Article

On the Frequency of Severe Terrorist Events

Department of Government, University of Essex, Colchester, England, United Kingdom
Journal of Conflict Resolution (Impact Factor: 2.24). 07/2006; 51(1). DOI: 10.1177/0022002706296157
Source: arXiv

ABSTRACT

In the spirit of Richardson's original (1948) study of the statistics of deadly conflicts, we study the frequency and severity of terrorist attacks worldwide since 1968. We show that these events are uniformly characterized by the phenomenon of scale invariance, i.e., the frequency scales as an inverse power of the severity, P(x) ~ x^-alpha. We find that this property is a robust feature of terrorism, persisting when we control for economic development of the target country, the type of weapon used, and even for short time-scales. Further, we show that the center of the distribution oscillates slightly with a period of roughly tau ~ 13 years, that there exist significant temporal correlations in the frequency of severe events, and that current models of event incidence cannot account for these variations or the scale invariance property of global terrorism. Finally, we describe a simple toy model for the generation of these statistics, and briefly discuss its implications.

Full-text preview

Available from: arxiv.org
    • "For example, Bogen and Jones [1] applied a PL distribution to approximate empirical data of victim/event rates and used the PL function to predict mortality due to terrorism through the year 2080. Clauset et al [3] studied the frequency and the number of casualties (deaths and injuries) of terrorist attacks. They observed scale-invariance behavior, where the frequency of the events was an inverse power of the number of casualties. "

    No preview · Article · Nov 2015
  • Source
    • "Security-relevant activities are generally heavy-tailed, as they are conducted by a small number of people compared to the overall population. The corresponding heavy-tailed distributions require special methods in order to apply algorithmic reasoning (Clauset et al., 2007). General statistical assumptions about what can be reasonably expected as the next event do not work, because these are 'distributions without expectations' (Janert, 2010: 201). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The Snowden revelations and the emergence of ‘Big Data’ have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into the public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals’ discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage.
    Preview · Article · Oct 2015
  • Source
    • "Furthermore, it was implemented on poweRlaw package (Gillespie, 2014; Gillespie, 2015). The í µí±¥ � í µí±ší µí±–í µí±› value estimated is chosen in way that the estimated power law model gets a best fit of the empirical probability distribution for í µí±¥ ≥ í µí±¥ í µí±ší µí±–í µí±› (Clauset et al., 2007). Under the supposition that our data follows a power law for í µí±¥ ≥ í µí±¥ í µí±ší µí±–í µí±› ,, the α parameter is estimated by a numeric optimization of the log – likelihood. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The aim of this paper is to analyze a collection of data gathered from surveys held every three weeks in a Spring Course of the Economic Faculty in the University " Ismail Qemali " of Vlora, Albania. The data set for each student also contains the names of other students through which he/she have a " social relationship ". This social relationship includes frequent communications, discussions on exercise solutions, and sitting usually close to each other in the class. We have constructed four social simple graphs and have analyzed them focusing only on degrees. In addition, we fit discrete power law degree distribution on the tail and their evolution through time. In analyzing the data, we employed the R platform.
    Full-text · Article · Aug 2015
Show more