Fwd: INARM Message - The physics of terror

[Dave Ingram]

Begin forwarded message:

From: Seamus Creedon <seamus....@gmail.com>

Date: April 28, 2013 2:41:58 PM EDT

To: inarm inarm <in...@list.soa.org>

Subject: INARM Message - The physics of terror

Interesting article by Gillian Tett in FT of 27/4 with above title - I am motivated to explore sources mentioned - Clauset, Woodard.

Regards

Seamus Creedon FSAI FIA CERA MAAA

seamus....@gmail.com

+44 7808 900 614

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[Dave Ingram]

Begin forwarded message:

From: Dave Ingram <davei...@optonline.net>

Date: April 28, 2013 5:54:51 PM EDT

To: Seamus Creedon <seamus....@gmail.com>

Subject: Re: INARM Message - The physics of terror

Here is the link to the study that she references.  

http://arxiv.org/pdf/1209.0089v1.pdf

Estimating the historical and future probabilities of large terrorist events

Aaron Clauset and Ryan Woodard

Abstract

Quantities with right-skewed distributions are ubiquitous in complex social systems, including

political conflict, economics and social networks, and these systems sometimes produce extremely

large events. For instance, the 9/11 terrorist events produced nearly 3000 fatalities, nearly six

times more than the next largest event. But, was this enormous loss of life statistically unlikely

given modern terrorism’s historical record? Accurately estimating the probability of such an event

is complicated by the large fluctuations in the empirical distribution’s upper tail. We present a

generic statistical algorithm for making such estimates, which combines semi-parametric models of

tail behavior and a non-parametric bootstrap. Applied to a global database of terrorist events, we

estimate the worldwide historical probability of observing at least one 9/11-sized or larger event

since 1968 to be 11–35%. These results are robust to conditioning on global variations in economic

development, domestic versus international events, the type of weapon used and a truncated history

that stops at 1998. We then use this procedure to make a data-driven statistical forecast of at least

one similar event over the next decade.

Dave