The Military Operations Research Society (MORS) publishes a periodical journal called the Phalanx. In the December 2018 issue was an article that referenced one of our blog posts. This took us by surprise. We only found out about thanks to one of the viewers of this blog. We are not members of MORS. The article is paywalled and cannot be easily accessed if you are not a member.
It is titled “Perspectives on Combat Modeling” (page 28) and is written by Jonathan K. Alt, U.S. Army TRADOC Analysis Center, Monterey, CA.; Christopher Morey, PhD, Training and Doctrine Command Analysis Center, Ft. Leavenworth, Kansas; and Larry Larimer, Training and Doctrine Command Analysis Center, White Sands, New Mexico. I am not familiar with any of these three gentlemen.
The blog post that appears to be generating this article is this one:
Simply by coincidence, Shawn Woodford recently re-posted this in January. It was originally published on 10 April 2017 and was written by Shawn.
The opening two sentences of the article in the Phalanx reads:
Periodically, within the Department of Defense (DoD) analytic community, questions will arise regarding the validity of the combat models and simulations used to support analysis. Many attempts (sic) to resurrect the argument that models, simulations, and wargames “are built on the thin foundation of empirical knowledge about the phenomenon of combat.” (Woodford, 2017).
It is nice to be acknowledged, although it this case, it appears that we are being acknowledged because they disagree with what we are saying.
Probably the word that gets my attention is “resurrect.” It is an interesting word, that implies that this is an old argument that has somehow or the other been put to bed. Granted it is an old argument. On the other hand, it has not been put to bed. If a problem has been identified and not corrected, then it is still a problem. Age has nothing to do with it.
On the other hand, maybe they are using the word “resurrect” because recent developments in modeling and validation have changed the environment significantly enough that these arguments no longer apply. If so, I would be interested in what those changes are. The last time I checked, the modeling and simulation industry was using many of the same models they had used for decades. In some cases, were going back to using simpler hex-games for their modeling and wargaming efforts. We have blogged a couple of times about these efforts. So, in the world of modeling, unless there have been earthshaking and universal changes made in the last five years that have completely revamped the landscape….then the decades old problems still apply to the decades old models and simulations.
More to come (this is the first of at least 7 posts on this subject).
Source: David A. Shlapak and Michael Johnson. Reinforcing Deterrence on NATO’s Eastern Flank: Wargaming the Defense of the Baltics. Santa Monica, CA: RAND Corporation, 2016.
[UPDATE] We had several readers recommend games they have used or would be suitable for simulating Multi-Domain Battle and Operations (MDB/MDO) concepts. These include several classic campaign-level board wargames:
Chris Lawrence recently looked at C-WAM and found that it uses a lot of traditional board wargaming elements, including methodologies for determining combat results, casualties, and breakpoints that have been found unable to replicate real-world outcomes (aka “The Base of Sand” problem).
What other wargames, models, and simulations are there being used out there? Are there any commercial wargames incorporating MDB/MDO elements into their gameplay? What methodologies are being used to portray MDB/MDO effects?
A great deal of importance has been placed on the knowledge derived from these activities. As the U.S. Army Training and Doctrine Command recently stated,
Concept analysis informed by joint and multinational learning events…will yield the capabilities required of multi-domain battle. Resulting doctrine, organization, training, materiel, leadership, personnel and facilities solutions will increase the capacity and capability of the future force while incorporating new formations and organizations.
There is, however, a problem afflicting the Defense Department’s wargames, of which the military operations research and models and simulations communities have long been aware, but have been slow to address: their models are built on a thin foundation of empirical knowledge about the phenomenon of combat. None have proven the ability to replicate real-world battle experience. This is known as the “base of sand” problem.
A Brief History of The Base of Sand
All combat models and simulations are abstracted theories of how combat works. Combat modeling in the United States began in the early 1950s as an extension of military operations research that began during World War II. Early model designers did not have large base of empirical combat data from which to derive their models. Although a start had been made during World War II and the Korean War to collect real-world battlefield data from observation and military unit records, an effort that provided useful initial insights, no systematic effort has ever been made to identify and assemble such information. In the absence of extensive empirical combat data, model designers turned instead to concepts of combat drawn from official military doctrine (usually of uncertain provenance), subject matter expertise, historians and theorists, the physical sciences, or their own best guesses.
