What is Killing the Intelligence Dinosaurs?

What is Killing the
Intelligence Dinosaurs?
March 2004
John Fairweather, President
___________________
MitoSystems, Inc.
3205 Ocean Park Blvd., Suite 180
Santa Monica, CA 90405
Tel: (310) 581-3600
FAX: (310) 581-3777

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Table of Contents
1. INTRODUCTION ............................................................................................................................................ 1
2. WHAT IS THE PROBLEM? ............................................................................................................................ 2
2.1 THE OODA LOOP......................................................................................................................................... 2
2.2 INFRASTRUCTURE PROBLEMS ........................................................................................................................ 4
2.3 TECHNOLOGY PROBLEMS ............................................................................................................................ 10
3. DEATH BY REQUIREMENTS CHANGE – A CASE STUDY.................................................................. 18
3.1 THE CLASSICAL APPROACH ........................................................................................................................ 20
3.2 THE MITOPIA-BASED APPROACH ................................................................................................................ 29
3.3 OTHER APPROACHES?................................................................................................................................. 34
4. HOW DOES MITOPIA® ADDRESS THE OVERALL PROBLEM? ....................................................... 38
5. SUMMARY...................................................................................................................................................... 40
List of Tables
TABLE 3.1 TYPICAL LOC/PERSON MONTH BY APPLICATION TYPE........................................................................... 20
TABLE 3.2 LINES OF CODE/FUNCTION POINTS FOR VARIOUS PROGRAMMING LANGUAGES...................................... 22
TABLE 3.3 BACKFIRE ADJUSTMENT FACTORS FOR DIFFERING SYSTEMS COMPLEXITY ............................................ 23
TABLE 3.4 SOFTWARE EFFORT BY PROJECT PHASE ................................................................................................. 25
TABLE 3.5 PRODUCTIVITY DEGRADATION WITH TEAM SIZE .................................................................................... 27
TABLE 3.6 COMPARISON OF COST, MANPOWER AND TIME FOR MITOPIA® VS. CURRENT APPROACHES................... 33
List of Figures
FIGURE 2.1 THE BOYD CYCLE (OODA LOOP) .......................................................................................................... 3
FIGURE 2.2 THE TRADITIONAL INTELLIGENCE CYCLE............................................................................................... 4
FIGURE 2.3 COLD WAR OODA LOOPS ..................................................................................................................... 5
FIGURE 2.4 TODAY’S OODA LOOP .......................................................................................................................... 7
FIGURE 2.5 INFORMATION SYSTEMS KNOWLEDGE PYRAMID .................................................................................. 10
FIGURE 2.6 INFORMATION SYSTEM FOCUS AT EACH STAGE OF OODA LOOP........................................................... 11
FIGURE 2.7 THE SOFTWARE BERMUDA TRIANGLE - INITIAL.................................................................................... 14
FIGURE 2.8 THE SOFTWARE BERMUDA TRIANGLE – SOME TIME LATER.................................................................. 15
FIGURE 3.1 ISO INFORMATION TECHNOLOGY CHANGES......................................................................................... 19
FIGURE 3.2 CLASSIC SOFTWARE DEVELOPMENT PRODUCTIVITY CURVES ............................................................... 21
FIGURE 3.3 CLASSICAL MANPOWER-LOADING CURVE ............................................................................................ 26
FIGURE 3.4 MITOPIA® MANPOWER-LOADING CURVE............................................................................................. 31
FIGURE 3.5 UPDATED SOFTWARE DEVELOPMENT PRODUCTIVITY CURVES.............................................................. 32
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1. Introduction
This white paper discusses problems faced by intelligence organizations in adapting to the
rapidly changing and hugely complex information environment that exists in the world today,
the difficulties inherent in monitoring this information ocean, and extracting indicators and
warnings from it to provide early notification to decision makers of existing or impending
terrorist threats. Central to understanding the reasons for past intelligence failures and
exponentiating intelligence system costs (and failures) is a full understanding of the decision
cycle or Observe Orient Decide Act (“OODA”) loop teachings of Colonel John Boyd (1927 –
1997), considered by some to be one of the foremost thinkers in military strategy of all time.
Unfortunately, much of Boyd’s teachings continue to be ignored today, and it is the failure to
fully and systematically embrace these teachings, which lies at the root of many of the problems
faced by intelligence organizations today. The central point is that change is a pervasive feature
of the information world, and that it is the failure to systematically address change and its
impact that results in many of the dramatic failures that we see today. These failures will
continue in the future if we do not learn from our mistakes and radically alter the way we
collect, analyze, and disseminate information.
