Modeling sociocultural influences on decision making understanding conflict enabling stability

Modeling
Sociocultural
Influences on
Decision Making


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Modeling
Sociocultural
Influences on
Decision Making
UNDERSTANDING CONFLICT,
ENABLING STABILITY

Edited by

Joseph V. Cohn • Sae Schatz
Hannah Freeman • David J. Y. Combs


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Library of Congress Cataloging-in-Publication Data
Names: Cohn, Joseph V., editor.
Title: Modeling sociocultural influences on decision making : understanding
conflict, enabling stability / editors, Joseph V. Cohn, Sae Schatz, Hannah
Freeman, David J.Y. Combs.
Description: Boca Raton, FL : CRC Press, 2016. | Series: Human factors and
ergonomics series | Includes bibliographical references and index.
Identifiers: LCCN 2016011243 | ISBN 9781498736695 (alk. paper)
Subjects: LCSH: Conflict management--Social aspects. | Social conflict. |
Culture conflict. | Decision making--Social aspects. | Applied sociology.
Classification: LCC HM1126 .M64 2016 | DDC 303.6/9--dc23
LC record available at />Visit the Taylor & Francis Web site at

and the CRC Press Web site at



This book is dedicated to our families, friends, and colleagues whose
guidance, support, and mentorship provided us with the inspiration
and motivation to see this project through to completion.

Contents
Foreword ...................................................................................................................................... xiii
Editors ........................................................................................................................................... xix
Contributors ................................................................................................................................. xxi
Introduction ................................................................................................................................ xxv

Section I

Building Theories

1. Expeditionary Modeling for Megacities and Other Dense Urban Areas ...................5
Brian P. Kettler, Rachel G. Hingst, and Mark A. Hoffman
2. More than Just a Story: Narrative Insights into Comprehension, Ideology,
and Decision Making .......................................................................................................... 27
Scott W. Ruston
3. The Spread of Information via Social Media .................................................................43
Brian M. Fairlie
4. The Spread of Opinions in Societies ................................................................................ 61
Boleslaw K. Szymanski, Omar Lizardo, Casey Doyle, Panagiotis D. Karampourniotis,
Pramesh Singh, Gyorgy Korniss, and Jonathan Z. Bakdash
5. Culture’s Influences on Cognitive Reflection .................................................................85
Vladimíra Čavojová and Róbert Hanák
6. Cultural Influences on Cognitive Biases in Judgment and Decision Making:
On the Need for New Theory and Models for Accidents and Safety ...................... 103
Atsuo Murata

Section II

Collecting and Analyzing Data

7. The Sixty Percent Mission: An Introduction to High-Risk Ethnography
Process and Protocol in Support of the US Army’s Civil Affairs
Humanitarian Mission ...................................................................................................... 115
Tracy Saint Benoit, Clarissa Graffeo, Mark A. Carter, and Col. Richard Swisher (Ret)
8. Challenges in Connecting with the Disconnected: An Introduction
to Connecting with Communities Disconnected from the Rest of the World ....... 135
Christina S. Kang

ix


x

Contents

9. They Are Beyond WEIRD: Helpful Frameworks for Conducting Non-WEIRD
Research................................................................................................................................ 163
David J. Y. Combs, Sarai Blincoe, Christopher P. Garris, and Eric S. Vorm
10. Deciphering the Emic Perspective in Data in Order to Assess Threat .................... 181
Laurie Fenstermacher and Lawrence A. Kuznar
11. Collecting Data and Semantic Content via Mobile Devices ..................................... 205
Alper Caglayan and Laura Cassani
12. Measuring Changes in Attitudes: Using Factor Analysis to Track Population
Attitudes Spatially and Temporally ............................................................................... 221
Joseph Maddux and Jeffrey Appleget
13. Gaining Insight by Applying Geographical Modeling.............................................. 243
Erman Çakıt and Waldemar Karwowski

