The Life Cycle of Terrorist Organizations

Phillips, Peter J.

doi:10.1007/s11294-011-9314-3

Cited by 10 publications

(10 citation statements)

References 16 publications

(25 reference statements)

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“…A recent discovery in terrorism studies has highlighted that terror activities have some regularities, such as periodicity [27] and tendency [28]. Moreover, terrorist behavior has multiple time-scale characteristics in the nonlinear and nonstationary time series [29].…”

Section: Periodic Featuresmentioning

confidence: 99%

“…However, the intervention repeats and oscillates cyclically as the focus of the government goes up and down. As terrorists will trade off risk and return when choosing their targets [31], thus, the strategies of terrorist group shift dynamically to avoid being eliminated [27]. On the other hand, terrorist groups are deeply covert, and their scale usually oscillates gradually and periodically on large time-scales (years or decades).…”

Section: Periodic Featuresmentioning

confidence: 99%

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Terrorist Group Behavior Prediction by Wavelet Transform-Based Pattern Recognition

Sun

et al. 2018

Discrete Dynamics in Nature and Society

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Predicting terrorist attacks by group networks is an important but difficult issue in intelligence and security informatics. Effective prediction of the behavior not only facilitates the understanding of the dynamics of organizational behaviors but also supports homeland security's missions in prevention, preparedness, and response to terrorist acts. There are certain dynamic characteristics of terrorist groups, such as periodic features and correlations between the behavior and the network. In this paper, we propose a comprehensive framework that combines social network analysis, wavelet transform, and the pattern recognition approach to investigate the dynamics and eventually predict the attack behavior of terrorist group. Our ideas rely on social network analysis to model the terrorist group and extract relevant features for group behaviors. Next, based on wavelet transform, the group networks (features) are predicted and mutually checked from two aspects. Finally, based on the predicted network, the behavior of the group is recognized based on the correlation between the network and behavior. The Al-Qaeda data are investigated with the proposed framework to show the strength of our approaches. The results show that the proposed framework is highly accurate and is of practical value in predicting the behavior of terrorist groups.

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Section: Periodic Featuresmentioning

confidence: 99%

Section: Periodic Featuresmentioning

confidence: 99%

Terrorist Group Behavior Prediction by Wavelet Transform-Based Pattern Recognition

Sun

et al. 2018

Discrete Dynamics in Nature and Society

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“…They provide both qualitative and quantitative results with empirical data and are helpful in understanding the relational structure and finding key elements (individual, subgroup, etc.). However, it is consensus that terrorist organization is dynamic and far more complicated than structural network [12,13]. Dynamic network analysis (DNA) and metanetwork are used to model the complex organizational structure and individual interaction [14].…”

Section: Introductionmentioning

confidence: 99%

Agent Based Modeling on Organizational Dynamics of Terrorist Network

Sun

Zhu

et al. 2015

Discrete Dynamics in Nature and Society

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Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.

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“…While various aspects of the lifecycles of terrorist groups are well known (Gupta et al 2009;Phillips 2011;Blomberg et al 2011) and are important for formulating proper counterterrorism policies, few attempts have been made to understand the internal compositions of terrorist groups. Based on a theoretical model of human behavior, Gupta (1990Gupta ( , 2008 identifies three types of agents operating within terrorist groups, which are distinguished by their respective motivations for joining the group.…”

Section: Introductionmentioning

confidence: 99%

“…19 Successful attacks draw additional ideologues, while unsuccessful attacks drive ideologues away (Phillips 2011;Crenshaw 1991;Gaibulloev and Sandler 2013). Any terrorist organization that can launch a large-scale attack against its enemies is likely to garner substantial media attention, as well as signal ideological commitment to its political base.…”

mentioning

confidence: 99%

Al Qaeda at the Bar: Coordinating Ideologues and Mercenaries in Terrorist Organizations

et al. 2015

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Most terrorist groups have limited lifespans. A number of scholars and casual observers have noted that terrorist organizations often are comprised of two types of participants: ideologues or "true believers" dedicated to the group's cause, and mercenaries, who are adept at raising money through illegal means. The latter are interested primarily in their personal gains and have relatively little ideological commitment. Terrorist groups need both participants in order to function effectively. The purpose of the study is to understand the impact of communication on the compositions of terrorist groups. Three experimental treatments consider a coordination problem, and focus on the behavior of the mercenaries. Participants choose whether or not to participate in a terrorist attack. Payoffs are U-shaped in the number of participants, and increase with the number of successful attacks. The treatments allow communication between a leader and frontline fighters ("leader" treatment) or among the frontline fighters themselves ("communication" treatment). In the first treatment, a group leader can post messages to the members, which has a 19% coordination success rate. For the communication treatment, all participants can post messages anonymously to each other, which yields a 27% coordination success rate. By contrast, the baseline ("no communication" treatment) shows a success rate of 11%. We conclude from our experimental evidence that disrupting communications among the frontline fighters is more effective in terminating terrorist organizations.

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The Life Cycle of Terrorist Organizations

Cited by 10 publications

References 16 publications

Terrorist Group Behavior Prediction by Wavelet Transform-Based Pattern Recognition

Terrorist Group Behavior Prediction by Wavelet Transform-Based Pattern Recognition

Agent Based Modeling on Organizational Dynamics of Terrorist Network

Al Qaeda at the Bar: Coordinating Ideologues and Mercenaries in Terrorist Organizations

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