This paper describes the infrastructural and technical impact of an OODA loop-based mind-set
(or the lack thereof) and presents many reasons why failure to adopt this mind-set has
contributed to intelligence failures in the past, and continues to do so today. The technical issues
that lead to the failure of complex intelligence systems are examined in detail and illustrated in
depth using a specific example. Finally, the paper discusses the advantages of using the
Mitopia® architecture, developed over the last 14 years specifically, to address these problems in
the intelligence field, and illustrates the significant benefits to be achieved by moving to such an
architecture. The paper presents concrete parallel findings for the chosen example when
addressed using a Mitopia®-based approach, and shows that in using such an approach it is
possible to address the problem given, whereas using the standard approaches popular today, it
is not.
The author has spent over 15 years developing the field of adaptive intelligence architectures,
specifically in the design and implementation of the architecture that has become Mitopia®. It is
the perspective and experience gleaned from this singular focus, and the frustrations
experienced in seeing others continue to pursue outmoded and technically bankrupt approaches,
that lead to the writing of this paper. It is hoped that those in charge of plotting our future
systems’ course will learn from, and incorporate, the points raised in this paper, whether by use
of Mitopia™ or otherwise. Failure to do so, in the author’s opinion, would constitute an
abrogation of responsibility to our collective futures.
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2. What is the Problem?
[1]
The problem in a nutshell is failure to adapt.
The world has changed; the intelligence threats we face are no longer few and large, slow
moving, and relatively static, they are small, many and fecund; they are diverse and adapting or
evolving at a ferocious rate. Our failure to adapt is both a technical problem as well as an
organizational and infrastructure problem, and it comes from a complete failure on the part of
our existing technologies and our existing intelligence organizations to consider the impact of
decision cycles or OODA loops in any of their doings. These organizational failings are largely
the result of ignorance and complacence. As a result, the government lags by decades behind
the commercial world. Companies failing to adapt to a competitive business-cycle approach are
now becoming extinct. They have been selected out for failure to adapt. As far as our technical
failings to facilitate an OODA loop-based framework, industry is in the same boat as the
government here. Simply put, the systems to enable this approach simply never existed in order
to be adopted.
2.1 The OODA Loop
[1]
Modern competitive and business intelligence cycles are now based on some derivative of the
Boyd Cycle (or OODA loop). This cycle was developed by Colonel John Boyd as a result of his
studies (and experience) of air-to-air combat in the Korean War. What Boyd discovered was
that the main factors that enabled U.S. pilots to consistently win dogfights were firstly that the F-
86 fighter aircraft’s canopy was larger than that of the opposing Mig-15’s, thus giving a greater
field of vision, and secondly, that although the F-86 aircraft was larger and slower, it was more
maneuverable (higher roll-rate), thus allowing US pilots to make more frequent adjustments.
Boyd was later largely responsible for the design of the F-15 canopy and perhaps more than
anyone else, contributed to development and deployment of the F-16. Boyd also helped create
the Fighter Weapons School at Nellis AFB, Nevada. The result of formalizing and abstracting
Boyd’s insight became a fundamental part of air-force fighter tactics, and was later embraced
fully by the Marines. The bulk of the defense establishment, however, has ignored or is
unaware of his teachings. In the intelligence community, his work is virtually unknown.
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Figure 2.1 The Boyd Cycle (OODA Loop)
[2]
The central idea behind the OODA loop is that all thinking entities are executing OODA loops of
their own (consciously or otherwise). The key to success in any conflict or competition is
therefore either:
a) Being able to cycle around the loop faster than your opponent
b) Disrupt the opponents OODA loop to cause him to slow down or make mistakes
c) Alter the tempo and rhythms of your own loop so that the opponent cannot keep up with
you
[3]
For a full description of the OODA loop and how it ties in with the intelligence problem, as well
as a complete bibliography in this area, see the paper “Avoiding Information Overload
Through the Understanding of OODA Loops, A Cognitive Hierarchy and Object-Oriented
Analysis and Design” by Dr. R.J. Curts, CDR, USN (Ret.), and Dr. D.E. Campbell, LCDR, USNR-
R(Ret.). This paper can be downloaded from www.belisarius.com. This site deals with business
intelligence, and is heavily focused on the work of Boyd. While this author is not in complete
agreement with the paper’s assertion that object-oriented techniques provide a practical
approach to addressing the issue, the paper does effectively describe the need for a ground-up
approach, and a consistent method for representing and storing data.
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2.2 Infrastructure Problems
[1]
The traditional intelligence cycle is depicted in the diagram below. In this model, the
intelligence consumers make known their needs for information via requests that are passed to
the organization that assigns priorities to information requirements. Determination of priorities
leads to tasking, which results in the various collection mechanisms or agencies taking steps to
gather the raw information necessary to pass on to the analysts. After performing whatever
analyses best fit the problem domain, the analysts prepare reports, which are then reviewed and
coordinated and finally disseminated back to the original intelligence consumer.
