

Although many insights from these and other studies remain relevant, much has happened in the last decade, in terms of new basic research results, new applications afforded by the acceleration of technology (particularly in sensing, networking, computation, and memory), and, not least, a resurgence of a general interest in natural and artificial cognition, with the recent reemergence of artificial intelligence and machine learning. For example, on the basic research side, a revolution in neuroimaging methods is linking the underpinning of human thinking – across individual and societal levels. On the applied side, masses of data on human behavior are now being collected to describe and predict activity in a huge variety of applications spanning everyday consumer devices, socio‐commercial networks, and population monitoring systems installed by local and national governments, to name a few. Online populations can now support crowd‐sourced and A/B experiments that drive how corporations interact with their customers and governments with their citizens.

A reexamination of the issues addressed by the earlier studies is clearly called for, in light of what's happened over the last decade. This volume does just that and is a particularly welcome addition to the research community. It is structured to address issues of science, modeling, and relationships among theory, modeling, empirical research, and computational social science. It candidly emphasizes past shortcomings in these relationships and current progress that's been made in improving those relationships. One can sense a good deal of excitement among the contributors – both established researchers in their chosen fields and fresh PhDs – because so much is happening on so many fronts, including theory development, data collection, and computational methods of inquiry. It's also a delight to see chapters bringing to bear new insights from the study of nonhuman social systems, neuropsychology, psychology, and anthropology, among other disciplines. And, having spent much of my career concerned about real‐world problems needing insights from the behavioral sciences, I was pleased to see several chapters addressing the frontier problem of how to develop and use social‐behavioral modeling to assist human decision‐making regarding profound social issues, some complicated by equally profound issues of privacy and ethics.

This book represents a carefully curated set of contributions that aim to inspire the current and next generation of researchers – and to encourage how the act of challenging current conceptual boundaries is needed to advance science. Social‐behavioral modeling will continue to be beset by uncertainties because the social‐behavioral phenomena occur in complex adaptive systems into which we have imperfect and sometimes contradictory insight. Nonetheless, such modeling – if undertaken appropriately – can help humans who must plan, operate, and adapt in their complex worlds. One of the editors' themes is most welcome: complexity need not be paralyzing, especially if we take a multidisciplinary use‐centric approach to working on real‐world problems.

All in all, this volume is a welcome contribution that will be valuable to diverse audiences in schoolhouses, research laboratories, and the workplace. It is a collection, not a textbook or single‐author monograph, and it conveys an excellent sense of the current state of the art and the exciting opportunities that are now being exploited. The editors deserve credit for bringing about, organizing, and personally contributing heavily to this volume that merits a prominent place in the libraries of human behavior researchers, as well as those interested in helping solve some of the larger socio‐technical issues facing us. I heartily recommend this volume to all of you.

22 July 2018

Greg L. Zacharias

Weston, Massachusetts

References

1998 National Research Council (1998).

Modeling Human and Organizational Behavior: Application to Military Simulations

. Washington, DC: The National Academies Press

https://doi.org/10.17226/6173

.

2008 National Research Council (2008).

Behavioral Modeling and Simulation: From Individuals to Societies

. Washington, DC: The National Academies Press

https://doi.org/10.17226/12169

.

