RAND Corporation. Social Science for Counterterrorism
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APPENDIX A
About the Authors
Paul K. Davis is a principal researcher at RAND and a professor of
policy analysis in the Pardee RAND Graduate School. He has pub-
lished widely on strategic planning (especially defense planning), strat-
egy, advanced methods of modeling and analysis, deterrence theory,
decisionmaking theory, cognitive modeling of adversaries, and analyti-
cal support of high-level decisionmakers. He was the lead author of
the 2002 monograph Deterrence and Influence in Counterterrorism: A
Component in the War on Al Qaeda. Before joining RAND, Paul was
a senior executive in the Office of the Secretary of Defense (Program
Analysis and Evaluation). He has a B.S. from the University of Michi-
gan and a Ph.D. in chemical physics from the Massachusetts Insti-
tute of Technology. He has served on numerous panels of the National
Research Council and Defense Science Board and is a past member of
the Naval Studies Board.
Kim Cragin is a senior policy analyst at RAND. As a cultural histo-
rian, her research focuses on terrorism-related issues, including arms
trafficking by the FARC (a left-wing rebel group in Colombia), sui-
cide bombings, anti-U.S. extremism, the relationship between terror-
ism and development, terrorist groups’ operational requirements, and
border security. She was the lead author of a recent study, Curbing Mili-
tant Recruitment in Southeast Asia. Before coming to RAND in 2000,
Kim received a master’s degree in public policy from Duke University,
where she studied Hamas and Israeli right-wing extremism and wrote
her thesis on U.S. counternarcotics policy with regards to Plan Colom-
456 Social Science for Counterterrorism: Putting the Pieces Together
bia and the FARC. She has also studied at Hebrew University, the
Moscow Economic Institute, and Xinjiang University. Kim received
her Ph.D. from the Faculty of History, Clare College, Cambridge Uni-
versity, writing on the transformation of Hamas over three decades.
Darcy M.E. Noricks is an associate political scientist at RAND,
where she works on terrorism and counterterrorism, homeland secu-
rity, defense strategy and planning, and international politics. Before
joining RAND, she was project manager for the Global Transnational
Terrorism Project and DFI Government Practice. She directed research
and collection efforts on jihadi cells in Indonesia and Australia, as well
as on al-Qaeda, and led a number of homeland security projects begin-
ning in 1999. She received a B.A. in political science from the Univer-
sity of California, Berkeley, and an M.S. in foreign policy from George-
town University; she is completing her Ph.D. in political science at the
University of Washington.
Claude Berrebi is an associate economist at RAND, with expertise in
terrorism and counterterrorism, labor and household behavior, work-
force and labor markets, and international social and economic issues.
He authored the recent papers “Human Capital and the Productivity
of Suicide Bombers” (Journal of Economic Perspectives) and “Are Voters
Sensitive to Terrorism?” (American Political Science Review), among
others. Claude received his B.A. in economics and M.B.A. in finance
from Hebrew University, and his M.A. and Ph.D. in economics from
Princeton University (his dissertation was entitled “Terrorism: Causes
and Consequences”).
Todd C. Helmus is a behavioral and social scientist at RAND and
was the lead author of the 2007 RAND monograph Enlisting Madison
Avenue: e Marketing Approach to Earning Popular Support in e-
aters of Operation. He has also written monographs on combat stress
reduction, the challenges of operations in an urban environment, and
curbing militant recruitment. Todd received a B.A. in psychology from
Hope College and a Ph.D. in clinical psychology from Wayne State
University.
About the Authors 457
Christopher Paul is a behavioral and social scientist at RAND. He is
the author of Information Operations—Doctrine and Practice: A Refer-
ence Handbook and has written on the history of U.S. military inter-
ventions, press-military relations, urban warfare, simulation training,
and acquisition. Chris received his Ph.D. in sociology from the Univer-
sity of California, Los Angeles.