Intelligence
Consumer
Requests for
Production &
Information
Dissemination
Requirements
Collection
Analysis
& Tasking
Intelligence Process
Figure 2.2 The Traditional Intelligence Cycle
[2]
The cycle described above represents the best thinking on how intelligence should work from the
1940’s and 1950’s. The cycle is still utilized today by the government intelligence community. In
today’s fast moving and information-rich environment, such a cycle is unfortunately inadequate
to the task of tracking the complexities of unfolding world events. A full description of the
problems with such a cycle is beyond the scope of this document, however, the basic problems
can be summarized as follows:
a) The cycle is too slow. Indeed it is not clear that it is a cycle at all, since most requests
result in just a single iteration. The existence of various organizations (bureaucracies) in
the cycle combined with the time taken for information to pass through the bureaucratic
interfaces in the loop mean that the cycle cannot keep up with evolving events.
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b) Because it is essentially command driven, the cycle only allows looking into questions
that the intelligence consumer already ‘knows’ to ask. As discussed previously, the
reality is that the cycle must support the discovery of things you didn’t even know were
important. The September 11th attacks provide a perfect example. This top-down
approach may have suited a situation where the enemy was known and stable (i.e.,
U.S.S.R.), but it does not deal well with today’s world where enemies are small,
distributed, loosely coupled, change constantly, and can have impacts disproportionate
to their size. The intelligence consumer cannot anticipate all possible threats and task the
complete cycle to investigate each.
c) The lack of feedback in the cycle between the consumer and the analyst, combined with
the inability of the consumer to directly access and examine the backup material leading
to analytical conclusions, tends to create a situation where the final product may not meet
the consumer’s requirements, and thus redundant iterations through the cycle with
corresponding increases in time and cost are required.
[3]
If we explore the reasons behind the community’s failure to move to a modern decision cycle
based on the OODA loop (or a derivative) we see that is primarily due to a failure to adapt to the
new threat environment posed by a post-cold-war world. Specifically, the fact that the enemy is
no longer the U.S.S.R. but a diverse, loosely coupled, and rapidly adapting amalgam of terrorist
organizations and other assorted whackos. Only a fraction of the intelligence problem now
relates to threats posed by sovereign states. In the good old days of the Cold War, the opposing
intelligence forces (viewed in OODA loop terms) looked something like that depicted below:
U.S.S.R
U.S.
Figure 2.3 Cold War OODA Loops
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[4]
The United States’ capabilities both in terms of intelligence gathering, and in terms of acting on
intelligence gathered were somewhat better than those of the Soviet Union. In a competitive
sense, the U.S. was bigger (hence the larger triangle). More importantly, the Soviet Union was
hampered by a hopelessly slow and ineffective decision cycle caused by excessive bureaucracy,
and a pervasive culture of distrust and territoriality throughout the organizational pyramid.
Fortunately, the Soviet decision cycle (such as it was) was thus considerably slower than that of
the United States. With both capability and adaptability in its favor, the U.S. community
gradually settled into a culture of confidence and complacency. The only thing needed was to
ensure that the U.S. organization continued to be bigger and more powerful than the Soviet one,
since there was no reason for the U.S. to even be aware of, much less consider, decision cycles in
its thinking, given its considerable advantages in this area. From these roots came the “big-iron”
mentality that now pervades not only the US military, but also the intelligence community. “All
we need is more satellites, bigger computer systems, more powerful weapons, more data and
we’ll stay ahead. We can see them building their silos earlier, building the necessary
infrastructure, it will take them years to pose any new threat; we need more of the same only
much bigger!“ So the thinking went. This thinking may have had some validity in the cold war
but it is hopelessly flawed in the new intelligence threat space.
[5]
The U.S. intelligence community now faces a multitude of much smaller but rapidly changing
threats coming from a highly (often religiously) motivated, diverse set of loosely organized
opponents with differing agendas, none of the stability of a state-sponsored organization, and
worst of all, with highly devolved command chains and decision cycles that are measured in
weeks, if not days, as opposed to the multi-year cycles of the good old days. These lengthy
governments cycles have now crystallized as congressional laws and regulations, politics, and
the Federal Acquisition Regulations (“FAR”). True, U.S. intelligence capabilities have advanced
beyond all measure: the community is now far ‘bigger’ than before. However, that is of only
limited use in this new threat space, and the picture now looks very different:
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U.S.
