Tag casualty estimation

Russian Body Count: Update

Map of the reported incident between U.S., Syrian, and Russian forces near Deir Ezzor, Syria on 7 February 2018 [Spiegel Online]

An article by Christoph Reuter in Spiegel Online adds some new details to the story of the incident between U.S., Syrian, and Russian mercenary forces near the Syrian city of Deir Ezzor on 7 February 2018. Based on interviews with witnesses and participants, the article paints a different picture than the one created by previous media reports.

According to Spiegel Online, early on 7 February, a 250-strong force comprised of Syrian tribal militia, Afghan and Iraqi fighters, and troops from the Syrian Army 4th Division attempted to cross from the west bank of the Euphrates River to the east, south of a Kurdish Syrian Defense Forces (SDF) base at Khusham. The Euphrates constitutes a “deconfliction” line established by the United States and Russia separating the forces of Syrian President Bashar al-Assad from those of the U.S.-supported SDF. The Syrian force was detected and U.S. combat forces fired warning shots, which persuaded the Syrians to withdraw.

After dark that evening, the Syrian force, reinforced to about 500 fighters, moved several kilometers north and attempted to cross the Euphrates a second time, this time successfully. As the force advanced through the village of Marrat, it was again spotted and engaged by U.S. air and artillery assets after an alleged 20-30 tank rounds impacted within 500 meters of the SDF headquarters in Khusham. The U.S. employed field artillery, drones, combat helicopters, and AC-130 gunships to devastating effect.

Speigel Online reported that U.S. forces also simultaneously engaged a force of approximately 400 pro-Assad Syrian tribal militia and Shi’a fighters advancing north from the village of Tabiya, south of Khusham. A small contingent of Russian mercenaries, stationed in Tabiya but not supporting the Syrian/Shi’a fighters, was hit by U.S. fire. This second Syrian force, which the U.S. had allowed to remain on the east side of the Euphrates as long as it remained peaceful and small, was allegedly attacked again on 9 February.

According to Spigel Online’s sources, “more than 200 of the attackers died, including around 80 Syrian soldiers with the 4th Division, around 100 Iraqis and Afghans and around 70 tribal fighters, mostly with the al-Baqir militia.” Around 10-20 Russian mercenaries were killed as well, although Russian state media has confirmed only nine deaths.

This account of the fighting and casualty distribution is in stark contrast to the story being reported by Western media, which has alleged tens or hundreds of Russians killed:

[A] completely different version of events has gained traction — disseminated at first by Russian nationalists like Igor “Strelkov” Girkin, and then by others associated with the Wagner unit. According to those accounts, many more Russians had been killed in the battle — 100, 200, 300 or as many as 600. An entire unit, it was said, had been wiped out and the Kremlin wanted to cover it up. Recordings of alleged fighters even popped up apparently confirming these horrendous losses.

It was a version that sounded so plausible that even Western news agencies like Reuters and Bloomberg picked it up. The fact that the government in Moscow at first didn’t want to confirm any deaths and then spoke of five “Russian citizens” killed and later, nebulously, of “dozens of injured,” some of whom had died, only seemed to make the version of events seem more credible.

Spiegel Online implies that the motive behind the account being propagated by sources connected to the mercenaries stems from the “claim they are being used as cannon fodder, are being kept quiet and are poorly paid. For them to now accuse the Kremlin of trying to cover up the fact that Russians were killed — by the Americans, of all people — hits President Vladimir Putin’s government in a weak spot: its credibility.”

The Spiegel Online account and casualty tally — 250 Syrian/Shi’a killed out of approximately 900 engaged, with 10-20 Russian mercenaries killed by collateral fire — seems a good deal more plausible than the figures mentioned in the initial Western media reports.

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.

How Does the U.S. Army Calculate Combat Power? ¯\_(ツ)_/¯

The constituents of combat power as described in current U.S. military doctrine. [The Lightning Press]

One of the fundamental concepts of U.S. warfighting doctrine is combat power. The current U.S. Army definition is “the total means of destructive, constructive, and information capabilities that a military unit or formation can apply at a given time. (ADRP 3-0).” It is the construct commanders and staffs are taught to use to assess the relative effectiveness of combat forces and is woven deeply throughout all aspects of U.S. operational thinking.