Brian A. Jackson is a senior physical scientist at RAND, where he has
written widely on organizational learning in terrorist groups, including
terrorists’ use of technology. His research focuses on homeland security
and terrorism preparedness. He is also associate director of RAND’s
homeland security research program. Brian received a Ph.D. in bio-
inorganic chemistry from the California Institute of Technology and
an M.A. in science, technology, and public policy from George Wash-
ington University.
Gaga Gvineria is currently a doctoral candidate in the Pardee RAND
Graduate School. Before coming to RAND, he was a consultant in
national security affairs at the Center for Strategic Research in Georgia
(former USSR). He has also been a consultant with the World Bank
and with the Post-Privatization Restructuring Project. He has received
degrees from Tblisi State University, the Georgian Institute of Public
Administration, and the Tbilisi State Institute of Economic Relations.
Michael Egner is currently a doctoral candidate in the Pardee RAND
Graduate School, where he has conducted research on counterterror-
ism, modern decision science, and strategic communications. Before
coming to RAND, Michael performed research for the Urban Insti-
tute, the White House Council of Economic Advisers, and Goldman
Sachs. He received his B.A. in economics at Princeton, where he won
the President’s Award for Academic Achievement
.
Ben Bahney is a project associate at RAND. He holds a B.A. in his-
tory from the University of Pennsylvania and an M.A. in international
458 Social Science for Counterterrorism: Putting the Pieces Together
economics from the University of California, San Diego. His primary
interests are counterinsurgency, international politics, and homeland
security.
459
APPENDIX B
Analytic Measures for Counterterrorism and
Counterinsurgency
Benjamin Bahney
Introduction
Objectives
is appendix provides initial suggestions about ways to measure the
factors discussed in the main body of the monograph. Well-conceived
metrics should be an important part of a counterterror or counterin-
surgency (CT/COIN) campaign. e appendix suggests a number of
possible metrics but provides little detail, because the intention is to
provide only a first-cut look. Just a few of the metrics discussed here
will be relevant to any particular campaign plan, and their specifics
will likely depend on both global conditions and conditions in the area
of operations (AOR). e following pages deal primarily with mea-
sures for factors that are amenable to influence.
The Measures Literature
e relevant literature on analytical measures for CT/COIN is surpris-
ingly modest with some exceptions, such as a School of Advanced Mili-
tary Studies monograph by then Major Douglas Jones (Jones, 2006),
which in turn drew on work by Bard O’Neill (O’Neill, 1990). In this
discussion, I adopt the terminology and framework of a recent disser-
tation on effects-based operations (EBO) (Bullock, 2006). is effort
stands out for discussing measures of effectiveness and providing both
a mathematical perspective and a typology relevant to military appli-
cations. Much of the other existing work on measures has been from
a security-studies perspective, which tends to highlight the pitfalls of
460 Social Science for Counterterrorism: Putting the Pieces Together
using either well-defined or poorly chosen measures (Murray, 2001)
and determining what characteristics can be attributed to an adequate
measure (Jones, 2006; Murray, 2001; Eisenstadt, 2005).
I will also incorporate some lessons learned in the measurement
of CT/COIN that have not been incorporated in the exiting literature.
e Hamlet Evaluation System (HES) from the Vietnam War provides
one interesting (if antiquated) test case in measuring COIN; it was
designed by the Central Intelligence Agency and conducted by the U.S.
Military Assistance Command Vietnam to measure rural development
and pacification (Hunt, 1995; Long, 2006; ayer, 1985; Corson, 1968;
and Sweetland, 1968). is system of measures was designed to assess
12,000 individual South Vietnamese hamlets by grading them on a
six-point scale across 18 indicators relating to security or development,
then complementing groups of these indicators with confidence scores.
e HES was a pioneering effort in developing analytical measures to
apply to CT/COIN, but its misuse and contentiousness in both mili-
tary and civilian circles caused it to be in low repute by the end of the
campaign. However, a recently declassified study on the HES showed
that it was a statistically valid, modest system for measuring rural paci-
fication (Sweetland, 1968), which may be rich in lessons-learned that
could potentially be applied to measuring the global war on terror.
