Tag Analysis

Attrition In Future Land Combat

Soldiers with Battery C, 1st Battalion, 82nd Field Artillery Regiment, 1st Brigade Combat Team, 1st Cavalry Division maneuver their Paladins through Hohenfels Training Area, Oct. 26. Photo Credit: Capt. John Farmer, 1st Brigade Combat Team, 1st Cav

[This post was originally published on June 9, 2017]

Last autumn, U.S. Army Chief of Staff General Mark Milley asserted that “we are on the cusp of a fundamental change in the character of warfare, and specifically ground warfare. It will be highly lethal, very highly lethal, unlike anything our Army has experienced, at least since World War II.” He made these comments while describing the Army’s evolving Multi-Domain Battle concept for waging future combat against peer or near-peer adversaries.

How lethal will combat on future battlefields be? Forecasting the future is, of course, an undertaking fraught with uncertainties. Milley’s comments undoubtedly reflect the Army’s best guesses about the likely impact of new weapons systems of greater lethality and accuracy, as well as improved capabilities for acquiring targets. Many observers have been closely watching the use of such weapons on the battlefield in the Ukraine. The spectacular success of the Zelenopillya rocket strike in 2014 was a convincing display of the lethality of long-range precision strike capabilities.

It is possible that ground combat attrition in the future between peer or near-peer combatants may be comparable to the U.S. experience in World War II (although there were considerable differences between the experiences of the various belligerents). Combat losses could be heavier. It certainly seems likely that they would be higher than those experienced by U.S. forces in recent counterinsurgency operations.

Unfortunately, the U.S. Defense Department has demonstrated a tenuous understanding of the phenomenon of combat attrition. Despite wildly inaccurate estimates for combat losses in the 1991 Gulf War, only modest effort has been made since then to improve understanding of the relationship between combat and casualties. The U.S. Army currently does not have either an approved tool or a formal methodology for casualty estimation.

Historical Trends in Combat Attrition

Trevor Dupuy did a great deal of historical research on attrition in combat. He found several trends that had strong enough empirical backing that he deemed them to be verities. He detailed his conclusions in Understanding War: History and Theory of Combat (1987) and Attrition: Forecasting Battle Casualties and Equipment Losses in Modern War (1995).

Dupuy documented a clear relationship over time between increasing weapon lethality, greater battlefield dispersion, and declining casualty rates in conventional combat. Even as weapons became more lethal, greater dispersal in frontage and depth among ground forces led daily personnel loss rates in battle to decrease.

The average daily battle casualty rate in combat has been declining since 1600 as a consequence. Since battlefield weapons continue to increase in lethality and troops continue to disperse in response, it seems logical to presume the trend in loss rates continues to decline, although this may not necessarily be the case. There were two instances in the 19th century where daily battle casualty rates increased—during the Napoleonic Wars and the American Civil War—before declining again. Dupuy noted that combat casualty rates in the 1973 Arab-Israeli War remained roughly the same as those in World War II (1939-45), almost thirty years earlier. Further research is needed to determine if average daily personnel loss rates have indeed continued to decrease into the 21st century.

Dupuy also discovered that, as with battle outcomes, casualty rates are influenced by the circumstantial variables of combat. Posture, weather, terrain, season, time of day, surprise, fatigue, level of fortification, and “all out” efforts affect loss rates. (The combat loss rates of armored vehicles, artillery, and other other weapons systems are directly related to personnel loss rates, and are affected by many of the same factors.) Consequently, yet counterintuitively, he could find no direct relationship between numerical force ratios and combat casualty rates. Combat power ratios which take into account the circumstances of combat do affect casualty rates; forces with greater combat power inflict higher rates of casualties than less powerful forces do.

Winning forces suffer lower rates of combat losses than losing forces do, whether attacking or defending. (It should be noted that there is a difference between combat loss rates and numbers of losses. Depending on the circumstances, Dupuy found that the numerical losses of the winning and losing forces may often be similar, even if the winner’s casualty rate is lower.)

Dupuy’s research confirmed the fact that the combat loss rates of smaller forces is higher than that of larger forces. This is in part due to the fact that smaller forces have a larger proportion of their troops exposed to enemy weapons; combat casualties tend to concentrated in the forward-deployed combat and combat support elements. Dupuy also surmised that Prussian military theorist Carl von Clausewitz’s concept of friction plays a role in this. The complexity of interactions between increasing numbers of troops and weapons simply diminishes the lethal effects of weapons systems on real world battlefields.

