Terrorist Learning: A Look at the Adoption of Political Kidnappings in Six Countries, 1968–1990
MetadataShow full item record
This article studies the epidemic of kidnappings across six countries between 1968 and 1990. The goal is to identify those factors that determine the operational decisions made by terrorists. Why and how do terrorists decide to engage in certain types of actions but not others? The article discusses a number of scholarly approaches, and the variables these studies have put forward to explain the decision- making processes within terrorist organizations. The argument made here is that the groups’ ideological preferences, strategic analysis, and need to attract media attention did not appear to exert much influence in the terrorists’ decision to kidnap. Organizational resources and the nature of the security environment in which the terrorists operated had some bearing. However, kidnappings became attractive when terrorists made a pragmatic evaluation of the reaction by governments and the public and consequently of the costs or benefits of a particular course of action. The decision to carry out a campaign of kidnappings, or to abstain from kidnapping, should be interpreted as clear evidence of terrorist learning. Two types of learning appear to have influenced the adoption of kidnappings: learning by observing others and learning by doing.
The article of record may be found at http://dx.doi.org/10.1080/1057610X.2016.1237226
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
Showing items related by title, author, creator and subject.
Eisenberg, Daniel; Seager, Thomas; Alderson, David L. (Wiley, 2019);The concept of “resilience analytics” has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported ...
Vu, Minh N.; Nguyen, Truc D.; Phan, NhatHai; Gera, Ralucca; Thai, My T. (Monterey, California. Naval Postgraduate School, 2019);Due to high complexity of many modern machine learning models such as deep convolutional networks, understanding the cause of model’s prediction is critical. Many explainers have been designed to give us more insights on ...
Wilson, Zachary S. (Monterey, California: Naval Postgraduate School, 2017-12);The purpose of this study is to evaluate whether there are predictive factors that increase or decrease the probability of a particular class of energy related projects being adopted within the Department of Defense (DOD). ...