Tag strategic studies

How Do You Solve A Problem Like North Korea?

Flight trajectories of North Korean missile tests, May-November 2017. [The Washington Post]

The Democratic People’s Republic of Korea (DPRK) conducted another ballistic missile test yesterday. Following a nearly vertical “lofted trajectory,” the test missile reached a height of 2,800 miles and impacted 620 miles downrange in the Sea of Japan. This performance would give the missile, which the North Koreans have designated the Hwasong-15, a strike range of 8,100 miles, which would include all of the United States.

Appended here is a roundup of TDI posts that address the political and military challenges posed by North Korea. It should be noted that the DPRK nuclear program has been underway for decades and has defied easy resolution thus far. There are no clear options at this stage and each potential solution carries a mix of risk and reward. The DPRK is highly militarized and the danger of catastrophic conflict looms large, with the potential to inflict military and civilian casualties running into the hundreds of thousands or more.

The first set of posts address a potential war on the Korean peninsula.

Chronology of North Korean Missile Development

Insurgency In The DPRK?

U.S. And China: Deterrence And Resolve Over North Korea

Casualty Estimates for a War with North Korea

The CRS Casualty Estimates

The second set of posts look at the DPRK ballistic missile threat and possible counters.

So, What Would Happen If The Norks Did Fire An ICBM At The U.S.?

Aegis, THAAD, Patriots and GBI

Defending Guam From North Korean Ballistic Missiles

The Pros And Cons Of Shooting Down North Korean Ballistic Missile Tests

 

 

Ted Gurr Has Passed Away

Dr. Ted Robert Gurr, noted researcher on political violence and author of Why Men Rebel (1970), passed away on 25 November 2017 at the age of 81. His obituary is here:

http://kraftsussman.com/tribute/details/1516/Ted-Gurr/obituary.html

Wikipedia article on Ted Gurr here: https://en.wikipedia.org/wiki/Ted_Robert_Gurr

 

I never knew him, but his work was a major influence on my work. In the late 1960s, Gurr and Professors Ivo and Rosalind Feierabend led two independent quantitative analysis efforts on the causes of revolutions. Even though they each created their own databases and independently did their own regression analysis of the subject, they came up with similar results. I did have several discussions with Dr. Ivo Feierabend while I was doing some independent work on the causes of revolution.

We have posted about this work before. It is here:

Quote from America’s Modern Wars

Why Are We Still Wondering Why Men (And Women) Rebel?

Why Men Rebel?

Rest in peace Dr. Gurr, and we expect that your work will live on.

Command and Combat Effectiveness: The Case of the British 51st Highland Division

Soldiers of the British 51st Highland Division take cover in bocage in Normandy, 1944. [Daily Record (UK)]

While Trevor Dupuy’s concept of combat effectiveness has been considered controversial by some, he was hardly the only one to observe that throughout history, some military forces have fought more successfully on the battlefield than others. While the sources of victory and defeat in battle remain a fertile, yet understudied topic, there is a growing literature on the topic of military effectiveness in the fields of strategic and security studies.

Anthony King, a professor in War Studies at the University of Warwick, has published an outstanding article in the most recent edition of British Journal of Military History, “Why did 51st Highland Division Fail? A case-study in command and combat effectiveness.” In it, he examined military command and combat effectiveness through the experience of the British 51st Highland Division in the 1944 Normandy Campaign. Most usefully, King developed a definition of military command that clarifies its relationship to combat effectiveness: “The function of a commander is to maximise combat power by defining achievable missions and, then, orchestrating subordinates into a cohesive whole committed to mission accomplishment.”

Defining Military Command

In order to analyze the relationship between command and combat effectiveness, King sought to “define the concept of command and to specify its relationship to management and leadership.” The construct he developed drew upon the work of Peter Drucker, an Austrian-born American business consultant and writer who is considered by many to be “the founder of modern management.” From Drucker, King distilled a definition of the function and process of military command: “command always consists of three elements: mission definition, mission management and mission motivation.”

As King explained, “When command is understood in this way, its connection to combat effectiveness begins to become clear.”

[C]ommand is an institutional solution to an organizational problem; it generates cohesion in a formation. Specifically, by uniting decision-making authority in one person and one role, a large military force is able to unite subordinate units, whose troops are not co-present with each other and who, in most cases, do not know each other. Crucially, the combat effectiveness of a formation, as a formation, is substantially dependent upon the ability of its commander to synchronise its disparate efforts in order to generate collective effects. Skillful command has a galvanising influence on a military force; by orchestrating the activities of subordinate units and motivating troops, command is able to create a level of combat power, which supervenes the capabilities of each of the parts. A well-commanded force has properties, which exceed those of its constituent units, fighting alone.

It is through the orchestration, synchronization, and motivation of effort, King concluded, that “command and combat effectiveness are immediately connected. Command fuses a formation together and increases its determination to fulfil its missions.”

