A Computational Model of Public Support for Insurgency and Terrorism
A Prototype for More-General Social-Science Modeling
Our rough guess is there are 27,500 words in this book.
At a pace averaging 250 words per minute, this book will take 1 hours and 50 minutes to read. With a half hour per day, this will take 4 days to read.
How long will it take you?
This book will take an estimated to read at a reading speed averaging words per minute. With 30 minutes per day, this will take to read.
Enter your reading speedYou can take one of our WPM reading speed tests to find your reading speed.
Create a free account to track your reading progress, build your reading list, and set reading goals.
Author
Publication
2013-06-07 - RAND Corporation
Word Count
27,500 words, Guess
Page Count
110 pages
Physical Format
Paperback
Identifiers
- Open LibraryOL30719011M
- ISBN-139780833079190
- ISBN-100833079190
- OCLC Control Number836557834
- Library of Congress Control Number2013013214
Classifications
- LCCHV6431 .D297 2013
Description
This report builds on earlier RAND research (e.g., Understanding and Influencing Public Support for Insurgency and Terrorism, 2012) that reviewed and integrated social science relevant to terrorism and insurgency. That research used qualitative conceptual causal models called ⁰́factor trees⁰́₊ to identify the factors that contribute to various aspects of terrorism or insurgency at a slice in time and how the factors relate to each other qualitatively. This report goes beyond the conceptual and qualitative by specifying a prototype uncertainty-sensitive computational model for one of the factor trees from the earlier research, one that describes public support for terrorism and insurgency. The authors first detail their approach to designing such a model, emphasizing the challenges they encountered in assigning mathematical meaning to the factor tree⁰́₉s numerous factors and subfactors, identifying suitable ⁰́₋building block⁰́₊ combining algorithms, and the uncertainty in their values and the relationships among them. They then describe how they implemented the model in a high-level visual-programming environment, show how the model can be used for exploratory analysis under uncertainty, and discuss their initial experience with it. Methodologically, the work illustrates a new approach to causal, uncertainty-and-context-sensitive, social-science modeling. It also illustrates how such models can be reviewable, reusable, and potentially composable.
Subjects
Reader Reviews
No reviews yet for this book.
Be the first to share your thoughts!