What does Chaos Theory mean for warfare?

[Cain]

http://www.airpower.maxwell.af.mil/airchronicles/apj/apj94/nichols.html

Maj David Nicholls, USAF
Maj Todor Tagarev, Bulgarian Air Force

For the last 30 years, the study of chaos has intrigued investigators, prompting many to see a great future for the study and application of chaos theory. In science and engineering, chaos theory has significantly improved our understanding of phenomena ranging from turbulence to weather to structural dynamics.  Chaos theory has even been used to drastically improve our ability to control some dynamic systems.  In the social sciences, there has been considerable interest in whether social phenomena, previously thought to be random, have an underlying chaotic order. Several mathematical tests for chaotic behavior have been applied to historical data from both the stock market and cotton prices. These tests indicate that these economic phenomena are chaotic and so have a deterministic basis (i.e., are governed by rules) as opposed to being random. Naturally, this has received some business attention, and at least two firms are now using chaos theory to guide their financial advice.

There is evidence that warfare might also be chaotic. First, strategic decision making, an integral part of war, has been found to be chaotic.  Second, nonlinearity, which is a requirement for chaotic behavior, appears to be a natural result of Clausewitzian friction.  Third, some computer war games6 and arms race simulations have been found to exhibit chaotic behavior. Fourth, previous work by the current authors applied several tests for chaos to historical data related to war. Those tests demonstrated that warfare is chaotic at the grand strategic, strategic, and operational levels.

An Overview of Chaos Theory

In this paper, we will discuss some important implications of chaos theory in the context of warfare. First, however, we will briefly summarize some important aspects of chaos theory.

Nonlinearity

If a system is linear, it means that the output of the system is linearly related to the input. In other words, if the input is doubled, the output will be doubled; if the input is tripled, the output will be tripled, and so on. In nonlinear systems, however, the output might be related to the square or the cube of the input. Such systems are often very sensitive to input. All chaotic systems are nonlinear.

Predictability of Chaotic Systems

Dynamic systems can differ from one another in how they change with time. In random systems, future behavior is independent of the initial state of the system and can be characterized only in terms of probabilities. For example, unless the dice are loaded, the next roll of the dice is totally independent of the previous roll. On the other hand, periodic systems return regularly to the same conditions, as exemplified by the pendulum clock. Such systems are totally predictable because once one period is known, all others must be identical. Chaotic systems are neither random nor periodic. They are not random because the future of a chaotic system is dependent upon initial conditions. They are not periodic because their behavior never repeats.

Chaotic systems never repeat exactly because their future behavior is extremely sensitive to initial conditions. Thus, infinitesimal differences in initial conditions eventually cause large changes in system behavior. An often-used example of this sensitivity is weather. Weather is so sensitive to initial conditions that there is a belief that the flap of a butterfly's wings in America could eventually cause a typhoon in China.  It is inconceivable that conditions on the earth could ever duplicate an earlier time to the point where even all butterfly flights are duplicated. Therefore, the earth's weather will never be periodic.

In addition to making chaotic systems aperiodic, extreme sensitivity to initial conditions means that it is not possible to determine the present conditions exactly enough to fully predict the future. Figure 1 illustrates this point. In figure 1, successive values for x are plotted resulting from the nonlinear equation xi+1 = 4xi - 4xi2. For one plot the initial value of x was 0.7. For the other plot it was 0.70001. Initially, they are indistinguishable from one another, but as time goes on, even such a small difference between the two is magnified until their behavior appears totally unrelated. Short-term predictions are still possible because small influences will not have had time to grow into large ones. However, what is short-term depends on how sensitive the system is to small changes at that point in time.

The importance of this concept is that it explains how a system can be governed by a set of equations and yet still be unpredictable. We cannot know the initial value of a system, such as that illustrated in figure 1, precisely enough to predict which path the system will follow. If warfare is chaotic, this tells us that we cannot make perfect predictions even if we could reduce war to a mechanistic set of equations. Fortunately, as is also illustrated by figure 1, there are bounds to the unpredictability of a chaotic system. Furthermore, chaos theory provides tools that can predict patterns of system behavior and can define bounds within which the behavior is unpredictable

http://www.airpower.maxwell.af.mil/airchronicles/apj/apj94/nic1.jpg

Phase Space

The construction of a phase space plot is often used to better understand chaotic behavior. A phase space plot is a plot of the parameters that describe system behavior. It is useful because it provides a pictorial perspective for examining the system. An example of a phase space plot for a simple pendulum is shown in figure 2. At point A in figure 2, the pendulum is the maximum positive distance from the bob's neutral point but its velocity is zero. This is shown as point A on the phase space diagram. At B the distance of the bob from its neutral position is zero, but its velocity is at a maximum (in a negative sense). The other points of the phase space plot show the relation between the velocity and position for other pendulum positions. In this case, where there is no friction, the motion of the pendulum is constrained to remain on the elliptical path shown in the phase space plot. The technical term for this ellipse is the attractor for the system. One can see that this attractor is periodic because the path of the system exactly repeats itself in each orbit around the origin.