Assessing the Combat Effectiveness of the 51st Division

The rest of King’s article is a detailed assessment of the combat effectiveness of the 51st Highland Division in Normandy in June and July 1944 using this military command construct. Observers at the time noted a decline in the division’s combat performance, which had been graded quite highly in North Africa and Sicily. The one obvious difference was the replacement of Major General Douglas Wimberley with Major General Charles Bullen-Smith in August 1943. After concluding that the 51st Division was no longer battleworthy, the commander of the British 21st Army Group, General Bernard Montgomery personally relieved Bullen-Smith in late July 1944.

In reviewing Bullen-Smith’s performance, King concluded that

Although a number of factors contributed to the struggles of the Highland Division in Normandy, there is little doubt that the shortcomings of its commander, Major General Charles Bullen-Smith, were the critical factor. Charles Bullen-Smith failed to fulfill the three essential functions required of a commander… Bullen-Smith’s inadequacies are highly suggestive of a direct relationship between command and combat effectiveness; they demonstrate how command can augment or undermine combat performance.

King’s approach to military studies once again demonstrates the relevance of multi-disciplinary analysis based on solid historical research. His military command model should prove to be a very useful tool for analyzing the elements of combat effectiveness and assessing combat power. Along with Dr. Jonathan Fennell’s work on measuring morale, among others, it appears that good progress is being made on the study of human factors in combat and military operations, at least in the British academic community (even if Tom Ricks thinks otherwise).

Will Tax Reform Throttle A U.S. Defense Budget Increase?

John Conger recently reported in Defense One that the tax reform initiative championed by the Trump administration and Republican congressional leaders may torpedo an increase in the U.S. defense budget for 2018. Both the House and Senate have passed authorizations approving the Trump administration’s budget request for $574.5 billion in defense spending, which is $52 billion higher than the limit established by the Budget Control Act (BCA). However, the House and Senate also recently passed a concurrent 2018 budget resolution to facilitate passage of a tax reform bill that caps the defense budget at $522 billion as mandated by the BCA.

The House and Senate armed services committees continue to hammer out the terms of the 2018 defense authorization, which includes increases in troop strength and pay. These priorities could crowd out other spending requested by the services to meet strategic and modernization requirements if the budget remains capped. Congress also continues to resist the call by Secretary of Defense James Mattis to close unneeded bases and facilities, which could free spending for other needs. There is also little interest in reforming Defense Department business practices that allegedly waste $125 billion annually.

Congressional Republicans and Democrats were already headed toward a showdown over 2018 BCA limits on defense spending. Even before the tax reform push, several legislators predicted yet another year-long continuing resolution limiting government spending to the previous year’s levels. A bipartisan consensus existed among some armed services committee members that this would constitute “borderline legislative malpractice, particularly for the Department of Defense.”

Despite the ambitious timeline set by President Trump to pass a tax reform bill, the chances of a continuing resolution remain high. It also seems likely that any agreement to increase defense spending will be through the Overseas Contingency Operations budget, which is not subject to the BCA. Many in Congress agree with Democratic Representative Adam Smith that resorting to this approach is “a fiscal sleight of hand [that] would be bad governance and ‘hypocritical.’”

Are tax reform and increased defense spending incompatible? Stay tuned.

TDI Friday Read: Afghanistan

[SIGAR, Quarterly Report to Congress, 30 October 2017, p. 107]

While it is too soon to tell if the Trump Administration’s revised strategy in Afghanistan will make a difference, the recent report by the Special Inspector General for Afghanistan Reconstruction (SIGAR) to Congress documents the continued slow erosion of security in that country. Today’s edition of TDI Friday Read offers a selection of recent posts addressing some of the problems facing the U.S. counterinsurgent and stabilization missions there.

Afghanistan

Meanwhile, In Afghanistan…

We probably need to keep talking about Afghanistan

What will be our plans for Afghanistan?

Stalemate in Afghanistan

Troop Increase in Afghanistan?

Sending More Troops to Afghanistan

Mattis on Afghanistan

Deployed Troop Counts

Disappearing Statistics

 

 

TDI Friday Read: U.S. Airpower

[Image by Geopol Intelligence]

This weekend’s edition of TDI’s Friday Read is a collection of posts on the current state of U.S. airpower by guest contributor Geoffery Clark. The same factors changing the character of land warfare are changing the way conflict will be waged in the air. Clark’s posts highlight some of the way these changes are influencing current and future U.S. airpower plans and concepts.

F-22 vs. F-35: Thoughts On Fifth Generation Fighters

The F-35 Is Not A Fighter

U.S. Armed Forces Vision For Future Air Warfare

The U.S. Navy and U.S. Air Force Debate Future Air Superiority

U.S. Marine Corps Concepts of Operation with the F-35B

The State of U.S. Air Force Air Power

Fifth Generation Deterrence

 

The Effects Of Dispersion On Combat

[The article below is reprinted from the December 1996 edition of The International TNDM Newsletter. A revised version appears in Christopher A. Lawrence, War by Numbers: Understanding Conventional Combat (Potomac Books, 2017), Chapter 13.]

