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Cross-Cutting Observations and Some Implications for Policymakers 395
What are al-Qaeda’s priorities and how do the priorities in public •
documents differ from clandestine priorities?
In many ways, the intelligence community likely has this infor-
mation available. us, our implications have less to do with the col-
lection of specific intelligence as its assessment and asking the right
questions to reach that assessment.
Finally, a couple cautions for the intelligence community also
emerge from our analysis. First, inaction should not automatically be
seen as a sign of terrorist weakness, as it may merely reflect a cost/
benefit calculation on the part of a terrorist organization. at is, under
certain circumstances, desisting from an attack could imaginably be
more instrumental to an organization’s aims than continuing to engage
in an active campaign of terrorist violence. And, second, governments
should be wary of personalizing terrorist groups in the guise of single
leaders or a narrow cohort of operational commanders. One negative
result of this personalization is that it could cause planners and publics
to place undue confidence in the utility of decapitation as an effective
instrument of counterterrorism.
Conclusion
In many ways, this study confirms what subject-matter experts often
have emphasized as important in counterterrorism. In many respects,
terrorists are rational actors. Undermining popular support is an effec-
tive tool to affect terrorist decisionmaking. Context has a significant
influence both within a terrorist organization and among terrorist
recruits and supporters. erefore, some of the most significant levers
discussed throughout this chapter should not be a surprise to experts,
as they are hardly revolutionary. e value comes not so much in the
repetition but rather in the way that the chapters have attempted to
pull together existing knowledge and demonstrate how the levers likely
relate to each other.
Nevertheless, this study also poses some provoking new ideas that
have yet to gain significant attention in the policymaking community.
396 Social Science for Counterterrorism: Putting the Pieces Together
Recruiters themselves might be a significant vulnerability in terror-
ist organizations. Countering root causes might not be the best way
to undermine popular support. Intelligence assessments that examine
internal decisionmaking among al-Qaeda senior leaders and opera-
tional leaders might provide insight into future priorities as well as new
vulnerabilities. Some of these statements could be hypotheses for fur-
ther studies. But, throughout this chapter, we have also have attempted
to provide ways for policymakers to contextualize what we suspect and
what social science knows about the terrorism phenomenon.
Finally, a number of questions are still left unanswered. Some
of the most important questions relate to al-Qaeda decisionmaking,
individual motivations, and even how policymakers might truly imple-
ment the concept of a “systems” approach. Although U.S. policymakers
clearly must make decisions in the absence of social science research, a
role exists for social scientists to help improve strategy, to help opera-
tionalize that strategy, and even provide a framework to think through
second- and third-order effects. is requires that U.S. policymakers
ask the questions and it also requires willingness on the part of social
scientists to work with U.S. government sponsors, using classified
information and providing concrete recommendations.
Bibliography
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Endnotes
1
Perhaps the most striking example is the development of Human Terrain Teams
and their use in Iraq and Afghanistan. e Minerva project, which provides fund-
ing to universities for academic research on violent phenomena, similarly, is indica-
tive of this trend.
2
For example, Todd Helmus’s paper on individual-level radicalization refers to
social-science research that has demonstrated that terrorists are not psychopaths,
as believed by most terrorist experts. In contrast, other social-science research, dis-
cussed in Claude Berrebi’s paper, suggests that poverty might not have quite the
effect always believed by terrorism experts.
3
Notably, in 1962, the U.S. Army Special Operations Research Office commis-
sioned a symposium entitled, “e US Army’s Limited-War Mission and Social Sci-
ence Research.” So the potential role that social science can play in counterterrorism
and insurgency has always existed, even if it was forgotten for a time.
4
Wiktorowitz (2003) and Kilcullen (2004).
5
For more research by Eli Berman, see Berman and Laitin (2008).
6
For more information on these programs, see the paper on disengagement and
deradicalization by Darcy Noricks.
7
For additional information on the recruitment of al-Qaeda foreign fighters, see
Felter and Fishman (2007).
