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P A R A M E T E R S
Contemporary Strategy & Landpower

VOL. 45 NO. 3 AUTUMN 2015

Special Commentary:
Soldiers Fighting Alone

Patrick Porter

Strategic Leadership
William E. Rapp

Jason W. Warren

Countering Gray-Zone Wars
Jakub Grygiel

William G. Pierce, Douglas G. Douds,
& Michael A. Marra

Thinking Strategically
Yakov Ben-Haim

David Patrick Houghton

Regional Challenges
Ted Middleton
Daniel Morgan

Page 2

Parameters is an official US Army Periodical, published quarterly by the US Army War College. The Secretary of the Army has determined that
publication of this periodical is necessary in the transaction of the public business as required by law of the Department. Use of funds for printing this
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Disclaimer: Articles and reviews published in Parameters are unofficial expressions of opinion. The views and opinions expressed in Parameters are those
of the authors and are not necessarily those of the Department of the Army, the US Army War College, or any other agency of the US government.

Editorial Board Members

Contributing Editors

Dr. Hal Brands
Duke University

Dr. Robert J. Bunker
US Army War College, SSI

Mr. Jeffery L. Caton
Kepler Strategies, LLC

Colonel Murray R. Clark, USAF
Norwich University

Dr. Martin L. Cook
US Naval War College

Dr. Conrad C. Crane, LTC (USA Retired)
Military History Institute

Prof. Audrey Kurth Cronin
George Mason University

Dr. Jacqueline Newmyer Deal
Long Term Strategy Group LLC

Mr. Gene Del Bianco
United States Central Command

Mark J. Eshelman, COL (USA Retired)
US Army War College, DDE

Dr. Paul Rexton Kan
US Army War College, DNSS

James O. Kievit, LTC (USA Retired)
At Large

Dr. Janeen M. Klinger
US Army War College, DNSS

Dr. Richard Krickus
University of Mary Washington (Professor Emeritus)

Dr. Matthew C. Mason
US Army War College, SSI

Dr. Andrew Monaghan
Chatham House

Dr. Matthew Pinsker
Dickinson University

Dr. George E. Reed, COL (USA Retired)
University of San Diego

Dr. Thomas Rid
King’s College London

Dr. Nadia Schadlow
Smith Richardson Foundation

Dr. Sibylle Scheipers
University of St. Andrews

Dr. Andrew C. Scobell
RAND Corporation

Dr. Kalev Sepp
Naval Postgraduate School

Dr. Luis Simón
Vrije Universiteit Brussel

Dr. Anna Simons
Naval Postgraduate School

Dr. Don M. Snider
US Army War College, SSI

John F. Troxell, COL (USA Retired)
US Army War College, SSI

Dr. Marybeth P. Ulrich
US Army War College, DNSS

Ms. Lesley Anne Warner
At Large

Dr. Katarzyna Zysk
Norwegian Institute for Defence Studies

Dr. Hal Brands
Duke University

Dr. Jacqueline Newmyer Deal
Long Term Strategy Group LLC

Dr. Steven Metz
US Army War College, SSI

Prof. Patrick Porter
University of Exeter

Acting Secretary of the Army
Mr. Eric K. Fanning

Chief of Staff of the Army
General Mark A. Milley

Commandant
Major General William E. Rapp

Editor
Dr. Antulio J. Echevarria II

Managing Editor
Ms. Jeanette M. Moyer

Assistant Editor
Mr. Richard K. Leach

Leonard J. Fullenkamp, COL (USA Ret.), Emeritus

Page 73

thinking Strategically Ben-Haim 71

Selecting aerial intel and delivery because it is predicted, by our knowl-
edge, to yield a better outcome than landpower, is unreliable because the
knowledge is uncertain and likely wrong in significant ways. In contrast,
the robust-satisfying approach is to select the option that would achieve
the specified goals with the greatest robustness against uncertainty in
the knowledge.

Second, goals that are more numerous or quantitatively more
demanding, are also more vulnerable (less robust) to strategic uncer-
tainty. For example, if the goal is to prevent both civilian casualties and
property damage, then more contingencies can prevent achievement of
the goal, than if the goal is only to prevent casualties. Similarly, the goal
of preventing all civilian casualties can fail in more ways, and is thus
less robust, than the goal of keeping casualties below a threshold, say 5
per year. We can summarize this by saying that more demanding and
ambitious goals are more vulnerable to surprise. We are not saying that
more audacious actions are necessarily less robust. We are saying that
striving to achieve more ambitious outcomes can fail in more ways than
striving to achieve less. A standard approach – optimizing the substan-
tive outcome – would favor achieving more rather than less. In contrast,
the robust-satisfying approach tries to achieve specified goals despite
inevitable surprises along the way.