Somewhat unsurprisingly, higher quality forces (that better manage the ambient effects of friction in combat) inflict casualties at higher rates than those with less effectiveness. This can be seen clearly in the disparities in casualties between German and Soviet forces during World War II, Israeli and Arab combatants in 1973, and U.S. and coalition forces and the Iraqis in 1991 and 2003.

Combat Loss Rates on Future Battlefields

What do Dupuy’s combat attrition verities imply about casualties in future battles? As a baseline, he found that the average daily combat casualty rate in Western Europe during World War II for divisional-level engagements was 1-2% for winning forces and 2-3% for losing ones. For a divisional slice of 15,000 personnel, this meant daily combat losses of 150-450 troops, concentrated in the maneuver battalions (The ratio of wounded to killed in modern combat has been found to be consistently about 4:1. 20% are killed in action; the other 80% include mortally wounded/wounded in action, missing, and captured).

It seems reasonable to conclude that future battlefields will be less densely occupied. Brigades, battalions, and companies will be fighting in spaces formerly filled with armies, corps, and divisions. Fewer troops mean fewer overall casualties, but the daily casualty rates of individual smaller units may well exceed those of WWII divisions. Smaller forces experience significant variation in daily casualties, but Dupuy established average daily rates for them as shown below.

For example, based on Dupuy’s methodology, the average daily loss rate unmodified by combat variables for brigade combat teams would be 1.8% per day, battalions would be 8% per day, and companies 21% per day. For a brigade of 4,500, that would result in 81 battle casualties per day, a battalion of 800 would suffer 64 casualties, and a company of 120 would lose 27 troops. These rates would then be modified by the circumstances of each particular engagement.

Several factors could push daily casualty rates down. Milley envisions that U.S. units engaged in an anti-access/area denial environment will be constantly moving. A low density, highly mobile battlefield with fluid lines would be expected to reduce casualty rates for all sides. High mobility might also limit opportunities for infantry assaults and close quarters combat. The high operational tempo will be exhausting, according to Milley. This could also lower loss rates, as the casualty inflicting capabilities of combat units decline with each successive day in battle.

It is not immediately clear how cyberwarfare and information operations might influence casualty rates. One combat variable they might directly impact would be surprise. Dupuy identified surprise as one of the most potent combat power multipliers. A surprised force suffers a higher casualty rate and surprisers enjoy lower loss rates. Russian combat doctrine emphasizes using cyber and information operations to achieve it and forces with degraded situational awareness are highly susceptible to it. As Zelenopillya demonstrated, surprise attacks with modern weapons can be devastating.

Some factors could push combat loss rates up. Long-range precision weapons could expose greater numbers of troops to enemy fires, which would drive casualties up among combat support and combat service support elements. Casualty rates historically drop during night time hours, although modern night-vision technology and persistent drone reconnaissance might will likely enable continuous night and day battle, which could result in higher losses.

Drawing solid conclusions is difficult but the question of future battlefield attrition is far too important not to be studied with greater urgency. Current policy debates over whether or not the draft should be reinstated and the proper size and distribution of manpower in active and reserve components of the Army hinge on getting this right. The trend away from mass on the battlefield means that there may not be a large margin of error should future combat forces suffer higher combat casualties than expected.

First World War Digital Resources

Informal portrait of Charles E. W. Bean working on official files in his Victoria Barracks office during the writing of the Official History of Australia in the War of 1914-1918. The files on his desk are probably the Operations Files, 1914-18 War, that were prepared by the army between 1925 and 1930 and are now held by the Australian War Memorial as AWM 26. Courtesy of the Australian War Memorial. [Defence in Depth]

Chris and I have both taken to task the highly problematic state of affairs with regard to military record-keeping in the digital era. So it is only fair to also highlight the strengths of the Internet for historical research, one of which is the increasing availability of digitized archival  holdings, documents, and sources.

Although the posts are a couple of years old now, Dr. Robert T. Foley of the Defence Studies Department at King’s College London has provided a wonderful compilation of  links to digital holdings and resources documenting the experiences of many of the many  belligerents in the First World War. The links include digitized archival holdings and electronic copies of often hard-to-find official histories of ground, sea, and air operations.