List of Contributors

Tarek Abdelzaher

Computer Science Department

University of Illinois at Urbana–Champaign

Champaign

IL 61801

USA

Scott Appling

Georgia Tech Research Institute

Atlanta

GA 30318

USA

Rebecca Balebako

RAND Corporation

Santa Monica

CA 90401

USA

Christopher L. Barrett

Biocomplexity Institute and Initiative

University of Virginia

Charlottesville

VA 22904

USA

Danielle S. Bassett

Department of Bioengineering

University of Pennsylvania

Philadelphia

PA 19104

USA

and

Department of Electrical & Systems Engineering

University of Pennsylvania

Philadelphia

PA 19104

USA

and

Department of Neurology

University of Pennsylvania

Philadelphia

PA 19104

USA

and

Department of Physics & Astronomy

University of Pennsylvania

Philadelphia

PA 19104

USA

Rahmatollah Beheshti

School of Public Health

Johns Hopkins University

Baltimore

MD 21218

USA

Leslie M. Blaha

Visual Analytics

Pacific Northwest National Laboratory

Richland

WA 99354

USA

David Blumstein

Charles River Analytics

Cambridge

MA 02138

USA

Bethany Bracken

Charles River Analytics

Cambridge

MA 02138

USA

Matthew E. Brashears

Department of Sociology

University of South Carolina

Columbia

SC 29208

USA

Erica Briscoe

Georgia Tech Research Institute

Atlanta

GA 30318

USA

Kathleen M. Carley

Institute of Software Research

School of Computer Science and Engineering and Public Policy

Carnegie Institute of Technology

Carnegie Mellon University

Pittsburgh

PA 15213

USA

Steven R. Corman

Hugh Downs School of Human Communication

Arizona State University

Tempe, AZ

USA

Gene Cowherd

Department of Anthropology

University of South Florida

Tampa

FL 33620

USA

Paul K. Davis

Pardee RAND Graduate School

Santa Monica

CA 90407

USA

CA 90401

USA

Andrea de Silva

Department of Epidemiology and Preventive Medicine

Alfred Hospital

Monash University

Clayton

VIC 3800

Australia

Laura Epifanovskaya

Sandia National Laboratories

California

Livermore

CA 94551

USA

Joshua M. Epstein

Department of Epidemiology

Agent‐Based Modeling Laboratory

New York University

New York

NY 10003

USA

Leonard Eusebi

Charles River Analytics

Cambridge

MA 02138

USA

Emily B. Falk

Annenberg School for Communication

University of Pennsylvania

Philadelphia

PA 19104

USA

and

Department of Psychology

University of Pennsylvania

Philadelphia

PA 19104

USA

and

Marketing Department

Wharton School

University of Pennsylvania

Philadelphia

PA 19104

USA

Michael Gabbay

Applied Physics Laboratory

University of Washington

Seattle

WA 98105

USA

Ivan Garibay

Department of Industrial Engineering and Management Systems

College of Engineering and Computer Science

University of Central Florida

Orlando

FL 32816

USA

Traci K. Gillig

Annenberg School for Communication and Journalism

University of Southern California

Los Angeles

CA 90007

USA

Prasanna Giridhar

Computer Science Department

University of Illinois at Urbana–Champaign

Champaign

IL 61801

USA

Christopher G. Glazner

Modeling, Simulation,Experimentation, and Analytics

The MITRE Corporation

McLean

VA 22103

USA

Emily Saldanha

Data Sciences and Analytics Group

National Security Directorate

Pacific Northwest National Laboratory

Richland

WA 99354

USA

Mark Greaves

Data Sciences and Analytics Group

National Security Directorate

Pacific Northwest National Laboratory

Richland

WA 99354

USA

Marco Gribaudo

Department of Computer Science

Polytechnic University of Milan

Milan

Italy

Sean Guarino

Charles River Analytics

Cambridge

MA 02138

USA

Chathika Gunaratne

Institute for Simulation and Training

University of Central Florida

Orlando

FL 32816

USA

Mirsad Hadzikadic

Department of Software and Information Systems

Data Science Initiative

University of North Carolina

Charlotte

NC 28223

USA

Nathan Hodas

Data Sciences and Analytics Group

National Security Directorate

Pacific Northwest National Laboratory

Richland

WA 99354

USA

Mauro Iacono

Department of Mathematics and Physics

Università degli Studi della Campania “Luigi Vanvitelli”

Caserta

Italy

Michael Jenkins

Charles River Analytics

Cambridge

MA 02138

USA

David C. Jeong

Annenberg School for Communication and Journalism

University of Southern California

Los Angeles

CA 90007

USA

and

CESAR Lab (Cognitive Embodied Social Agents Research)