Third, the option that is preferable, based on its predicted outcome,
may in fact be less robust than other alternatives for achieving the goal.
This was true in the Theban wars, where uniform deployment of the
Spartan phalanxes was disastrous for Sparta. The choice between aerial
intel and delivery, and landpower, is more complicated. Aerial intel
and delivery looks better than landpower because the knowledge pre-
dicts better outcomes with aerial intel and delivery. If the goal is very
demanding (e.g., no casualties), then aerial intel and delivery may be the
only feasible option and it will be more robust than landpower which
would not reach the goal even if the knowledge is correct. This has two
implications. First, the robustness of aerial intel and delivery for achiev-
ing a very demanding goal will be small, so perhaps the goal should
be re-examined. The robustness analysis reveals situations in which
existing capabilities can’t reliably deliver the goals; consequently, the
goals may need to be modified. Second, as a goal is relaxed (e.g. accept-
ing greater loss of life or property), landpower becomes more robust
against surprise. In short, the robust prioritization of options may differ
from the prioritization based on outcome optimization. That is, land-
power may be more robust than aerial intel and delivery for achieving
specified goals, even though aerial intel and delivery is predicted (by our
knowledge) to have a better outcome. Furthermore, the actual choice
depends on the goals. Very demanding goals (very low civilian injury
and damage) will indicate aerial intel and delivery, while less demanding
goals will indicate landpower.

Finally, the analysis identifies and clarifies the implications of central
judgments that must be made. The info-gap robust-satisfying analysis is
a conceptual framework for deliberation, judgment, and selection of an
option.

Page 74

72 Parameters 45(3) Autumn 2015

Conclusion
The future will often be surprising because current knowledge and

understanding are incomplete or deficient in functionally important
ways. Strategic uncertainty is the disparity between what one knows,
and what one needs to know in order to make a responsible decision.
Strategic uncertainty permeates defense policymaking and strategic
planning.

Planners and decision-makers for strategic issues must do their best,
but this does not mean achieving the best conceivable outcome. Political
rhetoric aside, strategic planners must identify critical goals – outcomes
that must be achieved, without which the result would be unaccept-
able – and then choose a decision that will achieve those goals over
the widest range of surprise. Referring to the aerial intel and delivery/
landpower example discussed earlier, we can contrast conventional
outcome-optimization, with the proposed robust-satisfying approach.
Conventionally one says: Use your best knowledge to predict outcomes,
and then adopt the plan whose outcome is predicted to be best. Aerial
intel and delivery was predicted to have lower cost than landpower, and
thus to be preferred by the outcome-optimizer. However, the prevalence
of strategic uncertainty means that our knowledge is wrong is important
and unknown ways. This undermines the reliability and usefulness of
such predictions. The robust-satisfying approach in choosing between
aerial intel and delivery and landpower begins by imagining how our
knowledge could err. One then chooses the option that would cause no
more than acceptable loss over the widest range of deviation between
our expectations and what the future could bring. Because of strategic
uncertainty, planners should maximize the robustness against surprise
in striving to achieve critical goals. It is unrealistic, and may be irrespon-
sible, to try to maximize the substantive value of the outcome itself.

We described the decision methodology of robust-satisfying and its
three components (knowledge, goals, and uncertainties), and illustrated
the prioritization of decision options with two examples. The methodol-
ogy is relevant to many challenges facing the United States.

Consider US coordination with a friendly state, in competition with
a neighboring state that can project both land and marine power. A
“competitive strategies” model argues that landpower development by
the friendly state could threaten the competitor’s border and draw the
competitor away from maritime competition with the United States. In
contrast, a “strategic partnership” model argues that friendly maritime
development could assist US efforts to protect the maritime commons
against the competitor.

Difficulty in establishing a US policy preference derives in part
from uncertainty in the relative validity of these two models. Friendly
landpower buildup could, unlike the competitive strategies prediction,
drive the competitor to maritime buildup as a path of least resistance for
power projection. Or, friendly maritime growth could, unlike the strate-
gic partnership anticipation, lead to re-doubled maritime competition in
response to augmented maritime challenges. Strategic uncertainty domi-
nates this policy selection, and weighs against choosing the strategy with
the best predicted outcome. The robust-satisfying approach chooses the

Page 145

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