The Effects of Dispersion on Combat
by Christopher A. Lawrence

The TNDM[1] does not play dispersion. But it is clear that dispersion has continued to increase over time, and this must have some effect on combat. This effect was identified by Trevor N. Dupuy in his various writings, starting with the Evolution of Weapons and Warfare. His graph in Understanding War of the battle casualties trends over time is presented here as Figure 1. As dispersion changes over time (dramatically), one would expect the casualties would change over time. I therefore went back to the Land Warfare Database (the 605 engagement version[2]) and proceeded to look at casualties over time and dispersion from every angle that l could.

l eventually realized that l was going to need some better definition of the time periods l was measuring to, as measuring by years scattered the data, measuring by century assembled the data in too gross a manner, and measuring by war left a confusing picture due to the number of small wars with only two or three battles in them in the Land Warfare Database. I eventually defined the wars into 14 categories, so I could fit them onto one readable graph:

To give some idea of how representative the battles listed in the LWDB were for covering the period, I have included a count of the number of battles listed in Michael Clodfelter’s two-volume book Warfare and Armed Conflict, 1618-1991. In the case of WWI, WWII and later, battles tend to be defined as a divisional-level engagement, and there were literally tens of thousands of those.

I then tested my data again looking at the 14 wars that I defined:

  • Average Strength by War (Figure 2)
  • Average Losses by War (Figure 3)
  • Percent Losses Per Day By War (Figure 4)a
  • Average People Per Kilometer By War (Figure 5)
  • Losses per Kilometer of Front by War (Figure 6)
  • Strength and Losses Per Kilometer of Front By War (Figure 7)
  • Ratio of Strength and Losses per Kilometer of Front by War (Figure 8)
  • Ratio of Strength and Loses per Kilometer of Front by Century (Figure 9)

A review of average strengths over time by century and by war showed no surprises (see Figure 2). Up through around 1900, battles were easy to define: they were one- to three-day affairs between clearly defined forces at a locale. The forces had a clear left flank and right flank that was not bounded by other friendly forces. After 1900 (and in a few cases before), warfare was fought on continuous fronts

with a ‘battle’ often being a large multi-corps operation. It is no longer clearly understood what is meant by a battle, as the forces, area covered, and duration can vary widely. For the LWDB, each battle was defined as the analyst wished. ln the case of WWI, there are a lot of very large battles which drive the average battle size up. ln the cases of the WWII, there are a lot of division-level battles, which bring the average down. In the case of the Arab-Israeli Wars, there are nothing but division and brigade-level battles, which bring the average down.

The interesting point to notice is that the average attacker strength in the 16th and 17th century is lower than the average defender strength. Later it is higher. This may be due to anomalies in our data selection.

Average loses by war (see Figure 3) suffers from the same battle definition problem.

Percent losses per day (see Figure 4) is a useful comparison through the end of the 19th Century. After that, the battles get longer and the definition of a duration of the battle is up to the analyst. Note the very dear and definite downward pattern of percent loses per day from the Napoleonic Wars through the Arab-Israeli Wars. Here is a very clear indication of the effects of dispersion. It would appear that from the 1600s to the 1800s the pattern was effectively constant and level, then declines in a very systematic pattern. This partially contradicts Trevor Dupuy’s writing and graphs (see Figure 1). It does appear that after this period of decline that the percent losses per day are being set at a new, much lower plateau. Percent losses per day by war is attached.

Looking at the actual subject of the dispersion of people (measured in people per kilometer of front) remained relatively constant from 1600 through the American Civil War (see Figure 5). Trevor Dupuy defined dispersion as the number of people in a box-like area. Unfortunately, l do not know how to measure that. lean clearly identify the left and right of a unit, but it is more difficult to tell how deep it is Furthermore, density of occupation of this box is far from uniform, with a very forward bias By the same token, fire delivered into this box is also not uniform, with a very forward bias. Therefore, l am quite comfortable measuring dispersion based upon unit frontage, more so than front multiplied by depth.

Note, when comparing the Napoleonic Wars to the American Civil War that the dispersion remains about the same. Yet, if you look at the average casualties (Figure 3) and the average percent casualties per day (Figure 4), it is clear that the rate of casualty accumulation is lower in the American Civil War (this again partially contradicts Dupuy‘s writings). There is no question that with the advent of the Minié ball, allowing for rapid-fire rifled muskets, the ability to deliver accurate firepower increased.

