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Endnotes

Most of the larger volume of which this paper is part are scholarly literature

reviews. is paper is diﬀerent in kind and style; it is more of a think piece, one

stimulated by the challenges of trying to pull together disparate segments of the

social-science literature.

is is a return to the past, when models were designed before being imple-

mented. Much of the paper is an attempt to use simple graphical and tabular tech-

niques as a ﬁrst-order, approximate, “speciﬁcation language.”

I thank Ben Wise and Claude Berrebi for comments on an earlier draft. e

paper also beneﬁted from an internal research and development project (Davis,

2006; Davis, Hillestad, Trujillo, and Neill, unpublished, 2005).

Confusion about this dates back centuries, as discussed in the philosophy-of-

economics literature (Hausmann, 2008). e 18th century work of John Stuart Mill,

for example, has sometimes been contrasted with empiricism, but Mill believed that

the principles in a domain should be empirically established. (Hausmann, 2008,

section 3.2.)

I refer to “mature” areas, because the data-driven approach is quite important in

areas of physical science where theory is less well developed..

e British refer to the “Comprehensive Approach” (Ministry of Defence, 2005);

the DoD refers to PMESII factors (i.e., political, military, economic, social, infra-

structure and information systems) and DIME instruments (i.e., diplomatic, infor-

mation, military, and economic instruments of power).

450 Social Science for Counterterrorism: Putting the Pieces Together

See, for example, Holland and Mimnaugh (1996), Alberts (2007), and Alberts

and Hayes (2003).

See, for example, Zeigler, Praenhofer, and Kim (2000) and Uhrmacher (2006).

e discipline of systems engineering is struggling with how to deal with systems

of systems and complex adaptive systems. See, for example, papers presented at the

Symposium on Complex Systems Engineering.

is point was made in the early work of system dynamics (Forrester, 1963). Mil-

itary “soft factors” are discussed in Military Operations Research Society (1989).

is section contrasts with econometrics, where practitioners insist on measur-

able variables and seek to “explain” data with as few variables as possible.

In some ﬁelds, such as in Bayesian belief nets, the term “inﬂuence diagram” has a

somewhat diﬀerent meaning from the one used here (see Pearl, 2000, for example).

See Davis and Arquilla (1991), National Research Council (1996), and Kulick

and Davis (2003).

For exploitation of binary-logic modeling, see Ragin (1989). See also his exten-

sion (Ragin, 2000).

My own suspicion is that this empirical phenomenon is an artifact of omitting

such important variables as the eﬀectiveness of the state’s internal security appara-

tus, of averaging over very heterogeneous data for diﬀerent countries or time peri-

ods, or both.

See, for example, Pourret, Naim, and Marcot (2008), Wagenhals, Shin, and Levis

(2001), and Pate-Cornell and Dillion (2006).

My colleague Richard Hillestad has illustrated how game-theoretic concepts can

be represented in CT modeling (Hillestad and Davis, 2007).

COMPOEX is an environment, and suite of tools, generated in a major eﬀort

sponsored by the Defense Advanced Research Projects Agency (DARPA) (program

manager: Sean O’Brien). Not much about it has been published so far in the schol-

arly literature, although some information is available on DARPA’s Web site.

CAS research has demonstrated that simple rules can predict emergent phenom-

ena that appear markedly similar to real-world behaviors (for example, the ﬂocking

of geese or the start of a riot). However, it does not follow that only a small set of

simple rules is suﬃcient to do justice to the actual phenomena. For example, human

beings are not solely driven by simple cost-beneﬁt calculations and, even when they

are, they may act as though they use complicated, idiosyncratic, and context-

dependent utility functions.

Representing Social-Science Knowledge Analytically 451

Statistical physics relates the molecular and thermodynamic domains for equilib-

rium and some nonequilibrium systems. My expectation is that exploratory analysis

using CAS models will lead to macroscopic “laws” in social-science behavior. CAS

research relevant to CT is ongoing under sponsorship of U.S. Joint Forces Com-

mand (Chaturvedi et al., 2000) and other DoD organizations. A great deal of pub-

lished research on related matters can be found on the Web site of Penn Professor

Barry Silverman. Recent work has also been done as part of DARPA’s COMPOEX

activity.

Colleague Ben Wise mentions a distinction between the ability to “learn” (and

therefore behave diﬀerently) without changing structures and the ability to adapt

even though it means changing structures (for example, changing from a central-

ized to decentralized operation or working with a faction that had previously been

an adversary).