8
is suggestion amounts to saying that the portfolio of efforts should be more
balanced rather than being tilted almost exclusively to the top-priority matters.
Such imbalance is a notorious problem in government and other organizations.
9
For more information on this debate, see Hoffman (2008) and Sciolino and
Schmidt (2008).
10
Kilcullen (2004).
11
Admittedly, al-Qaeda networks have subcomponents that are geographically
linked, but the point is to address these higher-level components globally because of
Cross-Cutting Observations and Some Implications for Policymakers 399
the reality of networking and the feasibility of relatively easy substitution effects if
only some nodes are attacked.
12
See, for example, Strong (1992), McClintock (1998), and Arnson (1999).
13
See, for example, Rashid (2002), Gerges (2007), Hafez (2003), Abuza (2003),
and Wiktorowitz (2003).
14
Abu-Rab (1996) and Kurzman (1998).
15
e matter is, however, complex. For example, Berrebi notes for the Palestinian
context that attacks by religiously motivated groups are more effective in terms of
lethality and damage.
16
Bergen and Cruickshank (2008).
17
Felter et al. (2006).
401
CHAPTER ELEVEN
Representing Social-Science Knowledge
Analytically
Paul K. Davis
Introduction
is paper asks how we can represent social-science knowledge about
terrorism analytically, and then communicate the results effectively
across interdisciplinary boundaries.
1
Doing so is nontrivial because
of the diversity of backgrounds and styles among those who need to
communicate.
I present ideas for taking the first steps to do so, with an empha-
sis on causal system models. e intent here is to express approximate
knowledge in understandable terms independent of any particular pro-
gramming language, mathematical formalism, or disciplinary back-
ground.
2
After introducing the ideas and pointing to relevant multidis-
ciplinary literature, I illustrate the methods’ integrative value by drawing
on companion papers that employed some of the suggested methods. I
end by suggesting next steps in using the methods to sharpen the ana-
lytical base of social science for counterterrorism.
3
Contrasting Approaches to Analytic Knowledge Representation
To better understand the tack taken in the paper, it is useful first to
contrast two very different approaches to scientific inquiry generally;
both are “analytic” in using structured reasoning, models, equations,
and charts. ese approaches are the “data-driven” (sometimes called
“atheoretical” or “empirical”) and the “theory-driven” approaches,
as summarized in Table 11.1. e titles are misleading in that both
depend on empirical information, but they use it differently.
4
402 Social Science for Counterterrorism: Putting the Pieces Together
Table 11.1
Contrasting Approaches
Data-Driven Theory-Driven
Specialization on one or a few factors System approach
Focus on empirical data and theory
based on readily measurable factors
Focus on factors underlying the phenomena,
whether or not easily measured
Statistical modeling Causal modeling
Discussion about correlations Causal explanations
Data-driven inquiry (“let the data
speak”)
Theory-driven inquiry, with data used to test
and calibrate theories
The Data-Driven Approach. Much of the quantitative counter-
terrorism social-science literature is of the data-driven variety (left
column). Practitioners commonly specialize, focusing on establishing
empirically the significance of one or a few factors (that is, variables).
Different papers may address, democratization, religion, and nation-
alism, for example. As discussed by Barbara Geddes (Geddes, 2003),
a result is that people view issues through different paradigm-related
lenses, without seeing the whole.
Data-driven practitioners analyze available data statistically. e
data may not be ideal. ey may be highly aggregated, for example,
as with counting the number of incidents of all kinds of terrorism
by country in a year. A given type of data may be a mere proxy for
the factor of ultimate interest. As another example, survey questions
may suggest the prevalence of religious extremism, but—for obvious
reasons—the questions will seldom be as concrete as “Are you a member
of a religiously motivated terrorist group?” e data sample may also be
biased, as with interviews of captured terrorists, who may be relatively
incompetent. And, of course, the data may be invalid: People may lie
when responding to surveys; and some governments lie about many
things, including the incidence rate of terrorism.