As you will also note, the average people per linear kilometer between WWI and WWII differs by a factor of a little over 1.5 to 1. Yet the actual difference in casualties (see Figure 4) is much greater. While one can just postulate that the difference is the change in dispersion squared (basically Dupuy‘s approach), this does not seem to explain the complete difference, especially the difference between the Napoleonic Wars and the Civil War.

lnstead of discussing dispersion, we should be discussing “casualty reduction efforts.” This basically consists of three elements:

  • Dispersion (D)
  • Increased engagement ranges (R)
  • More individual use of cover and concealment (C&C).

These three factors together result in the reduced chance to hit. They are also partially interrelated, as one cannot make more individual use of cover and concealment unless one is allowed to disperse. So, therefore. The need for cover and concealment increases the desire to disperse and the process of dispersing allows one to use more cover and concealment.

Command and control are integrated into this construct as being something that allows dispersion, and dispersion creates the need for better command control. Therefore, improved command and control in this construct does not operate as a force modifier, but enables a force to disperse.

Intelligence becomes more necessary as the opposing forces use cover and concealment and the ranges of engagement increase. By the same token, improved intelligence allows you to increase the range of engagement and forces the enemy to use better concealment.

This whole construct could be represented by the diagram at the top of the next page.

Now, I may have said the obvious here, but this construct is probably provable in each individual element, and the overall outcome is measurable. Each individual connection between these boxes may also be measurable.

Therefore, to measure the effects of reduced chance to hit, one would need to measure the following formula (assuming these formulae are close to being correct):

(K * ΔD) + (K * ΔC&C) + (K * ΔR) = H

(K * ΔC2) = ΔD

(K * ΔD) = ΔC&C

(K * ΔW) + (K * ΔI) = ΔR

K = a constant
Δ = the change in….. (alias “Delta”)
D = Dispersion
C&C = Cover & Concealment
R = Engagement Range
W = Weapon’s Characteristics
H = the chance to hit
C2 = Command and control
I = Intelligence or ability to observe

Also, certain actions lead to a desire for certain technological and system improvements. This includes the effect of increased dispersion leading to a need for better C2 and increased range leading to a need for better intelligence. I am not sure these are measurable.

I have also shown in the diagram how the enemy impacts upon this. There is also an interrelated mirror image of this construct for the other side.

I am focusing on this because l really want to come up with some means of measuring the effects of a “revolution in warfare.” The last 400 years of human history have given us more revolutionary inventions impacting war than we can reasonably expect to see in the next 100 years. In particular, I would like to measure the impact of increased weapon accuracy, improved intelligence, and improved C2 on combat.

For the purposes of the TNDM, I would very specifically like to work out an attrition multiplier for battles before WWII (and theoretically after WWII) based upon reduced chance to be hit (“dispersion”). For example, Dave Bongard is currently using an attrition multiplier of 4 for his WWI engagements that he is running for the battalion-level validation data base.[3] No one can point to a piece of paper saying this is the value that should be used. Dave picked this value based upon experience and familiarity with the period.

I have also attached Average Loses per Kilometer of Front by War (see Figure 6 above), and a summary chart showing the two on the same chart (see figure 7 above).

The values from these charts are:

The TNDM sets WWII dispersion factor at 3,000 (which l gather translates into 30,000 men per square kilometer). The above data shows a linear dispersion per kilometer of 2,992 men, so this number parallels Dupuy‘s figures.

The final chart I have included is the Ratio of Strength and Losses per Kilometer of Front by War (Figure 8). Each line on the bar graph measures the average ratio of strength over casualties for either the attacker or defender. Being a ratio, unusual outcomes resulted in some really unusually high ratios. I took the liberty of taking out six

data points because they appeared unusually lop-sided. Three of these points are from the English Civil War and were way out of line with everything else. These were the three Scottish battles where you had a small group of mostly sword-armed troops defeating a “modem” army. Also, Walcourt (1689), Front Royal (1862), and Calbritto (1943) were removed. L also have included the same chart, except by century (Figure 9).
Again, one sees a consistency in results in over 300+ years of war, in this case going all the way through WWI, then sees an entirely different pattern with WWII and the Arab-Israeli Wars

A very tentative set of conclusions from all this is:

  1. Dispersion has been relatively constant and driven by factors other than firepower from 1600-1815.
  2. Since the Napoleonic Wars, units have increasingly dispersed (found ways to reduce their chance to be hit) in response to increased lethality of weapons.
  3. As a result of this increased dispersion, casualties in a given space have declined.
  4. The ratio of this decline in casualties over area have been roughly proportional to the strength over an area from 1600 through WWI. Starting with WWII, it appears that people have dispersed faster than weapons lethality, and this trend has continued.
  5. In effect, people dispersed in direct relation to increased firepower from 1815 through 1920, and then after that time dispersed faster than the increase in lethality.
  6. It appears that since WWII, people have gone back to dispersing (reducing their chance to be hit) at the same rate that firepower is increasing.
  7. Effectively, there are four patterns of casualties in modem war:

Period 1 (1600 – 1815): Period of Stability

  • Short battles
  • Short frontages
  • High attrition per day
  • Constant dispersion
  • Dispersion decreasing slightly after late 1700s
  • Attrition decreasing slightly after mid-1700s.