http://www.airpower.maxwell.af.mil/airchronicles/apj/apj94/nic2.jpg

In contrast, figure 3 shows an attractor for a chaotic system. This attractor is a tangled mess of trajectories. The complexity of this attractor has led to its being dubbed a strange attractor. Although there are still constraints as to how the system behaves, there are a lot more possible states for the system. It is important to note that the phase space paths of a chaotic system will never coincide. If this were to happen, the system would become periodic. The longer a chaotic system is observed the more paths are taken and the messier the phase space plot of the attractor appears. Superficially, the attractor may appear to be completely disorganized. Closer examination of the phase space, however, reveals that the attractor is organized but in an unconventional manner.

http://www.airpower.maxwell.af.mil/airchronicles/apj/apj94/nic3.jpg

It is possible to simplify the portrayal of the attractor by taking a two-dimensional slice through it (shown in the lower half of figure 3). This also makes the structure of the attractor more obvious. This two-dimensional section is called a Poincar?© map.10

Fractals

We generally define things dimensionally in terms of integers. Lines are one-dimensional, planes are two-dimensional, and solids are three-dimensional. Fractals are objects with fractional dimensions. This concept appears at first sight to be nonsense. An object with a fractional dimension of 1.5, for example, would be more than a line but somehow less than a plane. Nevertheless, such things are not only thought to exist, but such geometries are central to chaos theory. One example of such a geometry, although it is not chaotic, is the Koch snowflake.

The Koch snowflake starts as an equilateral triangle. A one-third scale equilateral triangle is added to each side. A one-third scale triangle (of the new, smaller triangle) is then added to each side of the resulting figure. This process is continued ad infinitum as illustrated in figure 4.

http://www.airpower.maxwell.af.mil/airchronicles/apj/apj94/nic4.jpg

The perimeter of this shape has several unique features. First, although it is a single, continuous loop that does not intersect itself and that circumscribes a finite area, its length is infinite. Second, Benoit Mandelbrot calculated that the dimension of the perimeter of the Koch snowflake is 1.26.11 This means that the perimeter is between a line and a plane. Third, the shape of the perimeter of a Koch snowflake is self-scaling. That is, the perimeter would look the same whether you looked at it with the naked eye or with a powerful microscope.

These geometries are pertinent to chaos because strange attractors are fractal. Strange attractors, like the Koch snowflake, are infinite curves that never intersect within a finite area or volume. If a system is chaotic, it will have a strange attractor and the Poincar?© map will show fractal characteristics. That is, the Poincar?© map will remain similar regardless of scale.  Thus, Poincar?© maps can be used to determine if a system is chaotic by visually depicting the nature of the attractor. The dimension of the attractor can also be calculated. If an attractor's dimension is not an integer, then the system is chaotic.

Implications of the Presence of Chaos in Warfare Previous work examined historical data associated with the grand strategic, strategic, and operational levels of war. That work showed that war is chaotic on all of these levels. If war is chaotic, then it must have the characteristics of a chaotic system. We will now describe some of the characteristics of chaotic systems and define what they mean in the context of warfare.

Computer Simulation Can Enhance Understanding

Computer numerical modeling or simulation has greatly increased our understanding of physical chaotic systems. The reason for this is that the equations that govern chaotic systems are nonlinear and therefore are generally not analytically soluble. Chaos theory, however, cannot be used by itself to derive a theory of warfare. As with any other theory that describes a phenomenon, a theory of warfare must be based upon observation, hypothesis, and testing. Specifically, development of a model of warfare would require the development of the structure of the model, the determination of the number and type of variables, and the determination of the form of the equations. In addition, system parameters and control factors, as well as sources for noise, would have to be identified. This is a very difficult task for any particular situation that is complicated by the possibility that different models might apply for different antagonists.