Digital First World War Resources: Online Archival Sources

Digital First World War Resources: Online Official Histories — The War on Land

Digital First World War Resources: Online Official Histories — The War at Sea and in the Air

For TDI, the availability of such materials greatly broadens potential sources for research on historical combat. For example, TDI made use of German regional archival holdings for to compile data on the use of chemical weapons in urban environments from the separate state armies that formed part of the Imperial German Army in the First World War. Although much of the German Army’s historical archives were destroyed by Allied bombing at the end of the Second World War, a great deal of material survived in regional state archives and in other places, as Dr. Foley shows. Access to the highly detailed official histories is another boon for such research.

The Digital Era hints at unprecedented access to historical resources and more materials are being added all the time. Current historians should benefit greatly. Future historians, alas, are not as likely to be so fortunate when it comes time to craft histories of the the current era.

TDI Friday Read: How Do We Know What We Know About War?

The late, great Carl Sagan.

Today’s edition of TDI Friday Read asks the question, how do we know if the theories and concepts we use to understand and explain war and warfare accurately depict reality? There is certainly no shortage of explanatory theories available, starting with Sun Tzu in the 6th century BCE and running to the present. As I have mentioned before, all combat models and simulations are theories about how combat works. Military doctrine is also a functional theory of warfare. But how do we know if any of these theories are actually true?

Well, one simple way to find out if a particular theory is valid is to use it to predict the outcome of the phenomenon it purports to explain. Testing theory through prediction is a fundamental aspect of the philosophy of science. If a theory is accurate, it should be able to produce a reasonable accurate prediction of future behavior.

In his 2016 article, “Can We Predict Politics? Toward What End?” Michael D. Ward, a Professor of Political Science at Duke University, made a case for a robust effort for using prediction as a way of evaluating the thicket of theory populating security and strategic studies. Dropping invalid theories and concepts is important, but there is probably more value in figuring out how and why they are wrong.

Screw Theory! We Need More Prediction in Security Studies!

Trevor Dupuy and TDI publicly put their theories to the test in the form of combat casualty estimates for the 1991 Gulf Way, the U.S. intervention in Bosnia, and the Iraqi insurgency. How well did they do?

Predictions

Dupuy himself argued passionately for independent testing of combat models against real-world data, a process known as validation. This is actually seldom done in the U.S. military operations research community.

Military History and Validation of Combat Models

However, TDI has done validation testing of Dupuy’s Quantified Judgement Model (QJM) and Tactical Numerical Deterministic Model (TNDM). The results are available for all to judge.

Validating Trevor Dupuy’s Combat Models

I will conclude this post on a dissenting note. Trevor Dupuy spent decades arguing for more rigor in the development of combat models and analysis, with only modest success. In fact, he encountered significant skepticism and resistance to his ideas and proposals. To this day, the U.S. Defense Department seems relatively uninterested in evidence-based research on this subject. Why?

David Wilkinson, Editor-in-Chief of the Oxford Review, wrote a fascinating blog post looking at why practitioners seem to have little actual interest in evidence-based practice.

Why evidence-based practice probably isn’t worth it…

His argument:

The problem with evidence based practice is that outside of areas like health care and aviation/technology is that most people in organisations don’t care about having research evidence for almost anything they do. That doesn’t mean they are not interesting in research but they are just not that interested in using the research to change how they do things – period.

His explanation for why this is and what might be done to remedy the situation is quite interesting.

Happy Holidays to all!

TDI Friday Read: The Lanchester Equations

Frederick W. Lanchester (1868-1946), British engineer and author of the Lanchester combat attrition equations. [Lanchester.com]

Today’s edition of TDI Friday Read addresses the Lanchester equations and their use in U.S. combat models and simulations. In 1916, British engineer Frederick W. Lanchester published a set of calculations he had derived for determining the results of attrition in combat. Lanchester intended them to be applied as an abstract conceptualization of aerial combat, stating that he did not believe they were applicable to ground combat.

Due to their elegant simplicity, U.S. military operations researchers nevertheless began incorporating the Lanchester equations into their land warfare computer combat models and simulations in the 1950s and 60s. The equations are the basis for many models and simulations used throughout the U.S. defense community today.

The problem with using Lanchester’s equations is that, despite numerous efforts, no one has been able to demonstrate that they accurately represent real-world combat.

Lanchester equations have been weighed….

Really…..Lanchester?

