Global Patterns in Terrorism -...
Transcript of Global Patterns in Terrorism -...
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illion people,
Global Patternsin Terrorism
Lecture 1029 September 2011CSCI 7000-001Inference, Models and Simulation for Complex Systems
Prof. Aaron ClausetUniversity of Colorado
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ter•ror•ism (noun)
a violent act
by non-governmental actors
to create fear
for political purposes
“terrorism from below” vs. “terrorism from above”
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a brief history of modern terrorism
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19101880 1940 1970 2000
examples:
1881 Narodnaya Volya assassinates Tsar Alexander II
1901 Leon Czolgosz assassinates US President McKinley
1905 Terrorist Brigade operates in Switzerland and Finland
1914 Archduke Ferdinand’s assassination starts World War I
wave 1: anarchist c.1880 - c.1920
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19101880 1940 1970 2000
examples:
1928-66 Muslim Brotherhood’s Secret Apparatus in British Egypt
1931-48 Irgun fights to create Israel out of British Palestine
1955-59 EOKA fights for Cyprus’ independence from Britain
1954-62 FLN fights for Algerian independence from France
wave 1: anarchist
wave 2: anti-colonial
c.1880 - c.1920
c.1910 - c.1960
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19101880 1940 1970 2000
examples:
1964+ PLO in Palestinian territories
1970-90 “Contras” in Nicaragua
1970-93 Red Army Faction in West Germany
1980-92 Shining Path in Peru
tacit encouragement from USSR and USA
wave 1: anarchist
wave 2: anti-colonial
wave 3: revolutionary
c.1880 - c.1920
c.1910 - c.1960
c.1960 - c.2000
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19101880 1940 1970 2000
wave 1: anarchist
wave 2: anti-colonial
wave 3: revolutionary
wave 4: religiousexamples:
1976-09 Tamil Tigers in Sri Lanka (Hindu)
1984-00 Aum Shinrikyo in Japan (Cult)
1987+ Hamas in Palestinian territories (Islamic)
1991+ Lord’s Resistance Army in Uganda (Christian)
1994+ Taliban in Afghanistan, Pakistan (Islamic)
c.1880 - c.1920
c.1910 - c.1960
c.1960 - c.2000
c.1980 - present
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19101880 1940 1970 2000
tactics targetswave 1 assassinations, dynamite, suicide missions government,
heads of business
wave 2 assassinations, firearms, guerrilla attacks government, military
wave 3 hostages, firearms, high explosives, guerrilla attacks, assassinations
mainly military, some civilians
wave 4 suicide bombs, improvised explosives, firearms, unconventional methods
mainly civilians, some military
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conventional studies of terrorism
• historical, descriptive, policy-oriented
• focused on incidence and strategy (spoiler effects, substitution, public support)
• correlations (democracies, political motivation, strategic opportunities, alliances, material support)
• mainly use aggregate measures (totals, binary variables)
• general linear model (regressions)
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thus
• theories of rational behavior, context and strategic issues (a la economics)
• severity of events typically ignored
• theories rarely mechanistic (but often psychological)
• few general “laws”
• little hope of forecasting (context, context, context)
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where is terrorism today?
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RAND-MIPT data
•40 years (1968-2008)
•domestic + international
•5000+ cities, 187 countries
•36,018 events (37% deadly)
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Deaths per million people,1998-2007
<0.1 0.1 - 11 - 10 10 - 100100 - 400
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deaths per million people, USA 2007
terrorism 0lightning 0.15bee sting 0.18airplane crash 0.23homicide 61.74car crash 124.43
sources: , US Census, MIPT, NWS, CDC, NTSB and NHTSA
*
* most recent available data, from 2006
*
--1
x1.2x1.5
x408.3x829.5
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is terrorism getting worse?
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196819721976198019841988199219962000200420081
10
100
1000
10000
Year
Dea
ths
per y
ear
Terrorism worldwideSmoothed dataExponential trend
is terrorism getting worse?
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1968197219761980198419881992199620002004200810 9
10 8
10 7
10 6
Year
Terro
rism
dea
ths
per w
orld
cap
ita
Terrorism worldwideSmoothed dataExponential trend
is terrorism getting worse?
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19681972197619801984198819921996200020042008
2
4
6
8
10
12
14
16
18
Year
Aver
age
deat
hs p
er le
thal
atta
ck
Terrorism worldwideSmoothed dataLinear trend
is terrorism getting worse?
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196819721976198019841988199219962000200420080
0.5
1
1.5
2
2.5
3 x 10 9
Year
Aver
age
deat
hs p
er c
apita
Terrorism worldwideSmoothed dataLinear trend
is terrorism getting worse?