Section III

Building and Validating Sociocultural Models

14. Sociocultural Capability Requirements across All Phases of Military
Operations ............................................................................................................................ 269
Walter L. Perry
15. Methods to Characterize and Manage Uncertainty for Sociocultural
Applications ......................................................................................................................... 289
Perakath Benjamin, Kalyan Vadakkeveedu, and Satheesh Ramachandran
16. Validating Causal and Predictive Claims in Sociocultural Models ........................ 315
Amy Sliva, Scott Neil Reilly, John Chamberlain, and Randy Casstevens
17. Rapid Generation of Political Conflict Simulations for Scenarios
around the World ................................................................................................................ 335
Barry G. Silverman, David Q. Sun, Nathan Weyer, and Gnana K. Bharathy
18. Detailed Model Development Case Study: The Peace Game .................................... 361
Matthew J. Powers

Section IV

Applying Sociocultural Models to Gain
Insight into Conflict and Instability

19. Using the Social Framework Model of Trust to Better Understand Trust
in Government .................................................................................................................... 397
Walter W. Kulzy, David J. Y. Combs, and Ronald D. Fricker, Jr.
20. Understanding Public Opinion toward Violent Extremists ......................................423
Lewis A. Anderson and Ronald D. Fricker, Jr.


Contents

xi

21. Modeling Sociocultural Influences on Decision Making: Assessing Conflict
and Stability ........................................................................................................................ 449
Michael L. Bernard, George A. Backus, Asmeret Bier Naugle, Robert F. Jeffers,
and Regan W. Damron
22. Modeling Social System Resiliency: An Agent-Based Multiscale Approach ........ 473
Steven B. Hall, Curtis L. Blais, and Ryan G. Baird
23. Applying Modeling and Simulation to Foreign Policy: An Afghan Example....... 495
Corey Lofdahl
Subject Index .............................................................................................................................. 511
Author Index ............................................................................................................................... 535



Foreword

Why Is This Topic Important?
Our world is increasingly interconnected in terms of communications and commerce. Global
interdependence is generally a stabilizing factor. Yet at the same time, our global commons
are increasingly congested, competitive, and contested, which destabilizes our planet (Irving
2015; Puşcaş 2010). With nearly $2 trillion in global research and development, the rate of
technological change will continue to accelerate, which will likely bring nations and their
peoples closer and more intimately connected and codependent. This will further raise the
potential for both transnational threats and conflict as well as transnational opportunities
for peace and prosperity. One of the key dimensions of these societal tectonic movements
is culture. Unique languages, art, cuisine, histories, and traditions provide a rich tapestry to
our planet but also sow the seeds for potential culture clash. Understanding beliefs, attitudes,
and behaviors across multiple cultures in this increasingly wired world is an essential skill

to navigating and shaping this human terrain to ensure a more stable and prosperous world.

Background
Since the emergence of early descriptions of societies, humankind has had a thirst for
understanding social, cultural, and behavioral aspects of humans and groups. One of
the largest modern reviews of scientific sociocultural research was the Department of
Defense’s Human Social Cultural Behavioral (HSCB) modeling program. One of the program’s goals was to create a global and persistent indications and warnings capability that
would complement and enhance conventional sensors by leveraging massive open source
data at a global scale to support improved situation awareness, understanding, and decision making. An integrating vision of this program was the notion of a social radar, an
ability to sense and track events in the human domain. Metaphorically, the vision for social
radar was motivated by sensing science, including passive and active methods. For example, in 1490, Leonardo da Vinci inserted a tube in water to detect vessels. Centuries later,
in 1914, sonar (sound navigation and ranging) was invented to penetrate or see through
water, using sound propagation to navigate, communicate, or detect objects. In 1941, radar
(radio detection and ranging) was invented to use radio waves to see objects (range, angle,
and velocity) through air. Soon after astronomer William Herschel discovered nonvisible
infrared radiation in 1800, detectors and imagers were invented, and eventually humans
were provided the capacity to see with little or no (visible) light. An analogous sensing
modality would be valuable to detect human perceptions, attitudes, beliefs, and behaviors
as well as to geolocate and track these to support smart engagement with multicultural
populations in support of defense, diplomacy, and development (Costa and Boiney 2012).
xiii


xiv

Foreword

Analogous to classical radar or sonar and drawn from this legacy, social radar would ideally provide persistent, real-time, anonymous, global access to multilingual, multicultural,
multimodal information about geolocated perceptions, beliefs, attitudes, and behavior in a
manner that is secure and privacy preserving. Taking this analogy one step further, HSCB