To execute operations, commanders conceptualize capabilities in terms of combat power. Combat power has eight elements: leadership, information, mission command, movement and maneuver, intelligence, fires, sustainment, and protection. The Army collectively describes the last six elements as the warfighting functions. Commanders apply combat power through the warfighting functions using leadership and information. [ADP 3-0, Operations]

Yet, there is no formal method in U.S. doctrine for estimating combat power. The existing process is intentionally subjective and largely left up to judgment. This is problematic, given that assessing the relative combat power of friendly and opposing forces on the battlefield is the first step in Course of Action (COA) development, which is at the heart of the U.S. Military Decision-Making Process (MDMP). Estimates of combat power also figure heavily in determining the outcomes of wargames evaluating proposed COAs.

The Existing Process

The Army’s current approach to combat power estimation is outlined in Field Manual (FM) 6-0 Commander and Staff Organization and Operations (2014). Planners are instructed to “make a rough estimate of force ratios of maneuver units two levels below their echelon.” They are then directed to “compare friendly strengths against enemy weaknesses, and vice versa, for each element of combat power.” It is “by analyzing force ratios and determining and comparing each force’s strengths and weaknesses as a function of combat power” that planners gain insight into tactical and operational capabilities, perspectives, vulnerabilities, and required resources.

That is it. Planners are told that “although the process uses some numerical relationships, the estimate is largely subjective. Assessing combat power requires assessing both tangible and intangible factors, such as morale and levels of training.” There is no guidance as to how to determine force ratios [numbers of troops or weapons systems?]. Nor is there any description of how to relate force calculations to combat power. Should force strengths be used somehow to determine a combat power value? Who knows? No additional doctrinal or planning references are provided.

Planners then use these subjective combat power assessments as they shape potential COAs and test them through wargaming. Although explicitly warned not to “develop and recommend COAs based solely on mathematical analysis of force ratios,” they are invited at this stage to consult a table of “minimum historical planning ratios as a starting point.” The table is clearly derived from the ubiquitous 3-1 rule of combat. Contrary to what FM 6-0 claims, neither the 3-1 rule nor the table have a clear historical provenance or any sort of empirical substantiation. There is no proven validity to any of the values cited. It is not even clear whether the “historical planning ratios” apply to manpower, firepower, or combat power.

During this phase, planners are advised to account for “factors that are difficult to gauge, such as impact of past engagements, quality of leaders, morale, maintenance of equipment, and time in position. Levels of electronic warfare support, fire support, close air support, civilian support, and many other factors also affect arraying forces.” FM 6-0 offers no detail as to how these factors should be measured or applied, however.

FM 6-0 also addresses combat power assessment for stability and civil support operations through troop-to-task analysis. Force requirements are to be based on an estimate of troop density, a “ratio of security forces (including host-nation military and police forces as well as foreign counterinsurgents) to inhabitants.” The manual advises that most “most density recommendations fall within a range of 20 to 25 counterinsurgents for every 1,000 residents in an area of operations. A ratio of twenty counterinsurgents per 1,000 residents is often considered the minimum troop density required for effective counterinsurgency operations.”

While FM 6-0 acknowledges that “as with any fixed ratio, such calculations strongly depend on the situation,” it does not mention that any references to force level requirements, tie-down ratios, or troop density were stripped from both Joint and Army counterinsurgency manuals in 2013 and 2014. Yet, this construct lingers on in official staff planning doctrine. (Recent research challenged the validity of the troop density construct but the Defense Department has yet to fund any follow-on work on the subject.)

The Army Has Known About The Problem For A Long Time

The Army has tried several solutions to the problem of combat power estimation over the years. In the early 1970s, the U.S. Army Center for Army Analysis (CAA; known then as the U.S. Army Concepts & Analysis Agency) developed the Weighted Equipment Indices/Weighted Unit Value (WEI/WUV or “wee‑wuv”) methodology for calculating the relative firepower of different combat units. While WEI/WUV’s were soon adopted throughout the Defense Department, the subjective nature of the method gradually led it to be abandoned for official use.