Period 2 (1816 – 1905): Period of Adjustment

  • Longer battles
  • Longer frontages
  • Lower attrition per day
  • Increasing dispersion
  • Dispersion increasing slightly faster than lethality

Period 3 (1912 – 1920): Period of Transition

  • Long Battles
  • Continuous Frontages
  • Lower attrition per day
  • Increasing dispersion
  • Relative lethality per kilometer similar to past, but lower
  • Dispersion increasing slightly faster than lethality

Period 4 (1937 – present): Modern Warfare

  • Long Battles
  • Continuous Frontages
  • Low Attrition per day
  • High dispersion (perhaps constant?)
  • Relatively lethality per kilometer much lower than the past
  • Dispersion increased much faster than lethality going into the period.
  • Dispersion increased at the same rate as lethality within the period.

So the question is whether warfare of the next 50 years will see a new “period of adjustment,” where the rate of dispersion (and other factors) adjusts in direct proportion to increased lethality, or will there be a significant change in the nature of war?

Note that when l use the word “dispersion” above, l often mean “reduced chance to be hit,” which consists of dispersion, increased engagement ranges, and use of cover & concealment.

One of the reasons l wandered into this subject was to see if the TNDM can be used for predicting combat before WWII. l then spent the next few days attempting to find some correlation between dispersion and casualties. Using the data on historical dispersion provided above, l created a mathematical formulation and tested that against the actual historical data points, and could not get any type of fit.

I then locked at the length of battles over time, at one-day battles, and attempted to find a pattern. I could find none. I also looked at other permutations, but did not keep a record of my attempts. I then looked through the work done by Dean Hartley (Oakridge) with the LWDB and called Paul Davis (RAND) to see if there was anyone who had found any correlation between dispersion and casualties, and they had not noted any.

It became clear to me that if there is any such correlation, it is buried so deep in the data that it cannot be found by any casual search. I suspect that I can find a mathematical correlation between weapon lethality, reduced chance to hit (including dispersion), and casualties. This would require some improvement to the data, some systematic measure of weapons lethality, and some serious regression analysis. I unfortunately cannot pursue this at this time.

Finally, for reference, l have attached two charts showing the duration of the battles in the LWDB in days (Figure 10, Duration of Battles Over Time and Figure 11, A Count of the Duration of Battles by War).

NOTES

[1] The Tactical Numerical Deterministic Model, a combat model developed by Trevor Dupuy in 1990-1991 as the follow-up to his Quantified Judgement Model. Dr. James G. Taylor and Jose Perez also contributed to the TNDM’s development.

[2] TDI’s Land Warfare Database (LWDB) was a revised version of a database created by the Historical Evaluation Research Organization (HERO) for the then-U.S. Army Concepts and Analysis Agency (now known as the U.S. Army Center for Army Analysis (CAA)) in 1984. Since the original publication of this article, TDI expanded and revised the data into a suite of databases.

[3] This matter is discussed in Christopher A. Lawrence, “The Second Test of the TNDM Battalion-Level Validations: Predicting Casualties,” The International TNDM Newsletter, April 1997, pp. 40-50.

U.S. Army Updates Draft Multi-Domain Battle Operating Concept

The U.S. Army Training and Doctrine Command has released a revised draft version of its Multi-Domain Battle operating concept, titled “Multi-Domain Battle: Evolution of Combined Arms for the 21st Century, 2025-2040.” Clearly a work in progress, the document is listed as version 1.0, dated October 2017, and as a draft and not for implementation. Sydney J. Freeberg, Jr. has an excellent run-down on the revision at Breaking Defense.

The update is the result of the initial round of work between the U.S. Army and U.S. Air Force to redefine the scope of the multi-domain battlespace for the Joint Force. More work will be needed to refine the concept, but it shows remarkable cooperation in forging a common warfighting perspective between services long-noted for their independent thinking.

On a related note, Albert Palazzo, an Australian defense thinker and one of the early contributors to the Multi-Domain Battle concept, has published the first of a series of articles at The Strategy Bridge offering constructive criticism of the U.S. military’s approach to defining the concept. Palazzo warns that the U.S. may be over-emphasizing countering potential Russian and Chinese capabilities in its efforts and not enough on the broad general implications of long-range fires with global reach.

What difference can it make if those designing Multi-Domain Battle are acting on possibly the wrong threat diagnosis? Designing a solution for a misdiagnosed problem can result in the inculcation of a way of war unsuited for the wars of the future. One is reminded of the French Army during the interwar period. No one can accuse the French of not thinking seriously about war during these years, but, in the doctrine of the methodical battle, they got it wrong and misread the opportunities presented by mechanisation. There were many factors contributing to France’s defeat, but at their core was a misinterpretation of the art of the possible and a singular focus on a particular way of war. Shaping Multi-Domain Battle for the wrong problem may see the United States similarly sow the seeds for a military disaster that is avoidable.