brought to operational utility the Defense Advanced Research Projects Agency-created
worldwide integrated crisis early warning system for the US Southern Command, US Pacific
Command, and others, which could monitor and forecast, in near real-time, destabilizing
events in a commander’s area of responsibility and identify key drivers to instability. These
early systems effort to provide actionable links between social sensing and engagement
should enable users to
• Understand: Capabilities grounded in social and behavioral sciences to support
the perception and the comprehension of sociocultural features and dynamics in
an operational environment
• Detect: Capabilities to discover, distinguish, and locate operationally relevant
sociocultural signatures through collection, processing, and analysis of sociocultural behavior data
• Forecast: Capabilities for tracking and forecasting change in entities and phenomena of interest along multiple dimensions (time, space, social networks, types of
behavior) through persistent sensing and modeling of the environment
• Mitigate: Capabilities to develop, order/prioritize, execute, and measure courses of
action grounded in the social and behavioral sciences that are intended to influence entities and phenomena of interest
Of course social sensing is of value well beyond defense applications. For example, one
of the themes explored in this collection is how social media data can be exploited to
understand how different cultures make decisions. For example, Massachusetts Institute
of Technology (MIT) Media Lab’s Sandy Pentland, author of Honest Signals, collected and
analyzed data from mobile sensors to model human networks and predict human behavior using features such as proximity, friends, and colocation. Pentland was interested in
understanding how social exposure predicts behavior. Studying 65 young families over a
year, he found that he could predict with 45% accuracy the applications individuals would
download based on the behaviors of those in their networks. A subsequent electricity
incentive study in Switzerland (Mani, Rahwan, and Pentland 2013) found that social network incentives (of local friends) were four times as efficient as standard incentives. Wang,
Abdelzaher, and Kaplan (2015) provide a recent overview of social sensing.
Social sensing can be useful not only for advertising but also for enhancing health. For
example, recognizing that 68% of Americans were overweight and costing over $150 billion a year, $62 billion in Medicare cost alone, demonstrates the need for intervention programs to reduce future healthcare costs. In their research on persistent health assessment
tools (PHAT), researchers Meredith Keybl and John Henderson (2015) created a model to
provide timely predictions of obesity rates at the state level from tweets. PHAT provides
tailored obesity demographic and regional groups for public health policy makers (MITRE

2014). By providing finer grained and faster (city, monthly) reporting and decisions on the
success of obesity prevention programs beyond traditional surveillance, PHAT enables
program managers to allocate resources more efficiently. An interactive dashboard enables
public health policy makers to (1) browse, cluster, and search tweets and (2) visualize
behavioral risk factor surveillance system data to finer timeline scales.


Foreword

xv

More broadly, we might seek to apply social radar to not only improve the well-being
of particular individuals or sectors such as energy and healthcare, but also change entire
societies. For example, while we commonly focus on enhancing productivity and gross
national product, an equally important measure is happiness. If the pursuit of happiness
is an end state, the gross national happiness or the gross global happiness is perhaps as
important to measure (Dodds et al. 2011). Some researchers have noted that happiness is
not necessarily causally related to financial success. However, we can begin to measure
happiness on a large scale (with small geospatial and temporal intervals). Consider the
hedonometer (www.hedonometer.org), which senses local and global happiness. The system performs both offline and real-time analysis of emotional assessments. Researchers
initially used a mechanical turk to capture five million human scores of the degree of
positive valence of 100,000 words in 10 languages across 24 corpora including books, web,
news, social media, movies, and even songs (Dodds et al. 2015). Words like laughter, happiness, and love appear at the top. Spanish corpora turn out to be the most positive. Coupled
with Google Earth visualizations, this enables tracking happiness globally as well as temporally. The hedonometer computational back end processes and parses any digitized text
(e.g., Twitter, the New York Times) and localization data. Its back end can run on cloud computing solutions (e.g., Amazon Web Services) to compute daily and historical hedonometric values for a variety of languages and corpora, to include the analysis of real-time feeds.
These examples in energy, health, and happiness illustrate the broad importance of
sociocultural sensing. The chapters in this book provide a broad set of experiences and
perspectives, providing important insights for the future of human interaction, both positive and negative, and some level of understanding into how to shape more opportunitybased interactions for more resilient and prosperous societies.

Why Is This Book Important?