As the U.S. government’s interest in scientific management methods blossomed in the late 1950s and 1960s, the Defense Department’s support for operations research and use of combat modeling in planning and analysis grew as well. By the early 1970s, it became evident that basic research on combat had not kept pace. A survey of existing combat models by Gary Shubik and Martin Brewer for RAND in 1972 concluded that
Basic research and knowledge is lacking. The majority of the MSGs [models, simulations and games] sampled are living off a very slender intellectual investment in fundamental knowledge…. [T]he need for basic research is so critical that if no other funding were available we would favor a plan to reduce by a significant proportion all current expenditures for MSGs and to use the saving for basic research.
The [Defense Department]is becoming critically dependent on combat models (including simulations and war games)—even more dependent than in the past. There is considerable activity to improve model interoperability and capabilities for distributed war gaming. In contrast to this interest in model-related technology, there has been far too little interest in the substance of the models and the validity of the lessons learned from using them. In our view, the DoD does not appreciate that in many cases the models are built on a base of sand…
[T]he DoD’s approach in developing and using combat models, including simulations and war games, is fatally flawed—so flawed that it cannot be corrected with anything less than structural changes in management and concept. [Original emphasis]
As a remedy, the authors recommended that the Defense Department create an office to stimulate a national military science program. This Office of Military Science would promote and sponsor basic research on war and warfare while still relying on the military services and other agencies for most research and analysis.
Davis and Blumenthal initially drafted their white paper before the 1991 Gulf War, but the performance of the Defense Department’s models and simulations in that conflict underscored the very problems they described. Defense Department wargames during initial planning for the conflict reportedly predicted tens of thousands of U.S. combat casualties. These simulations were said to have led to major changes in U.S. Central Command’s operational plan. When the casualty estimates leaked, they caused great public consternation and inevitable Congressional hearings.
The Defense Department’s current generation of models and simulations harbor the same weaknesses as the ones in use in the 1990s. Some are new iterations of old models with updated graphics and code, but using the same theoretical assumptions about combat. In most cases, no one other than the designers knows exactly what data and concepts the models are based upon. This practice is known in the technology world as black boxing. While black boxing may be an essential business practice in the competitive world of government consulting, it makes independently evaluating the validity of combat models and simulations nearly impossible. This should be of major concern because many models and simulations in use today contain known flaws.
Others, such as the Joint Conflict And Tactical Simulation (JCATS), MAGTF Tactical Warfare System (MTWS), and Warfighters’ Simulation (WARSIM) adjudicate ground combat using probability of hit/probability of kill (pH/pK) algorithms. Corps Battle Simulation (CBS) uses pH/pK for direct fire attrition and a modified version of Lanchester for indirect fire. While these probabilities are developed from real-world weapon system proving ground data, their application in the models is combined with inputs from subjective sources, such as outputs from other combat models, which are likely not based on real-world data. Multiplying an empirically-derived figure by a judgement-based coefficient results in a judgement-based estimate, which might be accurate or it might not. No one really knows.
This state of affairs seems remarkable given the enormous stakes that are being placed on the output of the Defense Department’s modeling and simulation activities. After decades of neglect, remedying this would require a dedicated commitment to sustained basic research on the military science of combat and warfare, with no promise of a tangible short-term return on investment. Yet, as Biddle pointed out, “With so much at stake, we surely must do better.”
[NOTE: The attrition methodologies used in CBS and WARSIM have been corrected since this post was originally published per comments provided by their developers.]
There are three versions of force ratio versus casualty exchange ratio rules, such as the three-to-one rule (3-to-1 rule), as it applies to casualties. The earliest version of the rule as it relates to casualties that we have been able to find comes from the 1958 version of the U.S. Army Maneuver Control manual, which states: “When opposing forces are in contact, casualties are assessed in inverse ratio to combat power. For friendly forces advancing with a combat power superiority of 5 to 1, losses to friendly forces will be about 1/5 of those suffered by the opposing force.”[1]
The RAND version of the rule (1992) states that: “the famous ‘3:1 rule ’, according to which the attacker and defender suffer equal fractional loss rates at a 3:1 force ratio the battle is in mixed terrain and the defender enjoys ‘prepared ’defenses…” [2]
Finally, there is a version of the rule that dates from the 1967 Maneuver Control manual that only applies to armor that shows:
As the RAND construct also applies to equipment losses, then this formulation is directly comparable to the RAND construct.