He suggests that it would be wise for U.S. doctrine writers to take a more considered look at potential implications before venturing too far ahead with specific solutions.

Military History and Validation of Combat Models

Soldiers from Britain’s Royal Artillery train in a “virtual world” during Exercise Steel Sabre, 2015 [Sgt Si Longworth RLC (Phot)/MOD]

Military History and Validation of Combat Models

A Presentation at MORS Mini-Symposium on Validation, 16 Oct 1990

By Trevor N. Dupuy

In the operations research community there is some confusion as to the respective meanings of the words “validation” and “verification.” My definition of validation is as follows:

“To confirm or prove that the output or outputs of a model are consistent with the real-world functioning or operation of the process, procedure, or activity which the model is intended to represent or replicate.”

In this paper the word “validation” with respect to combat models is assumed to mean assurance that a model realistically and reliably represents the real world of combat. Or, in other words, given a set of inputs which reflect the anticipated forces and weapons in a combat encounter between two opponents under a given set of circumstances, the model is validated if we can demonstrate that its outputs are likely to represent what would actually happen in a real-world encounter between these forces under those circumstances

Thus, in this paper, the word “validation” has nothing to do with the correctness of computer code, or the apparent internal consistency or logic of relationships of model components, or with the soundness of the mathematical relationships or algorithms, or with satisfying the military judgment or experience of one individual.

True validation of combat models is not possible without testing them against modern historical combat experience. And so, in my opinion, a model is validated only when it will consistently replicate a number of military history battle outcomes in terms of: (a) Success-failure; (b) Attrition rates; and (c) Advance rates.

“Why,” you may ask, “use imprecise, doubtful, and outdated history to validate a modem, scientific process? Field tests, experiments, and field exercises can provide data that is often instrumented, and certainly more reliable than any historical data.”

I recognize that military history is imprecise; it is only an approximate, often biased and/or distorted, and frequently inconsistent reflection of what actually happened on historical battlefields. Records are contradictory. I also recognize that there is an element of chance or randomness in human combat which can produce different results in otherwise apparently identical circumstances. I further recognize that history is retrospective, telling us only what has happened in the past. It cannot predict, if only because combat in the future will be fought with different weapons and equipment than were used in historical combat.

Despite these undoubted problems, military history provides more, and more accurate information about the real world of combat, and how human beings behave and perform under varying circumstances of combat, than is possible to derive or compile from arty other source. Despite some discrepancies, patterns are unmistakable and consistent. There is always a logical explanation for any individual deviations from the patterns. Historical examples that are inconsistent, or that are counter-intuitive, must be viewed with suspicion as possibly being poor or false history.

Of course absolute prediction of a future event is practically impossible, although not necessarily so theoretically. Any speculations which we make from tests or experiments must have some basis in terms of projections from past experience.

Training or demonstration exercises, proving ground tests, field experiments, all lack the one most pervasive and most important component of combat: Fear in a lethal environment. There is no way in peacetime, or non-battlefield, exercises, test, or experiments to be sure that the results are consistent with what would have been the behavior or performance of individuals or units or formations facing hostile firepower on a real battlefield.

We know from the writings of the ancients (for instance Sun Tze—pronounced Sun Dzuh—and Thucydides) that have survived to this day that human nature has not changed since the dawn of history. The human factor the way in which humans respond to stimuli or circumstances is the most important basis for speculation and prediction. What about the “scientific” approach of those who insist that we cart have no confidence in the accuracy or reliability of historical data, that it is therefore unscientific, and therefore that it should be ignored? These people insist that only “scientific” data should be used in modeling.

In fact, every model is based upon fundamental assumptions that are intuitive and unprovable. The first step in the creation of a model is a step away from scientific reality in seeking a basis for an unreal representation of a real phenomenon. I have shown that the unreality is perpetuated when we use other imitations of reality as the basis for representing reality. History is less than perfect, but to ignore it, and to use only data that is bound to be wrong, assures that we will not be able to represent human behavior in real combat.

At the risk of repetition, and even of protesting too much, let me assure you that I am well aware of the shortcomings of military history:

The record which is available to us, which is history, only approximately reflects what actually happened. It is incomplete. It is often biased, it is often distorted. Even when it is accurate, it may be reflecting chance rather than normal processes. It is neither precise nor consistent. But, it provides more, and more accurate, information on the real world of battle than is available from the most thoroughly documented field exercises, proving ground less, or laboratory or field experiments.