This collection of chapters in an increasingly important area contributes to the scientific
foundation for better understanding cross-cultural behavior. It is carefully organized
to increase the reader’s awareness of theories, data and analysis that support or refine
those theories and associated models, creation and validation of sociocultural models,
and application of these models to conflict and instability. Culture-based behavior models
promise more resilient insights into societies across the globe. Those insights promise to
help humans better anticipate conflict and cooperation. This situational and predictive
intelligence can foster wiser decisions and increased collective progress.

What Core Questions Are Explored in the Book?
Representing a multiplicity of perspectives, this book provides a unique collection of
insights into some fundamental questions including the following:
• How sensitive and comprehensive are current cross-cultural theories of human
behavior? Where are the important gaps?


xvi

Foreword

• What are the most important entities, attitudes, social relations, and behaviors to
capture in cross-cultural models?
• What are the best methods and tools to capture and represent individual and
cross-cultural models?
• What types and sources of data would help create or validate cross-cultural
models?
• What classes of global societal needs are these theories and models effective for
(e.g., economic forecasting, stability and conflict prediction, global policy, global
policing, disease management, urban planning, sociocultural econometrics, critical infrastructure forecasting, attitude awareness, social policy development)?
• How will modeling and simulation for multiscale sociocultural analysis change

given the rapid growth of the Internet of Things, mobile platforms, cognitive/
emotional computing, and other emerging advanced technologies?
• How can we collect data, potentially from existing platforms (e.g., financial, medical, telecommunications, critical infrastructure, social media) to provide deeper
and broader insight into cross-cultural human behavior?
• How do we counter denial and deception to get genuine understanding of security and stability in nations?
• How do we overcome our individual, social, cultural, and national biases to gain
clearer understanding of other nations and their interests and intents?
• How confident are we in the validity and the applicability of cross-cultural models to various environments?

Conclusion
We are blessed to live in an age of abundance, yet we are faced with challenges, some
persistent—poverty, ignorance, and injustice—as well as some contemporary ones such as
earnings inequality gaps, lure of the sound bite, environmental instability, and resistance,
either reasoned or violent, to many components of globalization. The promise of crosscultural understanding in an era of big data is the possibility for personalized matchmaking in work, life, and love; detection and mitigation of deception in global communications;
reduction and offsetting of biases ranging from personal to national; emotionally sensitive
interfaces; tailored and culturally sensitive group facilitation and dispute arbitration; and
more lasting and stable global governance. As I write this on the dawn of a new year, I
hope the reader will find that this collection provides fresh insights into bringing to life
the promise of cross-cultural understanding.
Mark T. Maybury
Vice President
Chief Technology Officer and Chief Security Officer
The MITRE Corporation
Bedford, Massachusetts


Foreword

xvii

References
Costa, B., and Boiney, J. 2012. Social radar. />.pdf.
Dodds, P. S. et al. 2011. Temporal patterns of happiness and information in a global social network:
Hedonometrics and Twitter. PloS one 6.12: e26752. />=10.1371/journal.pone.0026752.
Dodds, P. S. et al. 2015. Human language reveals a universal positivity bias. Proceedings of the
National Academy of Science vol. 112 no. 8., 2389–2394. />/112/8/2389.abstract.
Irving, C. 2015, August 21. Interdependence day: Contending with a new global order. RAND Review.
/>-new-global-order.html.
Keybl, M., Henderson, J., Zarrella, G., Gibson, J., and Kluchnik, M. 2015. Supplementing obesityrelated surveillance with persistent health assessment tools. Online Journal of Public Health
Informatics 7(1): e86. />Mani, A., Rahwan, I., and Pentland, S. 2013. Including Peer Pressure to Promote Cooperation.
Scientific Reports 3: 1735. doi:10.1038/srep01735.
MITRE. 2014, December. Does this tweet make me look PHAT? Tracking obesity trends with social
media. />-tracking-obesity-trends-with-social.
Puşcaş, V. 2010, February. Management of post-crisis global interdependencies. Proceedings of the
International Economics Congress on an Interdisciplinary Analysis of the Roles of Global Politics and
Civil Society in International Economics, Berlin. />/content/articles/biec/speakers/speakers-pages/files/Managing_the_Post_Crisis_Global
_Economic_Interdependence_-_Dr._Puscas.pdf.
Wang, D., Abdelzaher, T., and Kaplan, L. 2015. Social Sensing: Building Reliable Systems on Unreliable
Data. New York: Elsevier.

