political economy of uavs, and cost-benefit analysis and optimization of uav usage in military...
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Political Economy of UAVs, and Cost-Benefit Analysis and Optimization of UAV
Usage in Military Operations
By Oleg Nekrassovski
Introduction:
The great significance of UAVs (Unmanned Aerial Vehicles) for modern warfare can be seen in
the fact that in the report outlining the keys to success of the US military, which was released
by The Defense Science Board of the Office of the Secretary of Defense (OSD) in February 2004,
the three key points were: decision weapons, UAVs, and knowledge (Valerdi, 2005).
But what exactly are these UAVs? According to the Department of Defense (DOD), UAVs are
“powered, aerial vehicles that do not carry a human operator, use aerodynamic forces to
provide vehicle lift, can fly autonomously or be piloted remotely, can be expendable or
recoverable, and can carry a lethal or nonlethal payload” (Gertler, 2012).
The present paper will first provide an overview of political and economic aspects of UAVs,
before going on to conducting a cost-benefit analysis of UAV versus manned aircraft usage in
military operations. Finally, the paper will address the optimal employment and deployment of
UAVs in military operations, through mathematical optimization.
Defense policy, economy, and UAVs:
“Laden with sophisticated sensors and carrying Hellfire missiles and laser-guided bombs, they
[UAVs] patrol the skies above Afghanistan, launch lethally accurate strikes against terrorists in
the tribal areas of Pakistan, Yemen and Somalia and have helped NATO turn the tide against
Muammar Qaddafi's forces in Libya” (The Economist, 2011).
Moreover, “The nature of the Iraq and Afghanistan wars has … increased the demand for UAVs,
as identification of and strikes against targets hiding among civilian populations required
persistent surveillance and prompt strike capability, to minimize collateral damage. Further,
UAVs provide an asymmetrical—and comparatively invulnerable—technical advantage in these
conflicts” (Gertler, 2012).
Also, UAVs may make the war more “just” and legal. In fact, as recently as 1997, some of the
most advanced jet fighters in usage had
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... no video links between cockpits and command centres, and even radio contact was patchy at times. As a
result, pilots often made their own calls on whether or not to strike. Today's drones, blimps, unmanned
boats and reconnaissance robots collect and transmit so much data ... that Western countries now practise
“warfare by committee”. Government lawyers and others in operation rooms monitor video feeds from
robots to call off strikes that are illegal or would “look bad on CNN,” (The Economist, 2012)
Hence, it is not surprising that
The drone campaign still receives uncharacteristic bipartisan support in America and is credited with
severely damaging al-Qaeda. But concerns about it are growing, and not just from civil-liberties groups. Mr
Obama wants to bring greater transparency and legal rectitude to the way America goes about eliminating
its foes, while Mr Brennan appears to want to hand over the operation of lethal drones to the Pentagon.
(The Economist, 2013)
These more recent concerns with UAVs stem, in part, from various, somewhat politically
embarrassing incidents involving American UAVs attacking ground targets on foreign soils. In
one such, recent, widely publicized incident, al-Awlaki, al-Qaeda’s chief propagandist and
strategist, was killed in Yemen in a targeted attack by American UAVs. The problem was that al-
Awlaki possessed US citizenship. Hence, perhaps not surprisingly, this incident “created a minor
stir among civil-liberties groups claiming that his citizenship entitled him to “due process”” (The
Economist, 2013).
Though the aviation and related industries are also likely to be concerned about political
fallouts from the military usage of UAVs; they also have other concerns and interests, of their
own, regarding this emerging, new, technology. In fact, “Industry analysts consider UAVs a
“market discontinuity” because they are a disruptive innovation which is changing the industry
just as the telephone and personal computers did so in the 1870’s and 1970’s, respectively. As
the operational benefits for UAVs become more apparent the market will continue to expand
and create applications beyond the current military scope” (Valerdi, 2005).
For now though, the main, increasing, investors and supporters of UAV development and
procurement, in the US, are the Congress and the Department of Defense (DOD). The process
has not been smooth, however, as Congress tends to impose various constraints and
regulations on such programs (Gertler, 2012).
Cost-Benefit Analysis:
The problem of selecting UAVs, versus manned aircraft, is a simple example of policy analysis.
“… we posit that efficiency should always be a goal in public policy analysis” (Vining &
Boardman, 2007, p. 51). And “An allocatively efficient policy is one that achieves the maximum
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difference between the social benefits and social costs relative to the alternatives, including the
status quo” (Vining & Boardman, 2007, p. 53).
Among the four policy analysis method classes described by Vining and Boardman (2007),
efficiency analysis class is most appropriate for our task. In efficiency analysis “the analyst
accepts the legitimacy of allocative efficiency as the sole goal, but is not willing (or able) to
monetize all of the impacts” (Vining & Boardman, 2007, p. 59).
