Modeling Sociocultural Influences on Decision Making

Understanding Conflict, Enabling Stability

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

• Publisher: CRC Press
• Year: 2016
• Print ISBN:
  9781498736695
• eBook ISBN:
  9781315352565
• Topic: Social Sciences
• Subtopic: National Security
• Index: Social Sciences

In our increasingly globally interconnected world, understanding and appreciating the sociocultural context within which individuals make their decisions is critical to developing successful partnerships. The collection of chapters in this volume illustrates how advances in information and social media technologies, as well as modeling and simulation tools, combined with the social sciences, can be leveraged to better understand how sociocultural context influences decision making.

The chapters in this volume were contributed by leading experts from academia, industry, and government and provide:

• Insights into cross-cultural decision making based on recent international events, with grounding in an historical context
• Discussions of cutting-edge modeling techniques used today by professionals across multiple organizations and agencies
• Descriptions of specific cross-cultural decision making tools designed for use by laypeople and professionals
• Case studies on the role of cross-cultural decision making grounded in current events and (in many cases) military applications.

Subject Index

Page numbers followed by f, t, and n indicate figures, tables, and footnotes, respectively.

A

Ability, defined, 401

ABMs, see Agent-based models (ABMs)

Academic achievement tests, 89

Access

data, to disconnected communities, 154, 156t

data ownership and, 154

limited, data collection and, 111

Accidents

cognitive biases and, 104–106, 105f

cultural differences role in (case studies)

Korean Air Flight 801 disaster, 106–107

Union Carbide’s Bhopal chemical plant accident, 107

cultural differences role in (case study), 106–107

Adaptive cycle process, social resiliency, 487–488, 487f

Advanced Progressive Matrices (APM), 89

Adverse events

descriptive statistics by district, 251–252, 251t, 252f

GIS-based analysis and visualization, 252–254, 253t–254t

mapping, vs. population density, 254–255

AF1, 116–117, 121, 122, 128; see also High-risk ethnography (HRE); Sixty Percent Mission (case study)

Afghanistan

GCM for, 498–503, 501f, 502f

population survey (case study); see also Population surveys

factor analysis (results), 231–232

history, 230–231

indicators deriving from factors, 232–233, 232t, 240t–242t

regions of, 248, 249f

unstable sociopolitical environment in (case study), 146–147

US experience in, 496–498

US interventions in, 496, 498

war in (2004–2010), geographical modeling, 243, 246–247

researching design and methodology, 247–251

results and discussion, 251–259

Afghan Security Forces Fund, 245

Age group differences, VEOs and, 441, 443f, 444t

Agent, defined, 477

Agent-based models (ABMs)

computational modeling, 450–451

populations, 19–20

Aggregation

defined, 298

uncertainty, 298

Al-Assad, Bashar, 191, 192

Aleatory (statistical) uncert...

Table of contents

• Cover
• Half Title
• Human Factors and Ergonomics Series
• Title Page
• Copyright Page
• Dedication Page
• Contents
• Foreword
• Editors
• Contributors
• Introduction
• Fictional Megacity Use Case
• Section I Building Theories
• Section II Collecting and Analyzing Data
• Section III Building and Validating Sociocultural Models
• Section IV Applying Sociocultural Models to Gain Insight into Conflict and Instability
• Subject Index
• Author Index