College of Computer and Information Science

Northeastern University

Boston

MA 02115

USA

Mubbasir Kapadia

Department of Computer Science

Rutgers University

New Brunswick

NJ

USA

Melvin Konner

Department of Anthropology and Neuroscience and Behavioral Biology

Emory University

Atlanta

GA 30322

USA

Bart Kosko

Department of Electrical Engineering and School of Law

University of Southern California

Los Angeles

CA 90007

USA

Kiran Lakkaraju

Sandia National Laboratories

Albuquerque

NM 87185

USA

Daniel Lende

Department of Anthropology

University of South Florida

Tampa

FL 33620

USA

Josh Letchford

Sandia National Laboratories

California

Livermore

CA 94551

USA

Alexander H. Levis

Department of Electrical and Computer Engineering

George Mason University

Fairfax

VA 22030

USA

Corey Lofdahl

Systems & Technology Research

Woburn

MA 01801

USA

Christian Madsbjerg

3ReD Associates

New York

NY 10004

USA

Achla Marathe

Biocomplexity Institute and Initiative

University of Virginia

Charlottesville

VA 22904

USA

Madhav V. Marathe

Biocomplexity Institute and Initiative

University of Virginia

Charlottesville

VA 22904

USA

Luke J. Matthews

Behavioral and Policy Sciences

RAND Corporation

Boston

MA 02116

USA

and

Pardee RAND Graduate School

Santa Monica

CA 90401

USA

Rod McClure

Faculty of Medicine and Health

School of Rural Health

University of New England

Armidale

NSW 2351

Australia

Laura McNamara

Sandia National Laboratories

Albuquerque

NM 87123

USA

Lynn C. Miller

Department of Communication and Psychology

University of Southern California

Los Angeles

CA 90007

USA

and

Annenberg School for Communication and Journalism

University of Southern California

Los Angeles

CA

USA

Kent C. Myers

Net Assessments

Office of the Director of National Intelligence

Washington

DC 20511

USA

Benjamin Nyblade

Empirical Research Group

University of California Los Angeles School of Law

Los Angeles

CA 90095

USA

Angela O'Mahony

Pardee RAND Graduate School

Santa Monica

CA 90407

USA

CA 90401

USA

Mark G. Orr

Biocomplexity Institute & Initiative

University of Virginia

Charlottesville

VA 22904

USA

Osonde Osoba

RAND Corporation and Pardee RAND Graduate School

Santa Monica

CA 90401

USA

Christopher Paul

RAND Corporation

Pittsburgh

PA 15213

USA

Theodore P. Pavlic

School of Computing, Informatics, and Decision Systems Engineering

and the School of Sustainability

Arizona State University

Tempe

AZ 85287

USA

Glenn Pierce

School of Criminology and Criminal Justice

Northeastern University

Boston

MA 02115

USA

Jonathan Pfautz

Information Innovation Office (I20)