Distinctions and Definitions
It is useful in what follows to distinguish among what will be referred
to in acronyms as MOPs, MOEs, and MOOs (Figure B.1). A measure
of process (MOP) relates how inputs of a system are transformed into
outputs, corresponding with tactical levels of operation.
1
A measure of
effectiveness (MOE) measures system changes resulting directly from
creating certain outputs, which correspond with operational tasks that
are determined to be necessary to realize strategic effects.
2
A measure
of outcome (MOO) characterizes higher-order conditions created by
numerous system effects and thus captures only strategic-level outcomes.
As I use the term, MOEs relate cause and effect and describe
how well actions are achieving their objectives. MOEs fit into a verti-
cal framework of linking measures to fundamental system objectives,
where the objective is an overall desired end-state (Keeney, 1992). In
Analytic Measures for Counterterrorism and Counterinsurgency 461
Figure B.1
A Taxonomy of MOEs
SOURCE: Adapted from Bullock (2006).
RAND MG849-B.1
Outcome or
condition
Effect
Output
Inputs
Output
Inputs
Effect
Output
Inputs
Output
Inputs
MOO
MOE
MOP
this sense, MOEs can be distinct from MOPs, which measure such
attributes as efficiency or technical qualities of system components con-
tributing to intermediate objectives (Artley, 2001; Army, 2006; and
Keeney, 1992).
3
MOOs gauge the indirect conditions created by system
effects (DSMC, 1994), and thus may be strongly affected by factors
that are not subject to influence. us, an intervention or action may
have high performance in the sense of using good technology and effi-
cient processes, various outcomes may be achieved, and yet the action
may be ineffective overall.
e quality of different proposed measures may depend on their
accessibility or on the ease of creating and operationalizing the measure
from the concept being measured. Any single measure also fits into an
accessibility type; certain measures are easily quantified and linked to
a system end-state; others may need to be constructed and may be cor-
related only with the end-state. In this context, measures can be cat-
egorized by the nature of their construction and the degree to which
they measure the objective end-state. Measures can be natural (that is,
commonly understood) or constructed (that is, developed for a specific
462 Social Science for Counterterrorism: Putting the Pieces Together
purpose), and direct (that is, linked to an objective) or proxy (that is,
correlated with an objective). is typology allows us to classify mea-
sures by how accessible they are in monitoring the system’s end-state,
with natural-direct measures being most accessible and constructed-
proxy measures being the least (Kirkwood, 1997).
Particular attributes of a measure must also be considered to
gauge its usefulness as a tool to measure the system in question. A
measure must reflect the system attributes it is supposed to represent;
if it does, the measure is considered valid. A measure must also be pre-
cise (that is, sharply defined) to be meaningful and reliable. Finally, an
analytical measure should be sensitive to changes in policy or action;
if it is, it is considered to be responsive.
4
With a developed theory laid
out for understanding the nature and use of measures, I can now turn
to applying specific measures to the most important factors delineated
in each of the preceding author’s chapters. Of course, I am primarily
concerned with understanding MOEs that are sensitive to policy and
action over both the short and the long term.
Although this appendix approaches the search for metrics rather
directly, other RAND work has emphasized that metrics are best devel-
oped as spinoffs of operational analysis to understand and build models
of relevant processes, such as combat operations—including the con-
sequences of adaptations by the adversary. Metrics developed in that
way have a better-defined relationship to what is actually of interest
and are more likely to be defined carefully than are metrics arrived at
through intuitive leaps. eir shortcomings are also more likely to be
understood, which is especially important in dynamic contexts where
adversaries adapt, those whose work is being assessed learn to “play the
game,” and circumstances change.
5
With this caveat expressed, let us
now proceed to discuss possible metrics for many of the subjects dis-
cussed in the main text.