Therefore, we have three basic versions of the 3-to-1 rule as it applies to casualties and/or equipment losses. First, there is a rule that states that there is an even fractional loss ratio at 3-to-1 (the RAND version), Second, there is a rule that states that at 3-to-1, the attacker will suffer one-third the losses of the defender. And third, there is a rule that states that at 3-to-1, the attacker and defender will suffer the same losses as the defender. Furthermore, these examples are highly contradictory, with either the attacker suffering three times the losses of the defender, the attacker suffering the same losses as the defender, or the attacker suffering 1/3 the losses of the defender.
Therefore, what we will examine here is the relationship between force ratios and exchange ratios. In this case, we will first look at The Dupuy Institute’s Battles Database (BaDB), which covers 243 battles from 1600 to 1900. We will chart on the y-axis the force ratio as measured by a count of the number of people on each side of the forces deployed for battle. The force ratio is the number of attackers divided by the number of defenders. On the x-axis is the exchange ratio, which is a measured by a count of the number of people on each side who were killed, wounded, missing or captured during that battle. It does not include disease and non-battle injuries. Again, it is calculated by dividing the total attacker casualties by the total defender casualties. The results are provided below:
As can be seen, there are a few extreme outliers among these 243 data points. The most extreme, the Battle of Tippennuir (l Sep 1644), in which an English Royalist force under Montrose routed an attack by Scottish Covenanter militia, causing about 3,000 casualties to the Scots in exchange for a single (allegedly self-inflicted) casualty to the Royalists, was removed from the chart. This 3,000-to-1 loss ratio was deemed too great an outlier to be of value in the analysis.
As it is, the vast majority of cases are clumped down into the corner of the graph with only a few scattered data points outside of that clumping. If one did try to establish some form of curvilinear relationship, one would end up drawing a hyperbola. It is worthwhile to look inside that clump of data to see what it shows. Therefore, we will look at the graph truncated so as to show only force ratios at or below 20-to-1 and exchange rations at or below 20-to-1.
Again, the data remains clustered in one corner with the outlying data points again pointing to a hyperbola as the only real fitting curvilinear relationship. Let’s look at little deeper into the data by truncating the data on 6-to-1 for both force ratios and exchange ratios. As can be seen, if the RAND version of the 3-to-1 rule is correct, then the data should show at 3-to-1 force ratio a 3-to-1 casualty exchange ratio. There is only one data point that comes close to this out of the 243 points we examined.
If the FM 105-5 version of the rule as it applies to armor is correct, then the data should show that at 3-to-1 force ratio there is a 1-to-1 casualty exchange ratio, at a 4-to-1 force ratio a 1-to-2 casualty exchange ratio, and at a 5-to-1 force ratio a 1-to-3 casualty exchange ratio. Of course, there is no armor in these pre-WW I engagements, but again no such exchange pattern does appear.
If the 1958 version of the FM 105-5 rule as it applies to casualties is correct, then the data should show that at a 3-to-1 force ratio there is 0.33-to-1 casualty exchange ratio, at a 4-to-1 force ratio a .25-to-1 casualty exchange ratio, and at a 5-to-1 force ratio a 0.20-to-5 casualty exchange ratio. As can be seen, there is not much indication of this pattern, or for that matter any of the three patterns.
Still, such a construct may not be relevant to data before 1900. For example, Lanchester claimed in 1914 in Chapter V, “The Principal of Concentration,” of his book Aircraft in Warfare, that there is greater advantage to be gained in modern warfare from concentration of fire.[3] Therefore, we will tap our more modern Division-Level Engagement Database (DLEDB) of 675 engagements, of which 628 have force ratios and exchange ratios calculated for them. These 628 cases are then placed on a scattergram to see if we can detect any similar patterns.
Even though this data covers from 1904 to 1991, with the vast majority of the data coming from engagements after 1940, one again sees the same pattern as with the data from 1600-1900. If there is a curvilinear relationship, it is again a hyperbola. As before, it is useful to look into the mass of data clustered into the corner by truncating the force and exchange ratios at 20-to-1. This produces the following:
Again, one sees the data clustered in the corner, with any curvilinear relationship again being a hyperbola. A look at the data further truncated to a 10-to-1 force or exchange ratio does not yield anything more revealing.