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• individual events no worse, on average
• number of fatal events increased exponentially
• odd lulls in 1992, 2006
comments
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• relatively few casualties (automobiles > terrorism)
• very infrequent
• disproportionate economic, political effects
For example: major re-organization of US/UK national security apparatus after 9.11.2001
severe events (“outlier” events)
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21 Dec. 1988: Pan Am Flight 103, Lockerbie Scotland (270 deaths)
19 Apr. 1995: Oklahoma City bombing (168 deaths)
11 Sept. 2001: World Trade Center (2749 deaths)
12 Oct. 2002: Bali nightclub bombing (202 deaths)
26 Nov. 2008: Lashkar-e-Taiba attack in South Mumbai (173 deaths)
image credits: wikipedia, BBC news
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1 9 10 99 100 999 1000+0
2000
4000
6000
8000
10000
12000
1400012280
95736 1
deaths per attack
num
ber o
f inc
iden
ts13,274 deadly attacks, 1968-2008
“normal” 92%
“severe” 8%{{
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1 10 100 1000 10000
0.01%
0.1%
1%
10%
100%
deaths per attack
perc
ent w
ith g
reat
er s
ever
ity
9-11
13,274 deadly attacks, 1968-2008
“severe” 8%
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1 10 100 1000 10000
0.01%
0.1%
1%
10%
100%
deaths per attack
perc
ent w
ith g
reat
er s
ever
ity
9-11
13,274 deadly attacks, 1968-2008
“severe” 8%
α = −2.4
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power-law is plausible model for deaths
• no fundamental difference, big vs. small
• marks “severe event” range
• could estimate risk of future severe events (additional validation needed for this)
• can use power-law model for additional analysis
testing the power law
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how does frequency-severity distribution vary with
• time
• weapon type
• economic development
variations
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study events in each 24 month interval
variation with time
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• ave. log-severity largely stable over 40 years
• apparent periodicity in ave. log-severity at years 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004
0
1
2
3
4
avg.
log 2 (
deat
hs )
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 181
0.5
0
0.5
1
separation time, (years)
auto
corre
latio
n
mean+
! ! 13
196819721976198019841988199219962000200420080
0.5
1
1.5
2
2.5
3 x 10 9
Year
Aver
age
deat
hs p
er c
apita
Terrorism worldwideSmoothed dataLinear trend
variation with time
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1968 1972 1976 1980 1984 1988 1992 1996 2000 20041.50
1.75
2.00
2.25
2.50
2.75
3.00
scal
ing
expo
nent
year
mean
• scaling exponent largely stable over 40 years
• suggests severity distribution largely stable
• main difference today: many more events
1968-2006
variation with time
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• chem/bio
• explosives
• fire/arson
• firearms
• knives
• other/unconventional
variation by weapon
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• more apparent power-law behavior
• but different ,
• not ubiquitous: no power laws by region
• explosives most deadly, overall
10 4
10 2
100
10 4
10 2
100
P(x)
100 102 10410 4
10 2
100
severity, x (total)
100 102 104
severity, x (total)
Chem/Bio Explosives
Fire
Firearms
Knives Other
!̂ x̂min
variation by weapon
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• 30 countries (USA, Japan, France, UK, Turkey...)
• tracks economic statistics and data for these 30 + 70 others
variation by economy
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1 10 100 1000 10000
0.01%
0.1%
1%
10%
100%
deaths per attack
perc
ent w
ith g
reat
er s
ever
ity developed nations 5%
developing nations 95%
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x ! xmin of totalTurkey 335 26.9%France 201 16.2%Spain 109 8.8%
Germany 98 7.9%USA 93 7.5%
Greece 76 6.1%Italy 73 5.9%UK 62 5.0%total 1047 84.2%
other factors must be involved
severe events
economy alone?
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power-law pattern holds for
• different decades (70s, 80s, 90s, 00s)
• different types of weapon (guns, fire, bombs, etc.)
• different levels of economic development (OECD)
• but not for “regions” or suicide attacks
open questions
• what creates this simple and robust pattern?*
• what does this mean for long-term planning? (can we make accurate statistical forecasts?)
comments
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model 1:• competition between states and terrorists leads to
exponential sampling (a la Reeds & Hughes) [Clauset, Young & Gleditsch (2007)]
model 2:• population density fluctuations + density-
dependent targeting (a la Reed & Huges) [Clauset, Young & Gleditsch (2010)]
model 3:• self-organized critical, fission-fusion model of group
dynamics leads to power-law in cell sizes [Johnson et al. (2009), Clauset & Weigel (2010)]
generating the power law
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tuesday:
orterrorist organizations civil wars
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