Defense Advanced Research Projects Agency

Arlington

VA 22203

USA

William Rand

Department of Marketing

Poole College of Management

North Carolina State University

Raleigh

NC 27695

USA

Stephen J. Read

Department of Psychology

University of Southern California

Los Angeles

CA 90007

USA

Scott Neal Reilly

Charles River Analytics

Cambridge

MA 02138

USA

Jason Reinhardt

Sandia National Laboratories

California

Livermore

CA 94551

USA

William B. Rouse

School of Systems and Enterprises

Stevens Institute of Technology

Center for Complex Systems and Enterprises

Hoboken

NJ 07030

USA

Scott W. Ruston

Global Security Initiative

Arizona State University

Tempe, AZ

USA

Arun V. Sathanur

Physical and Computational Sciences Directorate

Pacific Northwest National Laboratory

Seattle

WA 98109

USA

Davide Schaumann

Department of Computer Science

Rutgers University

New Brunswick

NJ

USA

Steve Scheinert

Department of Industrial Engineering and Management Systems

University of Central Florida

Orlando

FL 32816

USA

Katharine Sieck

Business Intelligence and Market Analysis

RAND Corporation and Pardee RAND Graduate School

Santa Monica

CA 90401

USA

Amy Sliva

Charles River Analytics

Cambridge

MA 02138

USA

Mallory Stites

Sandia National Laboratories

Albuquerque

NM 87185

USA

Gita Sukthankar

Department of Computer Science

University of Central Florida

Orlando

FL 32816

USA

Samarth Swarup

Biocomplexity Institute and Initiative

University of Virginia

Charlottesville

VA 22904

USA

Jason Thompson

Transport, Health and Urban Design Research Hub

Melbourne School of Design

University of Melbourne

Parkville

VIC 3010

Australia

Andreas Tolk

Modeling, Simulation, Experimentation, and Analytics

The MITRE Corporation

Hampton

VA 23666

USA

Steven H. Tompson

Human Sciences Campaign

U.S. Army Research Laboratory

Adelphi

MD 20783

USA

and

Department of Bioengineering

University of Pennsylvania

Philadelphia

PA 19104

USA

Hanghang Tong

School of Computing, Informatics

and Decision Systems Engineering (CIDSE)

Arizona State University

Los Angeles, CA

USA

Raffaele Vardavas

RAND Corporation and Pardee RAND Graduate School

Santa Monica

CA 90401

USA

Jean M. Vettel

Human Sciences Campaign

U.S. Army Research Laboratory

Adelphi

MD 20783

USA

and

Department of Bioengineering

University of Pennsylvania

Philadelphia

PA 19104

USA

and

Department of Psychological and Brain Sciences

University of California

Santa Barbara

93106 USA

Svitlana Volkova

Data Sciences and Analytics Group

National Security Directorate

Pacific Northwest National Laboratory

Richland

WA 99354

USA

Liyuan Wang

Annenberg School for Communication and Journalism

University of Southern California

Los Angeles

CA 90007

USA

Jon Whetzel

Sandia National Laboratories

Albuquerque

NM 87185

USA

Joseph Whitmeyer

Department of Sociology

University of North Carolina

Charlotte

NC 28223

USA

Levent Yilmaz

Department of Computer Science

Auburn University

Auburn

AL 36849

USA

Niloofar Yousefi

Department of Industrial Engineering and Management Systems

College of Engineering and Computer Science

University of Central Florida

Orlando

FL 32816

USA

About the Editors

Paul K. Davis is a senior principal researcher at RAND and a professor of policy analysis in the Pardee RAND Graduate School. He holds a BS in chemistry from the University of Michigan and a PhD in chemical physics from the Massachusetts Institute of Technology. After several years at the Institute for Defense Analyses focused primarily on the physics, chemistry, and interpretation of rocket observables, he joined the US government to work on strategic nuclear defense programs and related arms control. He became a senior executive in the Office of the Secretary of Defense and led studies relating to both regional and global military strategy and to program development. Subsequently, Dr. Davis joined the RAND Corporation. His research has involved strategic planning; resource allocation and decision aiding; advanced modeling, simulation, gaming, and analysis under uncertainty; deterrence theory; heterogeneous information fusion; and integrative work using social sciences to inform national strategies in defense and social policy. Dr. Davis teaches policy analysis and modeling of complex problems. He has served on numerous national panels and journals' editorial boards.

Angela O'Mahony is a senior political scientist at the RAND Corporation and a professor at the Pardee RAND Graduate School. Her research has focused on how international political, economic, and military ties affect policy‐making. Some of the topics she has examined are the effectiveness of US security cooperation and defense posture, the implications of international political and economic scrutiny on governments' decision‐making, the causes and consequences of transnational political behavior, public support for terrorism, and the role of social media in public policy analysis. From 2003 to 2011, O'Mahony was an assistant professor at the University of British Columbia. She received her PhD in political science from the University of California, San Diego.