In the 1980s and 1990s, the U.S. Army Command & General Staff College (CGSC) published the ST 100-9 and ST 100-3 student workbooks that contained tables of planning factors that became the informal basis for calculating combat power in staff practice. The STs were revised regularly and then adapted into spreadsheet format in the late 1990s. The 1999 iteration employed WEI/WEVs as the basis for calculating firepower scores used to estimate force ratios. CGSC stopped updating the STs in the early 2000s, as the Army focused on irregular warfare.

With the recently renewed focus on conventional conflict, Army staff planners are starting to realize that their planning factors are out of date. In an attempt to fill this gap, CGSC developed a new spreadsheet tool in 2012 called the Correlation of Forces (COF) calculator. It apparently drew upon analysis done by the U.S. Army Training and Doctrine Command Analysis Center (TRAC) in 2004 to establish new combat unit firepower scores. (TRAC’s methodology is not clear, but if it is based on this 2007 ISMOR presentation, the scores are derived from runs by an unspecified combat model modified by factors derived from the Army’s unit readiness methodology. If described accurately, this would not be an improvement over WEI/WUVs.)

The COF calculator continues to use the 3-1 force ratio tables. It also incorporates a table for estimating combat losses based on force ratios (this despite ample empirical historical analysis showing that there is no correlation between force ratios and casualty rates).

While the COF calculator is not yet an official doctrinal product, CGSC plans to add Marine Corps forces to it for use as a joint planning tool and to incorporate it into the Army’s Command Post of the Future (CPOF). TRAC is developing a stand-alone version for use by force developers.

The incorporation of unsubstantiated and unvalidated concepts into Army doctrine has been a long standing problem. In 1976, Huba Wass de Czege, then an Army major, took both “loosely structured and unscientific analysis” based on intuition and experience and simple counts of gross numbers to task as insufficient “for a clear and rigorous understanding of combat power in a modern context.” He proposed replacing it with a analytical framework for analyzing combat power that accounted for both measurable and intangible factors. Adopting a scrupulous method and language would overcome the simplistic tactical analysis then being taught. While some of the essence of Wass de Czege’s approach has found its way into doctrinal thinking, his criticism of the lack of objective and thorough analysis continues to echo (here, here, and here, for example).

Despite dissatisfaction with the existing methods, little has changed. The problem with this should be self-evident, but I will give the U.S. Naval War College the final word here:

Fundamentally, all of our approaches to force-on-force analysis are underpinned by theories of combat that include both how combat works and what matters most in determining the outcomes of engagements, battles, campaigns, and wars. The various analytical methods we use can shed light on the performance of the force alternatives only to the extent our theories of combat are valid. If our theories are flawed, our analytical results are likely to be equally wrong.

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.”

TDI Friday Read: Principles Of War & Verities Of Combat

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Trevor Dupuy distilled his research and analysis on combat into a series of verities, or what he believed were empirically-derived principles. He intended for his verities to complement the classic principles of war, a slightly variable list of maxims of unknown derivation and provenance, which describe the essence of warfare largely from the perspective of Western societies. These are summarized below.

What Is The Best List Of The Principles Of War?

The Timeless Verities of Combat

Trevor N. Dupuy’s Combat Attrition Verities

Trevor Dupuy’s Combat Advance Rate Verities

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

Human Factors In Warfare: Friction

The Prussian military philosopher Carl von Clausewitz identified the concept of friction in warfare in his book On War, published in 1832.

Everything in war is very simple, but the simplest thing is difficult. The difficulties accumulate and end by producing a kind of friction that is inconceivable unless one has experienced war… Countless minor incidents—the kind you can never really foresee—combine to lower the general level of performance, so that one always falls far short of the intended goal… Friction is the only concept that more or less corresponds to the factors that distinguish real war from war on paper… None of [the military machine’s] components is of one piece: each part is composed of individuals, every one of whom retains his potential of friction [and] the least important of whom may chance to delay things or somehow make them go wrong…

[Carl von Clausewitz, On War, Edited and translated by Michael Howard and Peter Paret (Princeton, NJ: Princeton University Press, 1984). Book One, Chapter 7, 119-120.]