And, if this data is truncated to show only 5-to-1 force ratio and exchange ratios, one again sees:
Again, this data appears to be mostly just noise, with no clear patterns here that support any of the three constructs. In the case of the RAND version of the 3-to-1 rule, there is again only one data point (out of 628) that is anywhere close to the crossover point (even fractional exchange rate) that RAND postulates. In fact, it almost looks like the data conspires to make sure it leaves a noticeable “hole” at that point. The other postulated versions of the 3-to-1 rules are also given no support in these charts.
While we can attempt to torture the data to find a better fit, or can try to argue that the patterns are obscured by various factors that have not been considered, we do not believe that such a clear pattern and relationship exists. More advanced mathematical methods may show such a pattern, but to date such attempts have not ferreted out these alleged patterns. For example, we refer the reader to Janice Fain’s article on Lanchester equations, The Dupuy Institute’s Capture Rate Study, Phase I & II, or any number of other studies that have looked at Lanchester.[4]
The fundamental problem is that there does not appear to be a direct cause and effect between force ratios and exchange ratios. It appears to be an indirect relationship in the sense that force ratios are one of several independent variables that determine the outcome of an engagement, and the nature of that outcome helps determines the casualties. As such, there is a more complex set of interrelationships that have not yet been fully explored in any study that we know of, although it is briefly addressed in our Capture Rate Study, Phase I & II.
[3] F. W. Lanchester, Aircraft in Warfare: The Dawn of the Fourth Arm (Lanchester Press Incorporated, Sunnyvale, Calif., 1995), 46-60. One notes that Lanchester provided no data to support these claims, but relied upon an intellectual argument based upon a gross misunderstanding of ancient warfare.
[This piece was originally posted on 13 July 2016.]
Trevor Dupuy’s article cited in my previous post, “Combat Data and the 3:1 Rule,” was the final salvo in a roaring, multi-year debate between two highly regarded members of the U.S. strategic and security studies academic communities, political scientist John Mearsheimer and military analyst/polymath Joshua Epstein. Carried out primarily in the pages of the academic journal International Security, Epstein and Mearsheimer argued the validity of the 3-1 rule and other analytical models with respect the NATO/Warsaw Pact military balance in Europe in the 1980s. Epstein cited Dupuy’s empirical research in support of his criticism of Mearsheimer’s reliance on the 3-1 rule. In turn, Mearsheimer questioned Dupuy’s data and conclusions to refute Epstein. Dupuy’s article defended his research and pointed out the errors in Mearsheimer’s assertions. With the publication of Dupuy’s rebuttal, the International Security editors called a time out on the debate thread.
These debates played a prominent role in the “renaissance of security studies” because they brought together scholars with different theoretical, methodological, and professional backgrounds to push forward a cohesive line of research that had clear implications for the conduct of contemporary defense policy. Just as importantly, the debate forced scholars to engage broader, fundamental issues. Is “military power” something that can be studied using static measures like force ratios, or does it require a more dynamic analysis? How should analysts evaluate the role of doctrine, or politics, or military strategy in determining the appropriate “balance”? What role should formal modeling play in formulating defense policy? What is the place for empirical analysis, and what are the strengths and limitations of existing data?[1]
It is well worth the time to revisit the contributions to the 1980s debate. I have included a bibliography below that is not exhaustive, but is a place to start. The collapse of the Soviet Union and the end of the Cold War diminished the intensity of the debates, which simmered through the 1990s and then were obscured during the counterterrorism/ counterinsurgency conflicts of the post-9/11 era. It is possible that the challenges posed by China and Russia amidst the ongoing “hybrid” conflict in Syria and Iraq may revive interest in interrogating the bases of military analyses in the U.S and the West. It is a discussion that is long overdue and potentially quite illuminating.
American soldiers of the 117th Infantry Regiment, Tennessee National Guard, part of the 30th Infantry Division, move past a destroyed American M5A1 “Stuart” tank on their march to recapture the town of St. Vith during the Battle of the Bulge, January 1945. [Wikipedia][This piece was originally posted on 16 May 2017.]