Trevor Dupuy was critical of combat models based on the Lanchester equations because they cannot account for the role behavioral and moral (i.e. human) factors play in combat.

Human Factors In Warfare: Interaction Of Variable Factors

He was also critical of models and simulations that had not been tested to see whether they could reliably represent real-world combat experience. In the modeling and simulation community, this sort of testing is known as validation.

Military History and Validation of Combat Models

The use of unvalidated concepts, like the Lanchester equations, and unvalidated combat models and simulations persists. Critics have dubbed this the “base of sand” problem, and it continues to affect not only models and simulations, but all abstract theories of combat, including those represented in military doctrine.

https://dupuyinstitute.dreamhosters.com/2017/04/10/wargaming-multi-domain-battle-the-base-of-sand-problem/

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).

TDI Friday Read: How Many Troops Are Needed To Defeat An Insurgency?

A paratrooper from the French Foreign Legion (1er REP) with a captured fellagha during the Algerian War (1954-1962). [Via Pinterest]

Today’s edition of TDI Friday Read is a compilation of posts addressing the question of manpower and counterinsurgency. The first four posts summarize research on the question undertaken during the first decade of the 21st century, while the Afghan and Iraqi insurgencies were in full bloom. Despite different research questions and analytical methodologies, each of the studies concluded that there is a relationship between counterinsurgent manpower and counterinsurgency outcomes.

The fifth post addresses the U.S. Army’s lack of a formal methodology for calculating manpower requirements for counterinsurgencies and contingency operations.

Force Ratios and Counterinsurgency

Force Ratios and Counterinsurgency II

Force Ratios and Counterinsurgency III

Force Ratios and Counterinsurgency IV

https://dupuyinstitute.dreamhosters.com/2016/06/29/has-the-army-given-up-on-counterinsurgency-research-again/

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

 

 

Validating Trevor Dupuy’s Combat Models

[The article below is reprinted from Winter 2010 edition of The International TNDM Newsletter.]

A Summation of QJM/TNDM Validation Efforts

By Christopher A. Lawrence

There have been six or seven different validation tests conducted of the QJM (Quantified Judgment Model) and the TNDM (Tactical Numerical Deterministic Model). As the changes to these two models are evolutionary in nature but do not fundamentally change the nature of the models, the whole series of validation tests across both models is worth noting. To date, this is the only model we are aware of that has been through multiple validations. We are not aware of any DOD [Department of Defense] combat model that has undergone more than one validation effort. Most of the DOD combat models in use have not undergone any validation.

The Two Original Validations of the QJM

After its initial development using a 60-engagement WWII database, the QJM was tested in 1973 by application of its relationships and factors to a validation database of 21 World War II engagements in Northwest Europe in 1944 and 1945. The original model proved to be 95% accurate in explaining the outcomes of these additional engagements. Overall accuracy in predicting the results of the 81 engagements in the developmental and validation databases was 93%.[1]

During the same period the QJM was converted from a static model that only predicted success or failure to one capable of also predicting attrition and movement. This was accomplished by adding variables and modifying factor values. The original QJM structure was not changed in this process. The addition of movement and attrition as outputs allowed the model to be used dynamically in successive “snapshot” iterations of the same engagement.

From 1973 to 1979 the QJM’s formulae, procedures, and variable factor values were tested against the results of all of the 52 significant engagements of the 1967 and 1973 Arab-Israeli Wars (19 from the former, 33 from the latter). The QJM was able to replicate all of those engagements with an accuracy of more than 90%?[2]

In 1979 the improved QJM was revalidated by application to 66 engagements. These included 35 from the original 81 engagements (the “development database”), and 31 new engagements. The new engagements included five from World War II and 26 from the 1973 Middle East War. This new validation test considered four outputs: success/failure, movement rates, personnel casualties, and tank losses. The QJM predicted success/failure correctly for about 85% of the engagements. It predicted movement rates with an error of 15% and personnel attrition with an error of 40% or less. While the error rate for tank losses was about 80%, it was discovered that the model consistently underestimated tank losses because input data included all kinds of armored vehicles, but output data losses included only numbers of tanks.[3]

This completed the original validations efforts of the QJM. The data used for the validations, and parts of the results of the validation, were published, but no formal validation report was issued. The validation was conducted in-house by Colonel Dupuy’s organization, HERO [Historical Evaluation Research Organization]. The data used were mostly from division-level engagements, although they included some corps- and brigade-level actions. We count these as two separate validation efforts.