While recognizing this hugely significant intangible element, Clausewitz also asserted that “[F]riction…brings about effects that cannot be measured, just they are largely due to chance.” Nevertheless, the clearly self-evident nature of friction in warfare subsequently led to the assimilation of the concept into the thinking of most military theorists and practitioners.

Flash forward 140 years or so. While listening to a lecture on combat simulation, Trevor Dupuy had a flash of insight that led him to conclude that it was indeed possible to measure the effects of friction.[1] Based on his work with historical combat data, Dupuy knew that smaller-sized combat forces suffer higher casualty rates than do larger-sized forces. As the diagram at the top demonstrates, this is partly explained by the fact that small units have a much higher proportion of their front line troops exposed to hostile fire than large units.

However, this relationship can account for only a fraction of friction’s total effect. The average exposure of a company of 200 soldiers is about seven times greater than an army group of 100,000. Yet, casualty rates for a company in intensive combat can be up to 70 times greater than that of an army group. This discrepancy clearly shows the influence of another factor at work.

Dupuy hypothesized that this reflected the apparent influence of the relationship between dispersion, deployment, and friction on combat. As friction in combat accumulates through the aggregation of soldiers into larger-sized units, its effects degrade the lethal effects of weapons from their theoretical maximum. Dupuy calculated that friction affects a force of 100,000 ten times more than it does a unit of 200. Being an ambient, human factor on the battlefield, higher quality forces do a better job of managing friction’s effects than do lower quality ones.

After looking at World War II combat casualty data to calculate the effect of friction on combat, Dupuy looked at casualty rates from earlier eras and found a steady correlation, which he believed further validated his hypothesis.

Despite the consistent fit of the data, Dupuy felt that his work was only the beginning of a proper investigation into the phenomenon.

During the periods of actual combat, the lower the level, the closer the loss rates will approach the theoretical lethalities of the weapons in the hands of the opposing combatants. But there will never be a very close relationship of such rates with the theoretical lethalities. War does not consist merely of a number of duels. Duels, in fact, are only a very small—though integral—part of combat. Combat is a complex process involving interaction over time of many men and numerous weapons combined in a great number of different, and differently organized, units. This process cannot be understood completely by considering the theoretical interactions of individual men and weapons. Complete understanding requires knowing how to structure such interactions and fit them together. Learning how to structure these interactions must be based on scientific analysis of real combat data.

NOTES

[1] This post is based on Trevor N. Dupuy, Understanding War: History and Theory of Combat (New York: Paragon House, 1987), Chapter 14.

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

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.

Predictions

We do like to claim we have predicted the casualty rates correctly in three wars (operations): 1) The 1991 Gulf War, 2) the 1995 Bosnia intervention, and 3) the Iraq insurgency.  Furthermore, these were predictions make of three very different types of operations, a conventional war, an “operation other than war” (OOTW) and an insurgency.

The Gulf War prediction was made in public testimony by Trevor Dupuy to Congress and published in his book If War Comes: How to Defeat Saddam Hussein. It is discussed in my book America’s Modern Wars (AMW) pages 51-52 and in some blog posts here.

The Bosnia intervention prediction is discussed in Appendix II of AMW and the Iraq casualty estimate is Chapter 1 and Appendix I.

We like to claim that we are three for three on these predictions. What does that really mean? If the odds of making a correct prediction are 50/50 (the same as a coin toss), then the odds of getting three correct predictions in a row is 12.5%. We may not be particularly clever, just a little lucky.

On the other hand, some might argue that these predictions were not that hard to make, and knowledgeable experts would certainly predict correctly at least two-thirds of the time. In that case the odds of getting three correct predictions in a row is more like 30%.