Jonathan Pfautz is a program manager at DARPA and previously led cross‐disciplinary research and guided system development and deployment at Charles River Analytics. His efforts spanned research in social science, neuroscience, cognitive science, human factors engineering, and applied artificial intelligence. Dr. Pfautz holds a doctor of philosophy degree in computer science from the University of Cambridge. He also holds degrees from the Massachusetts Institute of Technology: a master of engineering degree in computer science and electrical engineering, a bachelor of science degree in brain and cognitive sciences, and a bachelor of science degree in computer science and engineering. Dr. Pfautz has published more than 60 peer‐reviewed conference and journal publications and 5 book chapters. He holds five patents.

About the Companion Website

This book is accompanied by a companion website:

www.wiley.com/go/Davis_Social‐Behavioralmodeling

The website includes:

Supplementary material to the eighth chapter.

Part IIntroduction and Agenda

1Understanding and Improving the Human Condition: A Vision of the Future for Social‐Behavioral Modeling

Jonathan Pfautz1, Paul K. Davis2 and Angela O'Mahony2

1Information Innovation Office (I20), Defense Advanced Research Projects Agency, Arlington, VA, 22203‐2114, USA

2RAND Corporation and Pardee RAND Graduate School, Santa Monica, CA, 90407-2138, USA

Technology is transforming the human condition at an ever‐increasing pace. New technologies emerge and dramatically change our daily lives in months rather than years. Yet, key aspects of the human condition – our consciousness, personalities and emotions, beliefs and attitudes, perceptions, decisions and behaviors, and social relationships – have long resisted description in terms of scientific, falsifiable laws like those found in the natural sciences. Past advances in our knowledge of the human condition have had valuable impacts,1 but much more is possible. New technologies are providing extraordinary opportunity for gaining deeper understanding and, significantly, for using that understanding to help realize the immense positive potential of the humankind.

In the information age our understanding of the human condition is deepening with new ways to observe, experiment, and understand behavior. These range from, say, identifying financial and spatiotemporal data that correlate with individual well‐being to drawing on the narratives of social media and other communications to infer population‐wide beliefs, norms, and biases. An unprecedented volume of data is available, an astonishing proportion of which describes human activity and can help us explore the factors that drive behavior. Statistical correlations from such data are already helping to inform our understanding of human behavior. New experimentation platforms have the potential to support both theory‐informed and data‐driven analysis to discover and test the mechanisms that underlie human behavior. For example, millions of users of a social website or millions of players of online games can be exposed to different carefully controlled situations – within seconds – across regional and cultural boundaries. Such technologies enable heretofore impossible forms and scales of experimentation. At the same time, these new capabilities raise important issues of how to perform such experimentation, how to correctly interpret the results, and, critically, how to ensure the highest ethical standards.

Such advances mean that theory development and testing in the social‐behavioral sciences are poised for revolutionary changes. Behavioral theories, whether based on observation, in situ experiments, or laboratory experiments can now be revisited with new technology‐enabled instruments. Applying these new instruments requires confronting issues of reproducibility, generalizability, and falsifiability. Doing so will help catalyze new standards for scientific meaning in the social‐behavioral sciences. The massive scale of some such studies will require complex experimental designs, but these could also enable substantially automated methods that can address many problems of reproducibility and generalizability.

Similarly, representation of knowledge about the human condition is poised for revolution. Using mathematics and computation to formally describe human behavior is not new (Luce et al. 1963), but new and large‐scale data collection methods require us to reconsider how to best represent, verify, and validate knowledge in the social and behavioral sciences. New approaches are needed to capture the complex, multiresolution, and multifaceted nature of the human condition as studied with different observational and experimental instruments. Capturing this knowledge will require new thinking about mathematical and computational formalisms and methods, as well as attention to such engineering hurdles as achieving computational tractability.