Acknowledgments
Special thanks to Kristin Heckman, Lisa Costa, Frank Stech, Barry Costa, Jill Drury, Brian
Tivnan, Kerry Buckley, and Mike Cenkl for their feedback on this Foreword.



Editors
Joseph V. Cohn, PhD, earned his BS in biology from the University of Illinois–Urbana
Champaign in 1993. He earned his PhD in neuroscience from Brandeis University in 1998.
He has coauthored more than 100 publications and presented talks to national and international professional conferences on a host of topics, ranging from foundational neuroscience to the nature of technical innovation. He has coedited a three-volume book series

focusing on all aspects of training system development and a single-volume book on
enhancing human performance in high-risk environments. He has received industry and
professional society awards for his research across the spectrum of the human performance and biomedical sciences. He cochaired the Applied Human Factors and Ergonomics
Association’s Cross-Cultural Decision Making Conference from 2013 to 2015 and is a fellow of the American Psychological Association and the Society of Military Psychologists,
as well as an associate fellow of the Aerospace Medical Association.
Sae Schatz, PhD, is an applied human-systems researcher, learning science professional,
and cognitive scientist. She has headed an array of applied research efforts, authored more
than 50 peer-reviewed scholarly publications, led the development of three military textbooks, and received industry awards for both her publications and her research efforts.
Schatz cochairs the Applied Human Factors and Ergonomics Association’s Cross-Cultural
Decision Making Conference (2013–), is a service principal for the Interservice/Industry
Training, Simulation and Education Conference (2015–), and supports international science
and technology working groups associated with the North Atlantic Treaty Organization,
Partnership for Peace, and The Technical Cooperation Program. Schatz also maintains
close ties with her alma mater, the University of Central Florida, where she earned her
PhD in human systems in modeling and simulation in 2008.
Hannah Freeman earned her Bachelor of Arts degrees in international studies (Russian
and Eastern European studies) and Hispanic studies from Illinois Wesleyan University,
where she was awarded Phi Beta Kappa, in 2012. Freeman earned her Master of Science
degree in comparative politics (conflict studies) from The London School of Economics
and Political Science in 2013, where she also received a Russian Language Certificate. Her
research interests include the former Soviet Union and Soviet bloc, post-Soviet Russia,
human rights, national and ethnic conflict, radicalization, political violence, and terrorism.
David J. Y. Combs, PhD, earned his BA in psychology from Simpson University in 2003.
He earned his PhD in experimental social psychology from the University of Kentucky in
2010. He has also completed certifications in political psychology (Stanford University),
analysis of incomplete data sets (University of Michigan), and Afghanistan–Pakistan
regional expertise. He has completed additional coursework (applied survey sampling)
with The George Washington University. He has coauthored dozens of papers, conference presentations, and book chapters on social psychological topics such as trust, attitude change, experience of humiliation, and emotions resulting from political events. He is
especially interested in applying social psychological theory and methods to understanding cross-cultural interactions within the irregular warfare context.
xix

Contributors

Lewis A. Anderson
Special Operations Command Korea
(SOCKOR)
Seoul, South Korea

Alper Caglayan
Milcord LLC
Waltham, Massachusetts

Jeffrey Appleget
Naval Postgraduate School
Monterey, California

Erman Çakıt
Department of Industrial Engineering
Aksaray University
Aksaray, Turkey

George A. Backus
Sandia National Laboratories
Albuquerque, New Mexico

Mark A. Carter
Intelligent Software Solutions, Inc.
Independence, Missouri

Ryan G. Baird
Joint Warfare Analysis Center
King George, Virginia

Laura Cassani
Milcord LLC
Waltham, Massachusetts

Jonathan Z. Bakdash
US Army Research Laboratory
Aberdeen, Maryland

Randy Casstevens
OpenWare
Herndon, Virginia

Perakath Benjamin
Knowledge Based Systems, Inc.
College Station, Texas

Vladimíra Čavojová
Centre for Social and Psychological
Sciences
Institute of Experimental Psychology
Slovak Academy of Sciences
Bratislava, Slovakia