Such challenges are described in texts and good social scientists
are skilled at ferreting out information and adjusting cleverly for data
imperfections. e data are then analyzed statistically to evaluate
hypotheses about the factors of interest in a given study. An illustrative
Representing Social-Science Knowledge Analytically 403
result might describe the degree to which the incidence rate of terror-
ism correlates with those factors.
Philosophically, the data-driven approach is one of “letting the
data speak.” Practitioners are often skeptical about “theory,” even using
the term derisively to mean “mere speculation.” is attitude is under-
standable, since so many widely held CT beliefs have proven empiri-
cally to be wrong (see Sageman, 2008, Chapter 3; Berrebi, 2009), such
as the belief that jihadis must be poor, uneducated, unfamiliar with
democracy, or mentally imbalanced.
Much data-driven work says little about causality* and the results
of a given study may not apply outside the realm for which data are
available, such as to a different country, subgroup, or historical con-
text. Data-driven researchers may refer to “explanation” when describ-
ing results, but the “explanation” often is to be understood in a purely
statistical sense, as in “70 percent of the variance in data is predicted
by this regression equation.” at would not constitute “explanation”
to a layman.
The Theory-Driven Approach. e theory-driven approach is
common in mature areas of physical sciences and engineering (right
side of Table 11.1).
5
Here the term “theory” means a set of principles
that comprehensively explains much or all of the phenomena at issue.
If several factors matter, then theory will include all of them as a matter
of course. Whether the term is used explicitly or not, a “system per-
spective” is often taken so as to address the system or phenomenon
holistically.
It is also usual in the physical sciences to focus on the “real”
variables, even if proxy variables are sometimes used. For example, a
physician may measure a patient’s temperature (a proxy) but will then
reason in terms of underlying causes, such as infection and a weakened
immune system.
*
An exception is that economists have methods for inferring causality in data-rich circum-
stances. Some involve exploiting “natural experiments” (Angrist and Kreuger, 1999). Others
are more complex (Angrist and Pischke, 2009). Note, however, that economists use the term
“causal variable” somewhat differently than do researchers in other disciplines.
404 Social Science for Counterterrorism: Putting the Pieces Together
In the theory-driven approach, causal explanation guides reason-
ing about the likely effects of interventions, as in “We understand this
system. Its behavior is determined under all circumstances of interest
by three variables A, B, and C. We can reduce the system’s output D
by making changes in A, B, and/or C in any of several combinations.
e question is how to do so conveniently or economically.” is is
different from saying “Well, based on past data, it seems that if all else
is unchanged (that is, ceteris paribus), then increasing A will decrease
D.”
Whenever possible, the theory-driven approach is based on empir-
ical data, but data are used to test a theory, to calibrate its parameters
if the theory seems to work, and to suggest enhancements if the theory
is falsified. Where the theory comes from is one of the mysteries of
creative science. It comes from some combination of observation, rea-
soning, intuition, leaps of imagination, and guesswork. As one philoso-
pher of science noted with only some exaggeration, “Anything goes”
(Feyerabend, 1975, pp. 27–28). However, such theory is not just ad
hoc speculation. It reflects an effort to think about all the factors that
matter, although often simplifying with idealizations. eory becomes
increasingly complex realism and a wider range of circumstances are
considered.
A great deal of Department of Defense (DoD) analysis is theory-
driven. e North Atlantic Treaty Organization (NATO) and the
Warsaw Pact never went to war, but they amassed and operated sophis-
ticated militaries based on past experience, war gaming, modeling,
and analysis. Interestingly, given that so much is made currently about
“nonkinetic” considerations, much of military science has always been
“soft” and people-related (for example, the quality of an army and its
generals).
The Need for a Mix of Approaches
Many researchers viscerally prefer one or the other of the approaches
described above, but both are needed because of their different virtues
(Table 11.2). e data-driven approach (left column) provides hard
information and cautions. It can disconfirm false theories or claims.
It often demonstrates that effects are dominated by only one or a few