Still, one notes that there was a lot of predictions concerning the Gulf War that were higher than Trevor Dupuy’s. In the case of Bosnia, the Joint Staff was informed by a senior OR (Operations Research) office in the Army that there was no methodology for predicting losses in an “operation other than war” (AMW, page 309). In the case of the Iraq casualty estimate, we were informed by a director of an OR organization that our estimate was too high, and that the U.S. would suffer less than 2,000 killed and be withdrawn in a couple of years (Shawn was at that meeting). I think I left that out of my book in its more neutered final draft….my first draft was more detailed and maybe a little too “angry”. So maybe, predicting casualties in military operations is a little tricky. If the odds of a correct prediction was only one-in-three, then the odds of getting three correct predictions in a row is only 4%. For marketing purposes, we like this argument better 😉

Hard to say what are the odds of making a correct prediction are. The only war that had multiple public predictions (and of course, several private and classified ones) was the 1991 Gulf War. There were a number of predictions made and we believe most were pretty high. There was no other predictions we are aware of for Bosnia in 1995, other than the “it could turn into another Vietnam” ones. There are no other predictions we are aware of for Iraq in 2004, although lots of people were expressing opinions on the subject. So, it is hard to say how difficult it is to make a correct prediction in these cases.

P.S.: Yes, this post was inspired by my previous post on the Stanley Cup play-offs.

 

Trevor Dupuy and Historical Trends Related to Weapon Lethality

There appears to be renewed interest in U.S. Army circles in Trevor Dupuy’s theory of a historical relationship between increasing weapon lethality, declining casualty rates, and greater dispersion on the battlefield. A recent article by Army officer and strategist Aaron Bazin, “Seven Charts That Help Explain American War” at The Strategy Bridge, used a composite version of two of Dupuy’s charts to explain the American military’s attraction to technology. (The graphic in Bazin’s article originated in a 2009 Australian Army doctrinal white paper, “Army’s Future Land Operating Concept,” which evidently did not cite Dupuy as the original source for the charts or the associated concepts.)

John McRea, like Bazin a U.S. Army officer, and a founding member of The Military Writer’s Guild, reposted Dupuy’s graphic in a blog post entitled “Outrageous Fortune: Spears and Arrows,” examining tactical and economic considerations in the use of asymmetrical technologies in warfare.

Dr. Conrad Crane, Chief of Historical Services for the U.S. Army Heritage and Education Center at the Army War College, also referenced Dupuy’s concepts in his look at human performance requirements, “The Future Soldier: Alone in a Crowd,” at War on the Rocks.

Dupuy originally developed his theory based on research and analysis undertaken by the Historical Evaluation and Research Organization (HERO) in 1964, for a study he directed, “Historical Trends Related to Weapon Lethality.” (Annex I, Annex II, Annex III). HERO had been contracted by the Advanced Tactics Project (AVTAC) of the U.S. Army Combat Developments Command, to provide unclassified support for Project OREGON TRAIL, a series of 45 classified studies of tactical nuclear weapons, tactics, and organization, which took 18 months to complete.

AVTAC asked HERO “to identify and analyze critical relationships and the cause-effect aspects of major advances in the lethality of weapons and associated changes in tactics and organization” from the Roman Era to the present. HERO’s study itself was a group project, incorporating 58 case studies from 21 authors, including such scholars as Gunther E. Rothenberg, Samuel P. Huntington, S.L.A. Marshall, R. Ernest Dupuy, Grace P. Hayes, Louis Morton, Peter Paret, Stefan T. Possony, and Theodore Ropp.

Dupuy synthesized and analyzed these case studies for the HERO study’s final report. He described what he was seeking to establish in his 1979 book, Numbers, Predictions and War: Using History to Evaluate Combat Factors and Predict the Outcome of Battles.

If the numbers of military history mean anything, it appears self-evident that there must be some kind of relationship between the quantities of weapons employed by opposing forces in combat, and the number of casualties suffered by each side. It also seems fairly obvious that some weapons are likely to cause more casualties than others, and that the effectiveness of weapons will depend upon their ability to reach their targets. So it becomes clear that the relationship of weapons to casualties is not quite the simple matter of comparing numbers to numbers. To compare weapons to casualties it is necessary to know not only the numbers of weapons, but also how many there are of each different type, and how effective or lethal each of these is.

The historical relationship between lethality, casualties, and dispersion that Dupuy deduced in this study provided the basis for his subsequent quest to establish an empirically-based, overarching theory of combat, which he articulated through his Quantified Judgement Model. Dupuy refined and updated the analysis from the 1964 HERO study in his 1980 book, The Evolution of Weapons and Warfare.