Chaos theory can help us by suggesting ways to develop our model and ways to use the model once it is developed. For example, observation of a chaotic system can be used to determine the dimension of the system. The number of variables needed to describe the system must at least equal the dimension of the system. Therefore, chaos theory can be used to define the minimum number of variables required in our computer model. Chaos theory also suggests that computer models of warfare must contain some nonlinear relationships between system variables so that the computer model is chaotic and thus reflects the chaotic nature of warfare. This may actually prove to be advantageous since the fractal nature of chaotic systems may allow relatively small and simple war games to accurately simulate warfare. Realistic war games that could be run on a desktop computer would have significant educational and operational advantages. Finally, the rate of information loss can be calculated for a chaotic system. This quantity is related to how far into the future predictions can reasonably be made.

The ways in which computers have been used to understand chaotic behavior in physical systems also suggest ways to use the computer to model warfare. For example, although chaos theory explains some aspect of the weather, the reader has probably noted that weather forecasting has not become perfect. This criticism, however, misses one of the most important contributions that chaos theory has made to weather prediction--chaos has given weather forecasters a means to determine if their forecasts are likely to be accurate. Chaotic systems are highly dependent upon initial conditions but they are not always equally so. If a chaotic system is in a portion of its phase space where the initial conditions are critical, then uncertainty in determining the initial conditions makes a large number of outcomes possible. If a chaotic system is in a region of its phase space where the initial conditions are not critical, then only one outcome (prediction) is likely. In practice, weather forecasters use this behavior by inputting small changes in initial conditions into their model. If the small changes produce small variations in the prediction, they have shown that the system is in a portion of phase space where the initial conditions are not critical and their prediction is likely to be true. If the minor changes in initial conditions produce large deviations in future behavior, forecasters know that their prediction is likely to be in error.

The same approach could be taken to understand when predictions in warfare are likely to be accurate. This in itself would be a valuable contribution of computer simulation to understanding warfare. There are, however, two additional reasons why this approach may be even more applicable to warfare than it is to weather. First, unlike weather forecasters, we have some ability to change the initial conditions. Specifically, if we find ourselves in a region of great uncertainty, we could determine which conditions would have to be changed to move the system to a position where the outcome was predictable and desirable. The quantity and type of forces are examples of initial conditions that we might be able to change. Second, we could use our model to determine which initial conditions and which variables had the most profound effect on our predictions. This would aid in identifying centers of gravity (COG) and information that we needed to know precisely. That is, it would tell us where to concentrate our attack and what intelligence information was most critical.

[Cain]

Chaotic Systems Are Nonlinear

All chaotic systems are nonlinear. Among other things, nonlinearity means that a small effort can have a disproportionate effect. If warfare is chaotic, then chaos theory suggests COGs may be found where there is a nonlinear process in the enemy's system. In fact, nonlinearity is implicit in the concept of a COG. Because you can't predict future behavior of a chaotic system based on initial conditions, chaos theory suggests that the campaign planner should concentrate on processes in an enemy system rather than data on its current condition. It also suggests that identification of nonlinear processes is an essential ingredient in understanding warfare and being able to manipulate the outcome with the least effort. The following paragraphs will discuss some of the many sources for nonlinearity in warfare.

Feedback loops are one process that can introduce nonlinear effects in many systems. A feedback loop that is important to the air campaign is the feedback that attrition rates give to an air commander. High attrition rates could force a commander to change his tactics. For example, the loss rates of 16 percent experienced by the US in the daylight bombing raids over Schweinfurt were enough to stop the bombing raids for four months until a long-range fighter was developed. Col John A. Warden used this and other historical examples to argue that the maximum acceptable rate was about 10 percent.  He continued, however, by pointing out that the effect of one mission with a 10 percent attrition rate and nine missions with negligible casualties was much greater than a steady 1 percent attrition rate over 10 missions. In a linear system there would be no difference between the two--the additive effects would be the same. The fact that there is a difference shows that the feedback is nonlinear. When Warden suggested that massing for a few devastating blows is more effective than many minor blows, he described how to exploit the nonlinearity in the system.

A second source for nonlinearity in warfare is the psychology associated with interpreting enemy actions. This nonlinearity caused Clausewitz to state, "Thus, then, in strategy everything is very simple, but not on that account very easy."  He later amplified by saying that while maneuvers such as a flanking movement are simple in concept, they are difficult to actually accomplish because there is always the danger of what the enemy might be doing. In this environment, small actions on the part of the enemy often assume larger significance in a commander's mind than they deserve. According to B. H. Liddell Hart, this nonlinear effect occurred in World War I before the first Battle of the Marne.  The Germans, aware of a possible seam in their dispositions, had been ordered to retreat if the British Army advanced over the Marne. As it happened, a British division sent out a reconnaissance patrol. The Germans, misinterpreting this as a general advance, retreated when the way lay open for victory.