The Development of the TNDM and Desert Storm

In 1990 Col. Dupuy, with the collaborative assistance of Dr. James G. Taylor (author of Lanchester Models of Warfare [vol. 1] [vol. 2], published by the Operations Research Society of America, Arlington, Virginia, in 1983) introduced a significant modification: the representation of the passage of time in the model. Instead of resorting to successive “snapshots,” the introduction of Taylor’s differential equation technique permitted the representation of time as a continuous flow. While this new approach required substantial changes to the software, the relationship of the model to historical experience was unchanged.[4] This revision of the model also included the substitution of formulae for some of its tables so that there was a continuous flow of values across the individual points in the tables. It also included some adjustment to the values and tables in the QJM. Finally, it incorporated a revised OLI [Operational Lethality Index] calculation methodology for modem armor (mobile fighting machines) to take into account all the factors that influence modern tank warfare.[5] The model was reprogrammed in Turbo PASCAL (the original had been written in BASIC). The new model was called the TNDM (Tactical Numerical Deterministic Model).

Building on its foundation of historical validation and proven attrition methodology, in December 1990, HERO used the TNDM to predict the outcome of, and losses from, the impending Operation DESERT STORM.[6] It was the most accurate (lowest) public estimate of U.S. war casualties provided before the war. It differed from most other public estimates by an order of magnitude.

Also, in 1990, Trevor Dupuy published an abbreviated form of the TNDM in the book Attrition: Forecasting Battle Casualties and Equipment Losses in Modern War. A brief validation exercise using 12 battles from 1805 to 1973 was published in this book.[7] This version was used for creation of M-COAT[8] and was also separately tested by a student (Lieutenant Gozel) at the Naval Postgraduate School in 2000.[9] This version did not have the firepower scoring system, and as such neither M-COAT, Lieutenant Gozel’s test, nor Colonel Dupuy’s 12-battle validation included the OLI methodology that is in the primary version of the TNDM.

For counting purposes, I consider the Gulf War the third validation of the model. In the end, for any model, the proof is in the pudding. Can the model be used as a predictive tool or not? If not, then there is probably a fundamental flaw or two in the model. Still the validation of the TNDM was somewhat second-hand, in the sense that the closely-related previous model, the QJM, was validated in the 1970s to 200 World War II and 1967 and 1973 Arab-Israeli War battles, but the TNDM had not been. Clearly, something further needed to be done.

The Battalion-Level Validation of the TNDM

Under the guidance of Christopher A. Lawrence, The Dupuy Institute undertook a battalion-level validation of the TNDM in late 1996. This effort tested the model against 76 engagements from World War I, World War II, and the post-1945 world including Vietnam, the Arab-Israeli Wars, the Falklands War, Angola, Nicaragua, etc. This effort was thoroughly documented in The International TNDM Newsletter.[10] This effort was probably one of the more independent and better-documented validations of a casualty estimation methodology that has ever been conducted to date, in that:

  • The data was independently assembled (assembled for other purposes before the validation) by a number of different historians.
  • There were no calibration runs or adjustments made to the model before the test.
  • The data included a wide range of material from different conflicts and times (from 1918 to 1983).
  • The validation runs were conducted independently (Susan Rich conducted the validation runs, while Christopher A. Lawrence evaluated them).
  • The results of the validation were fully published.
  • The people conducting the validation were independent, in the sense that:

a) there was no contract, management, or agency requesting the validation;
b) none of the validators had previously been involved in designing the model, and had only very limited experience in using it; and
c) the original model designer was not able to oversee or influence the validation.[11]

The validation was not truly independent, as the model tested was a commercial product of The Dupuy Institute, and the person conducting the test was an employee of the Institute. On the other hand, this was an independent effort in the sense that the effort was employee-initiated and not requested or reviewed by the management of the Institute. Furthermore, the results were published.

The TNDM was also given a limited validation test back to its original WWII data around 1997 by Niklas Zetterling of the Swedish War College, who retested the model to about 15 or so Italian campaign engagements. This effort included a complete review of the historical data used for the validation back to their primarily sources, and details were published in The International TNDM Newsletter.[12]

There has been one other effort to correlate outputs from QJM/TNDM-inspired formulae to historical data using the Ardennes and Kursk campaign-level (i.e., division-level) databases.[13] This effort did not use the complete model, but only selective pieces of it, and achieved various degrees of “goodness of fit.” While the model is hypothetically designed for use from squad level to army group level, to date no validation has been attempted below battalion level, or above division level. At this time, the TNDM also needs to be revalidated back to its original WWII and Arab-Israeli War data, as it has evolved since the original validation effort.