One variation of efficiency analysis that appears to be most useful for choosing between UAVs
and manned aircraft, given the data on the subject obtained by the author of the present
paper, is qualitative cost-benefit analysis (qualitative CBA). Qualitative CBA
… entails consideration of all of the efficiency impacts of each alternative, but the analyst is not willing or
able to monetize all of the impacts… At one extreme, this might look like a ‘back of the envelope’ analysis,
where no entries are monetized and the cell entries are simply ‘+’s or ‘—’s… Often, however, even in
qualitative CBA, one or more efficiency impacts are omitted. This type of analysis is best described as
Incomplete Qualitative Cost-Benefit Analysis (IQCBA). (Vining & Boardman, 2007, p. 63)
Given that the data on UAVs and manned aircraft, obtained by the present author, clearly does
not consist of all of the efficiency impacts of each alternative, IQCBA is clearly the most
appropriate approach for the present study.
Optimization:
The problem of optimally deploying and employing a UAV or manned aircraft is an example of
an optimization problem. Optimization is a science of determining the ‘best’ or optimal
solutions to “to certain mathematically defined problems, which are often models of physical
reality.” It involves, among other things, “the determination of algorithmic methods of
solution,” and experimentation with such methods on theoretical and real life problems
(Fletcher, 2013, p. 1).
Optimization problems are often divided into two classes based on the variables involved. The
first such class consists of problems that involve continuous variables. While the second, of
problems involving discrete variables. An optimization problem involving discrete variables is
often called a combinatorial or discrete optimization problem; with the study of such problems
being known as combinatorial optimization (Lee, 2004). Since the study of optimal usage of
UAVs, presented later in this paper, builds a model, of the required problem, with discrete
variables; it is a clear example of discrete optimization.
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Thus, for our purposes (and expressed more rigorously), “A discrete-optimization problem is a
problem of maximizing a real-valued objective function c [e.g. the number of insurgents
detected] on a finite set of feasible solutions S [e. g. permissible locations for UAVs’ search areas
and ground control units]” (Lee, 2004, p. 1). Strictly speaking, though, the problem does not
exist, because all feasible solutions can be enumerated. In other words, in our example, all
permissible locations for UAVs’ search areas and ground control units can be determined and
listed, and the one, yielding the largest number of insurgents being detected, chosen. Such an
approach is absurd, however, because even though the number of feasible solutions is finite,
enumerating all of them would be, for all practical purposes, an infinitely long enterprise.
Hence, a discrete-optimization problem involves developing “algorithms [i.e. a specified
sequence of required calculations] that are provably or practically better than enumerating all
feasible solutions” (Lee, 2004, p. 1). Thus, in our example, a successful discrete-optimization
algorithm would allow the determination of specific locations for UAVs’ search areas and
ground control units, which will lead to the detection of the largest possible number of
insurgents by the UAVs, once all the required variables (e.g. insurgents’ movements and the
reliability of the employed UAVs) are specified.
Costs and Benefits of UAVs vs Manned Aircraft in Military Operations:
“UAVs are thought to offer two main advantages over manned aircraft: they eliminate the risk
to a pilot’s life, and their aeronautical capabilities, such as endurance, are not bound by human
limitations” (Gertler, 2012). UAVs “may also be cheaper to procure and operate than manned
aircraft.” However, this should be “weighed against their greater proclivity to crash” (Gertler,
2012). UAVs “also improve situational awareness and reduce many of the emotional hazards
inherent in air and ground combat, thus decreasing the likelihood of causing civilian
noncombatant casualties.” UAVs also reduce “the reaction time in a surgical strike” (Gertler,
2012).
Because they provide more detailed information about targets, their strikes are usually more accurate and
cause fewer civilian casualties (the idea that drones are constantly blowing up Afghan weddings is
wrong)… For counter-insurgency or anti-terrorism missions, drones are easier to use discretely than
manned aircraft because most of the team required to support them is far from the conflict zone. Nor do
UAVs have to be rotated in and out of a war zone like manned aircraft. Training UAVs controllers, even
those with no previous flying experience, costs less than a tenth as much as turning out a fast-jet pilot. (The
Economist, 2011)
However, “current drones depend on two-way satellite communications. If the datalink is
broken the remote pilot will lose direct control of the aircraft…” This makes UAVs vulnerable to
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datalink failure, whether from “electronic jamming or a direct attack on a communications
satellite” or simple malfunction (The Economist, 2011). “A related problem afflicting today's
drones is the slight delay between the remote pilot sending an instruction to the aircraft and its
response (known as latency). In contrast, a pilot in the cockpit can react instantly to a threat
and take evasive action” (The Economist, 2011).
It should be remembered that military aircraft engages in various types of missions, many of
which cannot yet be performed by UAVs. In fact, at present, UAVs are limited to performing
intelligence gathering and striking ground targets (Gertler, 2012). It is possible that in the
future, UAVs will be able to engage in resupply, combat search and rescue, refueling of other
aircraft, and air combat (Gertler, 2012). However, at present, these and other missions can only
be performed by manned aircraft. Thus, it is only meaningful to compare UAVs against manned
aircraft with regards to intelligence gathering and air-to-ground strike missions.