A third source for nonlinearity in warfare is that there are a number of processes within warfare that appear to be inherently nonlinear. The role of mass is an important example. Warden showed that for air power, losses vary disproportionately with the ratio of the forces involved.16 In 1944, for example, 287 American aircraft attacked a target defended by 207 German fighters. The Americans lost 34 aircraft. A month later, when 1,641 American aircraft were opposed by 250 German fighters, America lost 21 aircraft--a lower percentage and a lower absolute number.

A fourth source of nonlinearity in warfare is Clausewitzian friction.  Basically, there will be events in war, perhaps as a result of chance, that have an effect out of all proportion to their apparent importance. This is an exceedingly difficult form of nonlinearity to anticipate, but it can be taken advantage of once it happens. The German doctrine of Auftragstaktik, which allowed initiative on the part of junior commanders, was designed to do precisely this.

Finally, the process of decision making itself can be a source for nonlinearity. Sometimes the decision is clear-cut. Often, however, the decision can depend upon relatively minor circumstances at the time. One source suggests that the steam engine lost out to the gasoline internal combustion engine largely as a result of an outbreak of hoof-and-mouth disease.  Because of this outbreak, many horse troughs, which steam engines had used to top off their water supply, were removed. Once the decision is made, it is often irreversible because of the drive for standardization. Any major decision, including those made in wartime, can be nonlinearly based on such relatively minor factors.

Fractal Geometries Apply

If warfare is chaotic, then aspects of it must be fractal. This has implications for the analysis of an enemy system. First, the attractor for a chaotic system is fractal and so is infinitely complex. Therefore, efforts to analyze every aspect of an enemy's system are bound to be in vain as there will always be some finer level to analyze. Second, behaviors at the tactical, operational, and strategic levels are linked. If a technique is successful at one level, we can expect it to be successful at all levels. This suggests that we should, when possible, try out strategies on a small scale when the consequences of losing are inconsequential. It also suggests that analysis techniques that are useful on one level may be useful on others. An example of this is the observe-orient-decide-act (OODA) loop that was originally proposed for tactical level fighter combat.  The OODA loop, however, has since been applied successfully to operational level concepts such as information dominance. Third, if the small scale is similar in behavior to the large scale, then we can use observation of the small scale to predict the behavior of the large scale. For example, Adm Isoroku Yamamoto was fond of playing Shogi. In his biography of Admiral Yamamoto,  Hiroyuki Agawa noted that Yamamoto's style of playing this game was to risk everything on a bold, early stroke. If that failed, he would often lose the game. Agawa suggests that this philosophy was behind the way in which Admiral Yamamoto planned his large campaigns such as Pearl Harbor and Midway. The fractal nature of war may also have implications for the way we should organize for war. Sun Tzu implied a fractal nature of war when he said, "Generally, management of many is the same as management of few."  This indicates that he thought that the principles of organizing to fight were essentially the same regardless of the scale of the fight. Some principles such as span of control appear to be similar regardless of organizational level. Although research on the implications of chaos for organizational structures has started, conclusions are far from certain.

Multiple Attractors are Possible

Multiple attractors are possible in a chaotic system. This statement means that chaotic systems can have multiple quasi-stable states. The earth's climate is a good example of this sort of behavior. Our current climate appears to be relatively stable. There is some variation in the climate, but it falls within a general range for a number of years. On the other hand, we know that the earth's climate was significantly different during the ice ages, when it fell within a very different range for a long period. Our current climate and the ice age climate are both quasi-stable states for the earth's climate. The causes of changing climates for an ice age are still not understood and might be quite insignificant, which further highlights the nonlinearity of chaotic systems.

In an analogous fashion, armed forces can drastically change their organization and means of fighting a war. The People's War of Mao Tse-tung is an example of this. Mao divided the phases of war into different stages. In some stages, his army fought a guerrilla war as small units. Only later, when conditions were right (i.e., the opposing armies had been sufficiently weakened), did he combine his units into a conventional force. If warfare is chaotic, then chaos theory warns us that enemy systems can exist in different states. The implications are that we must be aware of these possible states and, if necessary, be capable of changing our own system's state to counter the enemy strategy. Chaos theory also warns us that the transition from one state to another can be very fast.