This post is a partial response to questions from one of our readers (Stilzkin). On the subject of force ratios in conventional combat….I know of no detailed discussion on the phenomenon published to date. It was clearly addressed by Clausewitz. For example:
At Leuthen Frederick the Great, with about 30,000 men, defeated 80,000 Austrians; at Rossbach he defeated 50,000 allies with 25,000 men. These however are the only examples of victories over an opponent two or even nearly three times as strong. Charles XII at the battle of Narva is not in the same category. The Russian at that time could hardly be considered as Europeans; moreover, we know too little about the main features of that battle. Bonaparte commanded 120,000 men at Dresden against 220,000—not quite half. At Kolin, Frederick the Great’s 30,000 men could not defeat 50,000 Austrians; similarly, victory eluded Bonaparte at the desperate battle of Leipzig, though with his 160,000 men against 280,000, his opponent was far from being twice as strong.
These examples may show that in modern Europe even the most talented general will find it very difficult to defeat an opponent twice his strength. When we observe that the skill of the greatest commanders may be counterbalanced by a two-to-one ratio in the fighting forces, we cannot doubt that superiority in numbers (it does not have to more than double) will suffice to assure victory, however adverse the other circumstances.
and:
If we thus strip the engagement of all the variables arising from its purpose and circumstance, and disregard the fighting value of the troops involved (which is a given quantity), we are left with the bare concept of the engagement, a shapeless battle in which the only distinguishing factors is the number of troops on either side.
These numbers, therefore, will determine victory. It is, of course, evident from the mass of abstractions I have made to reach this point that superiority of numbers in a given engagement is only one of the factors that determines victory. Superior numbers, far from contributing everything, or even a substantial part, to victory, may actually be contributing very little, depending on the circumstances.
But superiority varies in degree. It can be two to one, or three or four to one, and so on; it can obviously reach the point where it is overwhelming.
In this sense superiority of numbers admittedly is the most important factor in the outcome of an engagement, as long as it is great enough to counterbalance all other contributing circumstance. It thus follows that as many troops as possible should be brought into the engagement at the decisive point.
And, in relation to making a combat model:
Numerical superiority was a material factor. It was chosen from all elements that make up victory because, by using combinations of time and space, it could be fitted into a mathematical system of laws. It was thought that all other factors could be ignored if they were assumed to be equal on both sides and thus cancelled one another out. That might have been acceptable as a temporary device for the study of the characteristics of this single factor; but to make the device permanent, to accept superiority of numbers as the one and only rule, and to reduce the whole secret of the art of war to a formula of numerical superiority at a certain time and a certain place was an oversimplification that would not have stood up for a moment against the realities of life.
Force ratios were discussed in various versions of FM 105-5 Maneuver Control, but as far as I can tell, this was not material analytically developed. It was a set of rules, pulled together by a group of anonymous writers for the sake of being able to adjudicate wargames.
The only detailed quantification of force ratios was provided in Numbers, Predictions and War by Trevor Dupuy. Again, these were modeling constructs, not something that was analytically developed (although there was significant background research done and the model was validated multiple times). He then discusses the subject in his book Understanding War, which I consider the most significant book of the 90+ that he wrote or co-authored.
The only analytically based discussion of force ratios that I am aware of (or at least can think of at this moment) is my discussion in my upcoming book War by Numbers: Understanding Conventional Combat. It is the second chapter of the book: https://dupuyinstitute.dreamhosters.com/2016/02/17/war-by-numbers-iii/
In this book, I assembled the force ratios required to win a battle based upon a large number of cases from World War II division-level combat. For example (page 18 of the manuscript):
I did this for the ETO, for the battles of Kharkov and Kursk (Eastern Front 1943, divided by when the Germans are attacking and when the Soviets are attacking) and for PTO (Manila and Okinawa 1945).
There is more than can be done on this, and we do have the data assembled to do this, but as always, I have not gotten around to it. This is why I am already considering a War by Numbers II, as I am already thinking about all the subjects I did not cover in sufficient depth in my first book.