Military history is imperfect. At best it reflects the actions and interactions of unpredictable human beings. We must always realize that a single historical example can be misleading for either of two reasons: (1) The data may be inaccurate, or (2) The data may be accurate, but untypical.

Nevertheless, history is indispensable. I repeat that the most pervasive characteristic of combat is fear in a lethal environment. For all of its imperfections, military history and only military history represents what happens under the environmental condition of fear.

Unfortunately, and somewhat unfairly, the reported findings of S.L.A. Marshall about human behavior in combat, which he reported in Men Against Fire, have been recently discounted by revisionist historians who assert that he never could have physically performed the research on which the book’s findings were supposedly based. This has raised doubts about Marshall’s assertion that 85% of infantry soldiers didn’t fire their weapons in combat in World War ll. That dramatic and surprising assertion was first challenged in a New Zealand study which found, on the basis of painstaking interviews, that most New Zealanders fired their weapons in combat. Thus, either Americans were different from New Zealanders, or Marshall was wrong. And now American historians have demonstrated that Marshall had had neither the time nor the opportunity to conduct his battlefield interviews which he claimed were the basis for his findings.

I knew Marshall, moderately well. I was fully as aware of his weaknesses as of his strengths. He was not a historian. I deplored the imprecision and lack of documentation in Men Against Fire. But the revisionist historians have underestimated the shrewd journalistic assessment capability of “SLAM” Marshall. His observations may not have been scientifically precise, but they were generally sound, and his assessment has been shared by many American infantry officers whose judgements l also respect. As to the New Zealand study, how many people will, after the war, admit that they didn’t fire their weapons?

Perhaps most important, however, in judging the assessments of SLAM Marshall, is a recent study by a highly-respected British operations research analyst, David Rowland. Using impeccable OR methods Rowland has demonstrated that Marshall’s assessment of the inefficient performance, or non-performance, of most soldiers in combat was essentially correct. An unclassified version of Rowland’s study, “Assessments of Combat Degradation,” appeared in the June 1986 issue of the Royal United Services Institution Journal.

Rowland was led to his investigations by the fact that soldier performance in field training exercises, using the British version of MILES technology, was not consistent with historical experience. Even after allowances for degradation from theoretical proving ground capability of weapons, defensive rifle fire almost invariably stopped any attack in these field trials. But history showed that attacks were often in fact, usually successful. He therefore began a study in which he made both imaginative and scientific use of historical data from over 100 small unit battles in the Boer War and the two World Wars. He demonstrated that when troops are under fire in actual combat, there is an additional degradation of performance by a factor ranging between 10 and 7. A degradation virtually of an order of magnitude! And this, mind you, on top of a comparable built-in degradation to allow for the difference between field conditions and proving ground conditions.

Not only does Rowland‘s study corroborate SLAM Marshall’s observations, it showed conclusively that field exercises, training competitions and demonstrations, give results so different from real battlefield performance as to render them useless for validation purposes.

Which brings us back to military history. For all of the imprecision, internal contradictions, and inaccuracies inherent in historical data, at worst the deviations are generally far less than a factor of 2.0. This is at least four times more reliable than field test or exercise results.

I do not believe that history can ever repeat itself. The conditions of an event at one time can never be precisely duplicated later. But, bolstered by the Rowland study, I am confident that history paraphrases itself.

If large bodies of historical data are compiled, the patterns are clear and unmistakable, even if slightly fuzzy around the edges. Behavior in accordance with this pattern is therefore typical. As we have already agreed, sometimes behavior can be different from the pattern, but we know that it is untypical, and we can then seek for the reason, which invariably can be discovered.

This permits what l call an actuarial approach to data analysis. We can never predict precisely what will happen under any circumstances. But the actuarial approach, with ample data, provides confidence that the patterns reveal what is to happen under those circumstances, even if the actual results in individual instances vary to some extent from this “norm” (to use the Soviet military historical expression.).

It is relatively easy to take into account the differences in performance resulting from new weapons and equipment. The characteristics of the historical weapons and the current (or projected) weapons can be readily compared, and adjustments made accordingly in the validation procedure.

In the early 1960s an effort was made at SHAPE Headquarters to test the ATLAS Model against World War II data for the German invasion of Western Europe in May, 1940. The first excursion had the Allies ending up on the Rhine River. This was apparently quite reasonable: the Allies substantially outnumbered the Germans, they had more tanks, and their tanks were better. However, despite these Allied advantages, the actual events in 1940 had not matched what ATLAS was now predicting. So the analysts did a little “fine tuning,” (a splendid term for fudging). Alter the so-called adjustments, they tried again, and ran another excursion. This time the model had the Allies ending up in Berlin. The analysts (may the Lord forgive them!) were quite satisfied with the ability of ATLAS to represent modem combat. (Or at least they said so.) Their official conclusion was that the historical example was worthless, since weapons and equipment had changed so much in the preceding 20 years!