Conclusions

,Ä¢ Computer simulation can be used to better understand warfare. While chaos theory tells us that warfare will never be completely predictable, it also tells us that simulations could be used to identify COGs.

,Ä¢ Warfare is nonlinear. This implies an extreme sensitivity to initial conditions, which means that the campaign planner should concentrate on processes in an enemy's system. Attacking nonlinear processes promises the most effect for the least effort. There are several sources for nonlinearity in warfare.

,Ä¢ Fractal geometries apply. This suggests that analytical techniques and participant behaviors should be translated to the various levels of war.

,Ä¢ Multiple attractors are possible, which suggests a way of viewing transitions from conventional war to guerrilla war and vice versa.

[hunter s.durden]

I can see what they think the value in this might be, but computers don't measure heart, will, and intelligence.
It's kinda like these guys are measuring butterfly wings.

A simulation may tell you some things, but it is my opinion that, in the long run, enough variables will make the bonuses negligable (if at all).

[Triple Zero]

thank Cain, interesting article!

(though, while i appreciate the copy/paste, to anybody about to read this article: better click the link and read it from the webpage itself, so the formulas and images appear correctly)

hunter: it's exactly these arguments you make that go against traditional computer simulation and make a point for chaos theory. don't think they can predict everything, it's just that through chaos theory you can find out where there's a bonus to be found and where you can be absolutely sure it will get drowned in the noise of too much variables, like they said in the article:
> For example, although chaos theory explains some aspect of the weather, the reader
> has probably noted that weather forecasting has not become perfect. This criticism,
> however, misses one of the most important contributions that chaos theory has made
> to weather prediction--chaos has given weather forecasters a means to determine
> if their forecasts are likely to be accurate.
> Chaotic systems are highly dependent upon initial conditions but they are not always
> equally so. If a chaotic system is in a portion of its phase space where the initial
> conditions are critical, then uncertainty in determining the initial conditions
> makes a large number of outcomes possible. If a chaotic system is in a region of its
> phase space where the initial conditions are not critical, then only one outcome
> (prediction) is likely.

my other thoughts on it:

this is a gem:
> All chaotic systems are nonlinear. Among other things, nonlinearity means that a small
> effort can have a disproportionate effect.

pretty much a driving force that makes these things work: "Opensource Warfare", discordianism/O:MF, and other "underground" movements.

> If warfare is chaotic, then chaos theory suggests COGs ["centers of gravity": the "juicy"
> places where you can kick the Machine in the Nads, the bits where it hurts, etc] may be
> found where there is a nonlinear process in the enemy's system. In fact, nonlinearity is
> implicit in the concept of a COG. Because you can't predict future behavior of a chaotic
> system based on initial conditions, chaos theory suggests that the campaign planner should
> concentrate on processes in an enemy system rather than data on its current condition. It
> also suggests that identification of nonlinear processes is an essential ingredient in
> understanding warfare and being able to manipulate the outcome with the least effort.
> The following paragraphs will discuss some of the many sources for nonlinearity in warfare.

this obviously suggests some good ideas for O:MF, places where the Machine is non-linear.
where is this?
the answer is bureaucracy. where one form can initiate a huge chain of events.
it is the basis behind the aneristic delusion.

this is really good stuff (i dunno about the history behind it though, but the story adds up):

> For example, the loss rates of 16 percent experienced by the US in the daylight bombing
> raids over Schweinfurt were enough to stop the bombing raids for four months until a long-
> range fighter was developed. Col John A. Warden used this and other historical examples to
> argue that the maximum acceptable rate was about 10 percent.13 He continued, however, by
> pointing out that the effect of one mission with a 10 percent attrition rate and nine
> missions with negligible casualties was much greater than a steady 1 percent attrition
> rate over 10 missions. In a linear system there would be no difference between the two--
> the additive effects would be the same. The fact that there is a difference shows that the
> feedback is nonlinear. When Warden suggested that massing for a few devastating blows is
> more effective than many minor blows, he described how to exploit the nonlinearity in the system.

another one confirming what Hunter said:
> If warfare is chaotic, then aspects of it must be fractal. This has implications for the
> analysis of an enemy system. First, the attractor for a chaotic system is fractal and so
> is infinitely complex. Therefore, efforts to analyze every aspect of an enemy's system are
> bound to be in vain as there will always be some finer level to analyze.