Chaplain (Capt.) Emil Kapaun (right) and Capt. Jerome A. Dolan, a medical officer with the 8th Cavalry Regiment, 1st Cavalry Division, carry an exhausted Soldier off the battlefield in Korea, early in the war. Kapaun was famous for exposing himself to enemy fire. When his battalion was overrun by a Chinese force in November 1950, rather than take an opportunity to escape, Kapaun voluntarily remained behind to minister to the wounded. In 2013, Kapaun posthumously received the Medal of Honor for his actions in the battle and later in a prisoner of war camp, where he died in May 1951. [Photo Credit: Courtesy of the U.S. Army Center of Military History]
[This piece was originally published on 27 June 2017.]
Trevor Dupuy’s theories about warfare were sometimes criticized by some who thought his scientific approach neglected the influence of the human element and chance and amounted to an attempt to reduce war to mathematical equations. Anyone who has read Dupuy’s work knows this is not, in fact, the case.
Moral and behavioral (i.e human) factors were central to Dupuy’s research and theorizing about combat. He wrote about them in detail in his books. In 1989, he presented a paper titled “The Fundamental Information Base for Modeling Human Behavior in Combat” at a symposium on combat modeling that provided a clear, succinct summary of his thinking on the topic.
He began by concurring with Carl von Clausewitz’s assertion that
[P]assion, emotion, and fear [are] the fundamental characteristics of combat… No one who has participated in combat can disagree with this Clausewitzean emphasis on passion, emotion, and fear. Without doubt, the single most distinctive and pervasive characteristic of combat is fear: fear in a lethal environment.
Despite the ubiquity of fear on the battlefield, Dupuy pointed out that there is no way to study its impact except through the historical record of combat in the real world.
We cannot replicate fear in laboratory experiments. We cannot introduce fear into field tests. We cannot create an environment of fear in training or in field exercises.
So, to study human reaction in a battlefield environment we have no choice but to go to the battlefield, not the laboratory, not the proving ground, not the training reservation. But, because of the nature of the very characteristics of combat which we want to study, we can’t study them during the battle. We can only do so retrospectively.
We have no choice but to rely on military history. This is why military history has been called the laboratory of the soldier.
He also pointed out that using military history analytically has its own pitfalls and must be handled carefully lest it be used to draw misleading or inaccurate conclusions.
I must also make clear my recognition that military history data is far from perfect, and that–even at best—it reflects the actions and interactions of unpredictable human beings. Extreme caution must be exercised when using or analyzing military history. A single historical example can be misleading for either of two reasons: (a) The data is inaccurate, or (b) The example may be true, but also be untypical.
But, when a number of respectable examples from history show consistent patterns of human behavior, then we can have confidence that behavior in accordance with the pattern is typical, and that behavior inconsistent with the pattern is either untypical, or is inaccurately represented.
He then stated very concisely the scientific basis for his method.
My approach to historical analysis is actuarial. We cannot predict the future in any single instance. But, on the basis of a large set of reliable experience data, we can predict what is likely to occur under a given set of circumstances.
Dupuy listed ten combat phenomena that he believed were directly or indirectly related to human behavior. He considered the list comprehensive, if not exhaustive.
Images from a Finnish Army artillery salvo fired by towed 130mm howitzers during an exercise in 2013. [Puolustusvoimat – Försvarsmakten – The Finnish Defence Forces/YouTube][This piece was originally posted on 24 August 2017.]
There is probably no obscurity of combat requiring clarification and understanding more urgently than that of suppression… Suppression usually is defined as the effect of fire (primarily artillery fire) upon the behavior of hostile personnel, reducing, limiting, or inhibiting their performance of combat duties. Suppression lasts as long as the fires continue and for some brief, indeterminate period thereafter. Suppression is the most important effect of artillery fire, contributing directly to the ability of the supported maneuver units to accomplish their missions while preventing the enemy units from accomplishing theirs. (p. 251)
Official US Army field artillery doctrine makes a distinction between “suppression” and “neutralization.” Suppression is defined to be instantaneous and fleeting; neutralization, while also temporary, is relatively longer-lasting. Neutralization, the doctrine says, results when suppressive effects are so severe and long-lasting that a target is put out of action for a period of time after the suppressive fire is halted. Neutralization combines the psychological effects of suppressive gunfire with a certain amount of damage. The general concept of neutralization, as distinct from the more fleeting suppression, is a reasonable one. (p. 252)
Despite widespread acknowledgement of the existence of suppression and neutralization, the lack of interest in analyzing its effects was a source of professional frustration for Dupuy. As he commented in 1989,
The British did some interesting but inconclusive work on suppression in their battlefield operations research in World War II. In the United States I am aware of considerable talk about suppression, but very little accomplishment, over the past 20 years. In the light of the significance of suppression, our failure to come to grips with the issue is really quite disgraceful.