Michael L. Bernard
Sandia National Laboratories

Albuquerque, New Mexico
Gnana K. Bharathy
University of Pennsylvania
Philadelphia, Pennsylvania
Curtis L. Blais
Naval Postgraduate School
Monterey, California
Sarai Blincoe
Longwood University
Farmville, Virginia

John Chamberlain
Charles River Analytics
Cambridge, Massachusetts
David J. Y. Combs
Washington, D.C.
Regan W. Damron
Booz Allen Hamilton GmbH
Stuttgart, Germany

xxi


xxii

Casey Doyle
Network Science Technology Center
Rensselaer Polytechnic Institute
Troy, New York
Brian M. Fairlie

Washington, D.C.

Contributors

Robert F. Jeffers
Sandia National Laboratories
Albuquerque, New Mexico
Christina S. Kang
California

Laurie Fenstermacher
US Air Force Research Laboratory
Wright-Patterson, Ohio

Panagiotis D. Karampourniotis
Network Science Technology Center
Rensselaer Polytechnic Institute
Troy, New York

Ronald D. Fricker, Jr.
Statistics Department
Virginia Polytechnic Institute and State
University
Blacksburg, Virginia

Waldemar Karwowski
Department of Industrial Engineering
and Management Systems
University of Central Florida
Orlando, Florida

Christopher P. Garris
Metropolitan State University of Denver
Denver, Colorado

Brian P. Kettler
Lockheed Martin Advanced Technology
Laboratories
Arlington, Virginia

Clarissa Graffeo
College of Education and Human
Performance
University of Central Florida
Orlando, Florida
Steven B. Hall
Naval Postgraduate School
Monterey, California
Róbert Hanák
Centre for Social and Psychological
Sciences
Institute of Experimental Psychology
Slovak Academy of Sciences
Bratislava, Slovakia
Rachel G. Hingst
Lockheed Martin Advanced Technology
Laboratories
Arlington, Virginia
Mark A. Hoffman
Lockheed Martin Advanced Technology

Laboratories
Kennesaw, Georgia

Gyorgy Korniss
Network Science Technology Center
Rensselaer Polytechnic Institute
Troy, New York
Walter W. Kulzy
United States Central Command
Model and Simulation
Tampa, Florida
Lawrence A. Kuznar
Indiana University–Purdue University
at Fort Wayne
Fort Wayne, Indiana
Omar Lizardo
Department of Sociology
University of Norte Dame
Notre Dame, Indiana
Corey Lofdahl
Charles River Analytics
Cambridge, Massachusetts


xxiii

Contributors

Joseph Maddux
Operations Analysis Directorate

United States Marine Corps
Quantico, Virginia
Atsuo Murata
Graduate School of Natural Science
and Technology
Okayama University
Okayama, Japan
Asmeret Bier Naugle
Sandia National Laboratories
Albuquerque, New Mexico
Walter L. Perry
RAND Corporation
Arlington, Virginia
Matthew J. Powers
Operations Research Analyst
The Joint Center for International
Security Force Assistance
Fort Leavenworth, Kansas
Satheesh Ramachandran
Knowledge Based Systems, Inc.
College Station, Texas
Scott Neil Reilly
Charles River Analytics
Cambridge, Massachusetts
Scott W. Ruston
Center for Strategic Communication
Arizona State University
Tempe, Arizona
Tracy Saint Benoit
College of Education and Human

Performance
University of Central Florida
Orlando, Florida

Barry G. Silverman
University of Pennsylvania
Philadelphia, Pennsylvania
Pramesh Singh
Northwestern Institute on Complex Systems
Northwestern University
Evanston, Illinois
Amy Sliva
Charles River Analytics
Cambridge, Massachusetts
David Q. Sun
University of Pennsylvania
Philadelphia, Pennsylvania
Col. Richard Swisher (Ret)
College of Education and Human
Performance
University of Central Florida
Orlando, Florida
Boleslaw K. Szymanski
Network Science Technology Center
Rensselaer Polytechnic Institute
Troy, New York
Kalyan Vadakkeveedu
Knowledge Based Systems, Inc.
College Station, Texas
Eric S. Vorm

Indiana University
Bloomington, Indiana
Nathan Weyer
University of Pennsylvania
Philadelphia, Pennsylvania