Figure 2.4 Today’s OODA Loops
[6]
It is like some large animal being attacked by a swarm of bees. The larger combatant cannot
track and swat every bee in the swarm because its attention must focus on each in turn, and it
takes time to actually swat and kill one. Each individual sting may hurt only a little, and we can
certainly swat whatever stings us, but there are plenty more bees where that one came from, and
the end result will always be the same. The swarm will eventually kill the animal, no matter
how large, and it will finally collapse to the ground surrounded by a mountain of dead and
dying bees. All that is needed for the swarm-of-bees strategy to work is a vast supply of worker
bees, working largely independently, and eager and willing to die for the cause. Sound familiar?
Of course, this is exactly the threat situation we now face. Our nice blue triangle, no matter how
large, will eventually look like a piece of Swiss cheese. The swarm-of-bees attack is so effective,
not because it brings overwhelming force to bear (the total weight of the attacking bees is still
only a tiny fraction of their victim), but simply because it completely overloads the OODA loop
of the victim. The problem is the nature of the large combatant. It has a singular focus; that is, it
is command or control driven in that the brain must separately and serially issue orders to track
and destroy each foe. In the face of hundreds of autonomous foes, such a strategy cannot win.
Yet this is exactly the structure we see in our intelligence organizations. How is it possible for
intelligence consumers to even be aware of all the potential foes, let alone issue orders to track
and destroy them through a command/decision cycle that is measured in weeks or perhaps
months? If we consider 9-11 to be one well-placed sting, we can see that we are now facing a
swarm of something far deadlier than bees. We are noisily stamping the ground as we do battle
with our tiny foes, but we are near to the nest, and our stamping serves only to bring out an
endless supply of more and angrier foes. We think perhaps we can track and handle our current
problems, but can we really take on the whole hive? We had better be sure, because our noise
has surely wakened them!
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[7]
It is indeed interesting to draw parallels with the reports coming lately from coalition forces
attempting to stabilize the situation in Iraq. We see quotes from military commanders stating
that they need more localized intelligence. They need systems that can adapt to the situation
faster, work more autonomously, and communicate in a more coordinated manner with central
authorities. These forces are facing a military threat from exactly the kind of swarm-of-bees
opponents that the intelligence community now faces. These are not new lessons learned from
new and unprecedented situations. The fact of the matter is these forces are experiencing the full
impact of the OODA loop lessons learned and taught by Boyd so long ago. It is to be hoped that
the fact that none of these reports is couched in any terms such as OODA loop or decision cycles
is because these authors know that their audience may not understand this, and not because our
own military has failed to learn from Boyd’s teaching. The military is now talking seriously
about re-engineering our forces to be more maneuverable, more autonomous, and more
adaptive. We should be glad that the military at least has learned (or is re-learning) these
lessons. Regrettably the same cannot yet be said for the intelligence community.
[8]
What would we ideally like our intelligence cycle and organizations to look like? We can
imagine it consisting of a huge triangle comprised hierarchically of many smaller triangles, each
of which is attached to the main body, but can swivel independently to orient itself, and is
empowered to separately track known or potential foes. Each such triangle must be capable of
rapidly passing its findings, without dilution, through the hierarchy to the decision makers and
simultaneously to the “swatter” which must be capable of swatting multiple targets at once. In
the case of the “swatter”, size really does matter, and fortunately for us, the U.S. military, given
the political will to use it, constitutes the biggest, baddest swatter on the planet. If we could only
track, target, and decide to swat our foes, we certainly have the power to do the swatting.
[9]
Why have we not moved to a system like that described above? It is unfair to place all the blame
on the intelligence agencies, though they are certainly culpable. After all, these entities can only
create such an organizational structure if we give them the considerable technical tools needed to
do so. No, on this point, the blame for failure must lie squarely on the technologists and the
companies that the government relies on for advice, and to implement such technologies. It is
these entities, the big primes and the “Beltway Bandits,” primarily that share the guilt for our
sluggishness.
[10]
The key point is that the intelligence cycle itself needs to become a Boyd cycle, and that the speed
with which it is possible to iterate through the loop is critical to success. Moreover, we need to
realize that this same OODA loop must be practiced at all levels of the intelligence hierarchy if
we wish to handle the diverse threat space that exists today. This need for rapid iteration and
recursive loop cycling is a key driver for the end-to-end technical approach advocated in this
document. By use of such an approach, the barriers between intelligence consumers and those
involved in the intelligence process itself can be broken down, and the rapid feedback loop
required can be implemented. Most importantly, however, the key lesson of Boyd’s teachings is
that the ability to rapidly adapt to change is the single most important determinant in any
competitive situation. The technology, and the system built on it, must be able to adapt as fast or
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