The following tables attempt to compare the costs and benefits of UAVs and manned aircraft
for intelligence gathering and air-to-ground strike missions. An attempt is made is to quantify
the above-described costs and benefits of each type of aircraft by assigning values to each
quality (based on the qualitative descriptions of the extent of each cost or benefit, given above)
according to the following approximate scheme: 0 (nonexistent), 1 (low), 2 (medium), and 3
(high). Due to the absence of data to the contrary, and in order to simplify the comparison,
each quality is given an equal weight. All costs are treated as negative qualities and, to this end,
are assigned a negative sign; while all benefits are considered positive, and are assigned a
positive sign. A summation of all numbers assigned to each presented quality, separately for
each type of aircraft, is used to determine the superior type of aircraft in each case; with the
“winner” being the aircraft with the larger sum.
Table 1: Intelligence Gathering
QUALITY EXTENT (0[nonexistent]/
1[low]/2[medium]/3[high])
UAVs Manned Aircraft
Risk to human operators 0 -3
Aeronautical capabilities +3 +2
Cost of the aircraft -2 -3
Susceptibility to crashing -3 -2
Discreetness of usage +3 +2
Cost of training an operator -1 -3
Vulnerability to datalink failure -3 0
Need to be rotated in and out of a war zone 0 -2
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Latency -1 0
Total: -4 -9
Table 2: Strikes of Ground Targets
QUALITY EXTENT (0[nonexistent]/
1[low]/2[medium]/3[high])
UAVs Manned Aircraft
Operator’s/pilot’s situational awareness +3 +2
Risk to human operators 0 -3
Level of emotional hazards linked to
increased civilian casualties
-1 -3
Aeronautical capabilities +3 +2
Reaction time +3 +2
Cost of the aircraft -2 -3
Susceptibility to crashing -3 -2
Accuracy of strikes +3 +2
Need to be rotated in and out of a war zone 0 -2
Discreetness of usage +3 +2
Cost of training an operator -1 -3
Vulnerability to datalink failure -3 0
Latency -1 0
Total: +4 -6
Thus, as the above approximate calculations indicate, UAVs appear to be superior to manned
aircraft in intelligence gathering and air-to-ground strike missions. However, like with any
assets, in order to provide the greatest possible assistance, UAVs should be used in the most
optimal way.
Optimal Deployment and Employment of UAVs in Military Operations:
Kress and Royset (2007), for example, “have developed a two-stage stochastic integer linear
programming model for optimizing UAV deployment and employment” during intelligence
gathering missions carried out by a special operations team equipped with short-range
surveillance UAVs. Specifically, their model considers “situations where targets (e.g.,
insurgents) operate in a region of interest and a small special operations team is assigned to
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search and detect these targets” (Kress & Royset, 2007). Kress and Royset’s (2007) model
“determines optimal locations of ground control units and mobile control centers, as well as
time-phased search areas for UAVs.” “The output of [their] model is robust with respect to a
variety of contingencies” because it accounts for the “(uncertain) information about target
movement as well as reliability of the available UAVs.” Kress and Royset (2007) have subjected
their model to an empirical test, in which the comparison of the optimized plans generated by
the model “with manual plans generated by experienced commanders,” showed that the plans
provided by the model “resulted in 50% more detection opportunities of targets.”
Conclusion:
Thus, we have seen that the existence and employment of UAVs considerably affects the
defense policy and economy. And when it comes to their comparison with manned aircraft,
UAVs appear to be superior in intelligence gathering and air-to-ground strike missions. Given
the necessary data, however, such a comparison can be greatly enhanced by elaborating on,
analysing, and rigorously quantifying each cost and benefit individually, before even attempting
to sum them up. Moreover, accounting for all possible costs and benefits will lead to a far
better result. Either way, however, we have also seen that the optimal employment and
deployment of UAVs in intelligence gathering and air-to-ground strike missions requires
appropriate optimization models; which are superior to the manual plans generated by
experienced commanders.
References
Fletcher, R. (2013). Practical Methods of Optimization. John Wiley & Sons.
Gertler, J. (2012). U.S. Unmanned Aerial Systems. CRS Report for Congress. Available at
http://www.fas.org/sgp/crs/natsec/R42136.pdf.
Kress, M., and Royset, J. O. (2007). Aerial Search Optimization Model (ASOM) for UAVs in
Special Operations. Available at http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA486711.
Lee, J. (2004). A First Course in Combinatorial Optimization. Cambridge: Cambridge University
Press.
Interdisciplinary, unpaid research opportunities are available. Various academic specialties are required. If interested, email me at
The Economist. (2011, Oct. 8th). “Unmanned aerial warfare: Flight of the drones.” Available at
http://www.economist.com/node/21531433.
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drone-has-enjoyed-rare-bipartisan-support-america.
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http://web.mit.edu/rvalerdi/www/UAVcostValerdi.pdf.
Vining, A. R. and Boardman, A. E. (2007). The Choice of Formal Policy Analysis Methods in
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