Root-Cause Factors
e elements that constitute root causes of terrorism are fairly well
understood, if not their relative strength and relationships (see Chapter
Analytic Measures for Counterterrorism and Counterinsurgency 463
Two, by Noricks). It is useful, in terms of warning, to monitor trends
in these elements or factors contributing to these elements. For root
causes, the desired end-state is the prevention or avoidance of terrorism
and insurgency. e elements that I consider in subsequent paragraphs
are regime legitimacy, repression, demography, grievances, and mobi-
lizing structures. (See Table B.1.)
Regime Legitimacy
I can divide the measures of legitimacy between those that are predictive
(or “leading”) and those that lag shifts in popular perception. Upward
movement or an acceleration in inflation (consumer price index or CPI),
unemployment, and income inequality (measured by the Gini index or
by concentration of land ownership) or downward shifts in the prices
of exported goods all may have a degree of predictive power in measur-
ing shifts in popular opinion of a regime (Long, 2006). e activity
of small- to medium-sized enterprises (SMEs) may also be a leading
indicator of insurgent or terrorist action (Army, 2006), and changes in
the availability of goods may also be of interest if these groups oppose
the presence of particular goods, such as alcohol or tobacco.
6
Dramatic
increases in the prices of goods that are uniquely from a specific area
may be indicative of insurgent taxation (direct or indirect) and thus may
indicate the weakness of the regime in that locale (Fall, 1966). Further,
changes in flows of foreign investment or domestic private investment
into an area may be a leading indicator of a change in political or social
stability. Economic indicators may be reliable indicators of shifts in the
perceived legitimacy of a regime but are not valid measures in this con-
text, because they reflect system processes that are not directly connected
to the end-state of perceived legitimacy and are thus mostly MOPs.
Because the desired end-state in this instance is a regime that
is legitimate in the eyes of the populace, indicators that capture gov-
ernment control over administrative processes may reflect changes
in popular perceptions of the regime—once government action is
controlled for. Bernard Fall found that government appointment of
schoolteachers and collection of taxes reflected the weakness of the gov-
ernment of Vietnam in the late 1950s when faced with the Vietminh
(Fall, 1956). Because these measures are responsive and capture both
464 Social Science for Counterterrorism: Putting the Pieces Together
Table B.1
Root-Cause Measures
Concept Measure Group
Measure
Type Comments
Regime
legitimacy
Leading economic indicators
(e.g., consumer price index,
unemployment, foreign direct
investment flows, consumer
price indices)
MOP Moderate data-collection
difficulty; reliable and
responsive; no direct
connection to end-state
Regime
legitimacy
Lagging economic indicators
(e.g., gross domestic product,
wealth inequality [i.e., Gini
index], stocks of foreign direct
investment)
MOP Moderate data-collection
difficulty; reliable and
responsive; no direct
connection to end-state
Regime
legitimacy
Measures of regime control
(e.g., taxes collected per
capita, school teacher
assignments)
MOE Moderate data-collection
difficulty; reliable
and responsive; direct
connection to end-state
Regime
legitimacy
and grievances
Polled support for government
and support for government
among key opinion leaders
MOP Difficult data-collection;
reliability is questionable;
no direct connection with
end-state
Regime
legitimacy
Social ferment (e.g.,
numbers and intensity of
demonstrations, desertion
rate from military, rise of
subversive groups)
MOE Moderate data collection
difficulty; reliable and
responsive; well connected
to end-state
Repression Disproportionate flows of
government funds
MOP Difficult data collection;
validity and reliability are
questionable; no direct
connection with end-state
Repression Disenfranchisement of at-risk
social groups
MOP Very difficult data
collection; validity is
questionable; no direct
connection with end-state
Repression Limitation of access to key
resources for social groups
MOP Difficult data collection;
validity is questionable; no
direct connection with end-
state
Demographics Increase in number of young
males in the population
MOP Easy data collection; not
responsive; no direct
connection with end-
state but should be used
in conjunction with other
measures