As I demonstrated in my book, Options of Command, the problem was that the model was unable to represent the German strategy, or to reflect the relative combat effectiveness of the opponents. The analysts should have reached a different conclusion. ATLAS had failed validation because a model that cannot with reasonable faithfulness and consistency replicate historical combat experience, certainly will be unable validly to reflect current or future combat.

How then, do we account for what l have said about the fuzziness of patterns, and the fact that individual historical examples may not fit the patterns? I will give you my rules of thumb:

  1. The battle outcome should reflect historical success-failure experience about four times out of five.
  2. For attrition rates, the model average of five historical scenarios should be consistent with the historical average within a factor of about 1.5.
  3. For the advance rates, the model average of five historical scenarios should be consistent with the historical average within a factor of about 1.5.

Just as the heavens are the laboratory of the astronomer, so military history is the laboratory of the soldier and the military operations research analyst. The scientific basis for both astronomy and military science is the recording of the movements and relationships of bodies, and then analysis of those movements. (In the one case the bodies are heavenly, in the other they are very terrestrial.)

I repeat: Military history is the laboratory of the soldier. Failure of the analyst to use this laboratory will doom him to live with the scientific equivalent of Ptolomean astronomy, whereas he could use the evidence available in his laboratory to progress to the military science equivalent of Copernican astronomy.

Human Factors In Warfare: Combat Effectiveness

An Israeli tank unit crosses the Sinai, heading for the Suez Canal, during the 1973 Arab-Israeli War [Israeli Government Press Office/HistoryNet]

It has been noted throughout the history of human conflict that some armies have consistently fought more effectively on the battlefield than others. The armies of Sparta in ancient Greece, for example, have come to epitomize the warrior ideal in Western societies. Rome’s legions have acquired a similar legendary reputation. Within armies too, some units are known to be superior combatants than others. The U.S. 1st Infantry Division, the British Expeditionary Force of 1914, Japan’s Special Naval Landing Forces, the U.S. Marine Corps, the German 7th Panzer Division, and the Soviet Guards divisions are among the many superior fighting forces from history.

Trevor Dupuy found empirical substantiation of this in his analysis of historical combat data. He discovered that in 1943-1944 during World War II, after accounting for environmental and operational factors, the German Army consistently performed more effectively in ground combat than the U.S. and British armies. This advantage—measured in terms of casualty exchanges, terrain held or lost, and mission accomplishment—manifested whether the Germans were attacking or defending, or winning or losing. Dupuy observed that the Germans demonstrated an even more marked effectiveness in battle against the Soviet Army throughout the war.

He found the same disparity in battlefield effectiveness in combat data on the 1967 and 1973 Arab-Israeli wars. The Israeli Army performed uniformly better in ground combat over all of the Arab armies it faced in both conflicts, regardless of posture or outcome.

The clear and consistent patterns in the historical data led Dupuy to conclude that superior combat effectiveness on the battlefield was attributable to moral and behavioral (i.e. human) factors. Those factors he believed were the most important contributors to combat effectiveness were:

  • Leadership
  • Training or Experience
  • Morale, which may or may not include
  • Cohesion

Although the influence of human factors on combat effectiveness was identifiable and measurable in the aggregate, Dupuy was skeptical whether all of the individual moral and behavioral intangibles could be discreetly quantified. He thought this particularly true for a set of factors that also contributed to combat effectiveness, but were a blend of human and operational factors. These include:

  • Logistical effectiveness
  • Time and Space
  • Momentum
  • Technical Command, Control, Communications
  • Intelligence
  • Initiative
  • Chance

Dupuy grouped all of these intangibles together into a composite factor he designated as relative combat effectiveness value, or CEV. The CEV, along with environmental and operational factors (Vf), comprise the Circumstantial Variables of Combat, which when multiplied by force strength (S), determines the combat power (P) of a military force in Dupuy’s formulation.

P = S x Vf x CEV

Dupuy did not believe that CEVs were static values. As with human behavior, they vary somewhat from engagement to engagement. He did think that human factors were the most substantial of the combat variables. Therefore any model or theory of combat that failed to account for them would invariably be inaccurate.

NOTES

This post is drawn from Trevor N. Dupuy, Numbers, Predictions and War: Using History to Evaluate Combat Factors and Predict the Outcome of Battles (Indianapolis; New York: The Bobbs-Merrill Co., 1979), Chapters 5, 7 and 9; Trevor N. Dupuy, Understanding War: History and Theory of Combat (New York: Paragon House, 1987), Chapters 8 and 10; and Trevor N. Dupuy, “The Fundamental Information Base for Modeling Human Behavior in Combat, ” presented at the Military Operations Research Society (MORS) Mini-Symposium, “Human Behavior and Performance as Essential Ingredients in Realistic Modeling of Combat – MORIMOC II,” 22-24 February 1989, Center for Naval Analyses, Alexandria, Virginia.