The Corps- and Division-level Validations of the TNDM

Having now having done one extensive battalion-level validation of the model and published the results in our newsletters, Volume 1, issues 5 and 6, we were then presented an opportunity in 2006 to conduct two more validations of the model. These are discussed in depth in two articles of this issue of the newsletter.

These validations were again conducted using historical data, 24 days of corps-level combat and 25 cases of division-level combat drawn from the Battle of Kursk during 4-15 July 1943. It was conducted using an independently-researched data collection (although the research was conducted by The Dupuy Institute), using a different person to conduct the model runs (although that person was an employee of the Institute) and using another person to compile the results (also an employee of the Institute). To summarize the results of this validation (the historical figure is listed first followed by the predicted result):

There was one other effort that was done as part of work we did for the Army Medical Department (AMEDD). This is fully explained in our report Casualty Estimation Methodologies Study: The Interim Report dated 25 July 2005. In this case, we tested six different casualty estimation methodologies to 22 cases. These consisted of 12 division-level cases from the Italian Campaign (4 where the attack failed, 4 where the attacker advanced, and 4 Where the defender was penetrated) and 10 cases from the Battle of Kursk (2 cases Where the attack failed, 4 where the attacker advanced and 4 where the defender was penetrated). These 22 cases were randomly selected from our earlier 628 case version of the DLEDB (Division-level Engagement Database; it now has 752 cases). Again, the TNDM performed as well as or better than any of the other casualty estimation methodologies tested. As this validation effort was using the Italian engagements previously used for validation (although some had been revised due to additional research) and three of the Kursk engagements that were later used for our division-level validation, then it is debatable whether one would want to call this a seventh validation effort. Still, it was done as above with one person assembling the historical data and another person conducting the model runs. This effort was conducted a year before the corps and division-level validation conducted above and influenced it to the extent that we chose a higher CEV (Combat Effectiveness Value) for the later validation. A CEV of 2.5 was used for the Soviets for this test, vice the CEV of 3.0 that was used for the later tests.

Summation

The QJM has been validated at least twice. The TNDM has been tested or validated at least four times, once to an upcoming, imminent war, once to battalion-level data from 1918 to 1989, once to division-level data from 1943 and once to corps-level data from 1943. These last four validation efforts have been published and described in depth. The model continues, regardless of which validation is examined, to accurately predict outcomes and make reasonable predictions of advance rates, loss rates and armor loss rates. This is regardless of level of combat (battalion, division or corps), historic period (WWI, WWII or modem), the situation of the combats, or the nationalities involved (American, German, Soviet, Israeli, various Arab armies, etc.). As the QJM, the model was effectively validated to around 200 World War II and 1967 and 1973 Arab-Israeli War battles. As the TNDM, the model was validated to 125 corps-, division-, and battalion-level engagements from 1918 to 1989 and used as a predictive model for the 1991 Gulf War. This is the most extensive and systematic validation effort yet done for any combat model. The model has been tested and re-tested. It has been tested across multiple levels of combat and in a wide range of environments. It has been tested where human factors are lopsided, and where human factors are roughly equal. It has been independently spot-checked several times by others outside of the Institute. It is hard to say what more can be done to establish its validity and accuracy.

NOTES

[1] It is unclear what these percentages, quoted from Dupuy in the TNDM General Theoretical Description, specify. We suspect it is a measurement of the model’s ability to predict winner and loser. No validation report based on this effort was ever published. Also, the validation figures seem to reflect the results after any corrections made to the model based upon these tests. It does appear that the division-level validation was “incremental.” We do not know if the earlier validation tests were tested back to the earlier data, but we have reason to suspect not.

[2] The original QJM validation data was first published in the Combat Data Subscription Service Supplement, vol. 1, no. 3 (Dunn Loring VA: HERO, Summer 1975). (HERO Report #50) That effort used data from 1943 through 1973.

[3] HERO published its QJM validation database in The QJM Data Base (3 volumes) Fairfax VA: HERO, 1985 (HERO Report #100).