Various terms have been used for related methods. ese include “adaptive plan-

ning,” a shorthand for planning for ﬂexible, adaptive, and robust capabilities (Davis,

2002a; National Research Council, 2006); “robust adaptive planning” (Lempert,

Groves, Popper, and Bankes, 2006), and “planning for agility” (Alberts, 2007).

See also a recent book taking a life-cycle approach to describing terrorism (Gupta,

2008).

e nodes in Figure 11.10 have subcomponents. For example, “Support for ter-

rorism” might apply to each of a number of groups. Aggregating upward is nontriv-

ial. For example, a given terrorist organization might need only a modest amount of

support to succeed. us, “Support for terrorism” might have a high value (indicat-

ing adequacy) if even a relatively small subpopulation provided it.

Four such environments are DARPA’s COMPOEX (see the DARPA Web site),

SEAS (associated with Alok Catuverdi of Simulex), an environment developed by

Barry Silverman, and REPAST, an extensively exercised environment developed at

Argonne National Laboratory and the University of Chicago (North et al., 2007).

Related suggestions have been made by Dell Lunceford, previously of DARPA

and the Army’s Modeling and Simulation Oﬃce, and by members of a working

group contributing to a white paper for DoD (Davis and Henninger, 2006; Allen et

al., 2007).

is refers to experience with the RAND-ABEL language (Hall, Lacasse, Ander-

son, and Shapiro, 1985; Shapiro, Hall, Edward, and Anderson, 1988; Davis, 1990).

453

CHAPTER TWELVE

Conclusions

Paul K. Davis and Kim Cragin

is review of the base in social science relating to terrorism and

coun-

terterrorism began with a set of chapters taking diﬀerent perspectives:

root causes of terrorism (Noricks, Chapter Two), individual radical-

ization (Helmus, Chapter ree), the sustainment of public support

for terrorism (Paul, Chapter Four), the economics of terrorism (Ber-

rebi, Chapter Five), how terrorist organizations make decisions (Jack-

son, Chapter Six), how terrorism ends (Gvineria, Chapter Seven), what

we know about deradcialization and disengagement (Noricks, Chapter

Eight), insights about strategic communications (Egner, Chapter Nine),

cross-cutting observations (Cragin, Chapter Ten), and an analytical

representation of social-science knowledge about terrorism and coun-

terterrorism (Davis, Chapter Eleven). We have also included an appen-

dix taking a ﬁrst cut at identifying measures of eﬀectiveness applicable

to the various topics studied throughout the volume (Bahney, Appen-

dix B).

Because our volume includes an extensive executive summary, we

have chosen to keep the conclusions section short. However, our over-

arching conclusions are the following:

Social science does well in identifying the key factors aﬀecting •

terrorism and counterterrorism. However, special analytical tech-

niques are needed to bring some order out of the resulting chaos.

We have developed and illustrated a ﬁrst set of such techniques,

with an emphasis on being able to communicate and debate ideas

and assumptions across disciplinary boundaries.

454 Social Science for Counterterrorism: Putting the Pieces Together

A key element in doing so is taking a causal system perspective— •

at least for the purposes of establishing a framework of com-

munication.

Consistent with this system perspective, it is essential to distin-•

guish sharply among diﬀerent contexts: Failure to do so has prob-

ably been the biggest single problem impeding coherent scientiﬁc

discussion of terrorism and counterterrorism. Terrorist organiza-

tions diﬀer enormously, even “aﬃliates” of al-Qaeda diﬀer, and

many key issues are local.

Because multiple factors aﬀect terrorism and counterterrorism, •

and because their relative importance depends so much on con-

text, social scientists must often answer simple questions with “It

depends.”

Fortunately, it is possible to go well beyond “It depends” and to •

identify diﬀerent “types” of situation, that is, diﬀerent “types” of

context. is volume has identiﬁed the way ahead for doing so in

future work.

Even where social-science knowledge is quite good, however, the •

aspiration should be one of anticipating possibilities and improv-

ing the odds of correct predictions, as distinct from seeking reli-

able prediction. Reliable prediction is not only infeasible in most

cases, it is a counterproductive goal to the extent that it discour-

ages an emphasis on monitoring, feedback, and adaptation.

e style of analysis should be determined accordingly. In partic-•

ular, it should inform ﬁnding strategies that are ﬂexible, adaptive,

and robust rather than ﬁnely tuned to dubious assumptions and

fragile to random developments.