this is a mistake, btw, just because warfare=chaotic=fractal does NOT imply this:
> Second, behaviors at the tactical, operational, and strategic levels are linked. If a
> technique is successful at one level, we can expect it to be successful at all levels.
> This suggests that we should, when possible, try out strategies on a small scale when
> the consequences of losing are inconsequential. It also suggests that analysis techniques
> that are useful on one level may be useful on others.

because of the non-linearity issues described above, even though things may look similar
on different levels of scale, but they do NOT scale simply like that.
(but maybe i'm overlooking some subtle distinction here)

some more notes and thoughts from the beginning of the article:

> Naturally, this has received some business attention, and at least two firms are
> now using chaos theory to guide their financial advice.

i don't know, but i think it's highly likely that this is snake oil using "ZOMG chaos theory!" as a marketing tool. a prime example of people using numbers to "separate them from their money"

though it's true that a lot of my friends who graduate in the more exact sciences (physics, astronomy, mathematics) but aren't too computer-oriented to have real good software engineering skills, usually find some kind of banking firm where they end up using mathematical models to predict trends and whatnot (sounds like a horrible thing to me, but they say it's interesting).

> Fortunately, as is also illustrated by figure 1, there are bounds to the
> unpredictability of a chaotic system.

this is not always the case. i've seen enough chaotic systems that start out chaotic, and at a certain moment arrive in some part of the phase space that causes a catastrophe to occur and the entire system blows up (to infinity, or to zero, both cases, when translated to the real world, most probably resulting in undesirable effects).
and even for systems that do not display this behaviour, the bounds can still be unknown. so just because "it's chaotic" doesn't mean "it's safe and bounded".

for an example in warfare of this catastrophic behaviour running completely out of bounds, just think about one of the parties feeling "forced" to drop the H-Bomb.

--

good explanation of phase-space and fractal dimension. pretty pictures, too

> Previous work examined historical data associated with the grand strategic,
> strategic, and operational levels of war. That work showed that war is chaotic on
> all of these levels.

one problem i have with measuring fractal dimension to check whether something is chaotic or fractal--but maybe this is because i don't know enough about chaotic systems--is that the most often used fractal dimension measuring techniques (box-counting), hardly ever yield an integer value for the dimension, unless you present it with very strict geometric shapes like a triangle or a square.
if you present it with a photograph or a graph with some bumps in it, it will report a fractal dimension and you could call the "system" "chaotic". this would pretty much suggest that almost all real-world phenomena are chaotic. which may be the case, but not because of this reason.
but perhaps the box-counting technique i learned about is not too accurate way of measuring fractal dimension. i only did a few programming assignments on this, not even an entire course

> We will now describe some of the characteristics of chaotic systems and define
> what they mean in the context of warfare.

this sort of talk is where i can get a littlebit suspicious, and get my barstool ready. but in the end it turned out pretty accurate, well written and interesting..

i think the conclusions at the end are cutting some corners a littlebit too tight though

anyway this gives good food for thought as to how to maximize the effect of our O:MF and other projects

[rygD]

I quit reading right about here:

"Maj David Nicholls, USAF"

There is a book out there called "On Killing".  Haven't had time to finish reading, and as much as I hate the guy I like what he has to say.  I have heard him speak, and this is pretty good too, until he gets a bit too full of himself and his wolf/sheep/sheepdog type bullshit.  I think you should seek out sources that see ground combat, as that is where all the fun is, and that is where everything gets fucked up rather quickly.

[Cain]

Actually, the USAF have produced some pretty brilliant strategic thinkers, Col. Boyd springing immediately to mind.  But I can understand why a ground eye's view may also be nice.

Is that the book by Dave Grossman?

[hunter s.durden]

Yeah, it's about the psychological ramifications of killing.

There's alot about the governments method of making more efficent killers.

[rygD]

I have some questions about you, Hunter.

[hunter s.durden]

If they're about my psychological stability, don't ask.
I'm the tamest motherfucker in here.

[P3nT4gR4m]

I'll vouch for him - Hunter is a lovely person

... but he's a bit of a spoilsport cos he wont come to candy mountain with us

[rygD]

I will start off with "Have you ever been to one of Grossman's lectures or whatever the fuck they are called?"

[hunter s.durden]

 No, I read books. I can't go to lectures because I am poor and live in a cultural and spiritual void (West Virginia). Noone lectures here, because that would be stupid.

[rygD]

Ok, now I can relax...

If you move to regular Virginia you wouldn't suck so bad.

[hunter s.durden]

I do wish I lived in Fairfax...

[rygD]

Shut up, stop stalking me.