[4] The Dupuy Institute, The Tactical Numerical Deterministic Model (TNDM): A General and Theoretical Description, McLean VA: The Dupuy Institute, October 1994.

[5] This had the unfortunate effect of undervaluing WWII-era armor by about 75% relative to other WWII weapons when modeling WWII engagements. This left The Dupuy Institute with the compromise methodology of using the old OLI method for calculating armor (Mobile Fighting Machines) when doing WWII engagements and using the new OLI method for calculating armor when doing modem engagements

[6] Testimony of Col. T. N. Dupuy, USA, Ret, Before the House Armed Services Committee, 13 Dec 1990. The Dupuy Institute File I-30, “Iraqi Invasion of Kuwait.”

[7] Trevor N. Dupuy, Attrition: Forecasting Battle Casualties and Equipment Losses in Modern War (HERO Books, Fairfax, VA, 1990), 123-4.

[8] M-COAT is the Medical Course of Action Tool created by Major Bruce Shahbaz. It is a spreadsheet model based upon the elements of the TNDM provided in Dupuy’s Attrition (op. cit.) It used a scoring system derived from elsewhere in the U.S. Army. As such, it is a simplified form of the TNDM with a different weapon scoring system.

[9] See Gözel, Ramazan. “Fitting Firepower Score Models to the Battle of Kursk Data,” NPGS Thesis. Monterey CA: Naval Postgraduate School.

[10] Lawrence, Christopher A. “Validation of the TNDM at Battalion Level.” The International TNDM Newsletter, vol. 1, no. 2 (October 1996); Bongard, Dave “The 76 Battalion-Level Engagements.” The International TNDM Newsletter, vol. 1, no. 4 (February 1997); Lawrence, Christopher A. “The First Test of the TNDM Battalion-Level Validations: Predicting the Winner” and “The Second Test of the TNDM Battalion-Level Validations: Predicting Casualties,” The International TNDM Newsletter, vol. 1 no. 5 (April 1997); and Lawrence, Christopher A. “Use of Armor in the 76 Battalion-Level Engagements,” and “The Second Test of the Battalion-Level Validation: Predicting Casualties Final Scorecard.” The International TNDM Newsletter, vol. 1, no. 6 (June 1997).

[11] Trevor N. Dupuy passed away in July 1995, and the validation was conducted in 1996 and 1997.

[12] Zetterling, Niklas. “CEV Calculations in Italy, 1943,” The International TNDM Newsletter, vol. 1, no. 6. McLean VA: The Dupuy Institute, June 1997. See also Research Plan, The Dupuy Institute Report E-3, McLean VA: The Dupuy Institute, 7 Oct 1998.

[13] See Gözel, “Fitting Firepower Score Models to the Battle of Kursk Data.”

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.

TDI Friday Read: Mike Spagat’s Economics of Warfare Lectures & Commentaries

Below is an aggregated list of links to Dr. Michael Spagat‘s E3320: Economics of Warfare lecture series at the Royal Holloway University of London, and Chris Lawrence’s commentary on each. Spagat is a professor of economics and the course addresses quantitative research on war.

The aim of the course is to:

Introduce students to the main facts about conflict. Apply theoretical and empirical economic tools to the study of conflict. Give students an appreciation of the main questions at the research frontier in the economic analysis of conflict. Draw some policy conclusions on how the international community should deal with conflict. Study data issues that arise when analysing conflict.
Mike’s Lecture Chris’s Commentary
Economics of Warfare 1 Commentary
Economics of Warfare 2 Commentary
Economics of Warfare 3 Commentary
Economics of Warfare 4 Commentary
Economics of Warfare 5 Commentary
Economics of Warfare 6 Commentary
Economics of Warfare 7 Commentary
Economics of Warfare 8 Commentary
Economics of Warfare 9 Commentary
Economics of Warfare 10 Commentary
Economics of Warfare 11 Commentary 1

Commentary 2
Economics of Warfare 12 Commentary
Economics of Warfare 13 Commentary 1

Commentary 2

Commentary 3
Economics of Warfare 14 Commentary
Economics of Warfare 15 Commentary 1

Commentary 2
Economics of Warfare 16 Commentary
Economics of Warfare 17 Commentary 1

Commentary 2

Commentary 3
Economics of Warfare 18 Commentary
Economics of Warfare 19 Commentary 1

Commentary 2

Commentary 3

Commentary 4
Economics of Warfare 20 Commentary