This lack of interest is curious, given that suppression and neutralization remain embedded in U.S. Army combat doctrine to this day. The current Army definitions are:
Suppression – In the context of the computed effects of field artillery fires, renders a target ineffective for a short period of time producing at least 3-percent casualties or materiel damage. [Army Doctrine Reference Publication (ADRP) 1-02, Terms and Military Symbols, December 2015, p. 1-87]
Neutralization – In the context of the computed effects of field artillery fires renders a target ineffective for a short period of time, producing 10-percent casualties or materiel damage. [ADRP 1-02, p. 1-65]
A particular source for Dupuy’s irritation was the fact that these definitions were likely empirically wrong. As he argued in Understanding War,
This is almost certainly the wrong way to approach quantification of neutralization. Not only is there no historical evidence that 10% casualties are enough to achieve this effect, there is no evidence that any level of losses is required to achieve the psycho-physiological effects of suppression or neutralization. Furthermore, the time period in which casualties are incurred is probably more important than any arbitrary percentage of loss, and the replacement of casualties and repair of damage are probably irrelevant. (p. 252)
Thirty years after Dupuy pointed this problem out, the construct remains enshrined in U.S. doctrine, unquestioned and unsubstantiated. Dupuy himself was convinced that suppression probably had little, if anything, to do with personnel loss rates.
I believe now that suppression is related to and probably a component of disruption caused by combat processes other than surprise, such as a communications failure. Further research may reveal, however, that suppression is a very distinct form of disruption that can be measured or estimated quite independently of disruption caused by any other phenomenon. (Understanding War, p. 251)
He had developed a hypothesis for measuring the effects of suppression, but was unable to interest anyone in the U.S. government or military in sponsoring a study on it. Suppression as a combat phenomenon remains only vaguely understood.
Canadian soldiers going “over the top” during an assault in the First World War. [History.com][This post was originally published on 1 December 2017.]
How many troops are needed to successfully attack or defend on the battlefield? There is a long-standing rule of thumb that holds that an attacker requires a 3-1 preponderance over a defender in combat in order to win. The aphorism is so widely accepted that few have questioned whether it is actually true or not.
Trevor Dupuy challenged the validity of the 3-1 rule on empirical grounds. He could find no historical substantiation to support it. In fact, his research on the question of force ratios suggested that there was a limit to the value of numerical preponderance on the battlefield.
TDI President Chris Lawrence has also challenged the 3-1 rule in his own work on the subject.
The validity of the 3-1 rule is no mere academic question. It underpins a great deal of U.S. military policy and warfighting doctrine. Yet, the only time the matter was seriously debated was in the 1980s with reference to the problem of defending Western Europe against the threat of Soviet military invasion.
It is probably long past due to seriously challenge the validity and usefulness of the 3-1 rule again.
Armies have historically responded to the increasing lethality of weapons by dispersing mass in frontage and depth on the battlefield. Will combat see a new period of adjustment over the next 50 years like the previous half-century, where dispersion continues to shift in direct proportion to increased weapon range and precision, or will there be a significant change in the character of warfare?
One point of departure for such an inquiry could be the work of TDI President Chris Lawrence, who looked into the nature of historical rates of dispersion in combat from 1600 to 1991.
I am focusing on this because l really want to come up with some means of measuring the effects of a “revolution in warfare.” The last 400 years of human history have given us more revolutionary inventions impacting war than we can reasonably expect to see in the next 100 years. In particular, I would like to measure the impact of increased weapon accuracy, improved intelligence, and improved C2 on combat.
His tentative conclusions were:
Dispersion has been relatively constant and driven by factors other than firepower from 1600-1815.
Since the Napoleonic Wars, units have increasingly dispersed (found ways to reduce their chance to be hit) in response to increased lethality of weapons.
As a result of this increased dispersion, casualties in a given space have declined.
The ratio of this decline in casualties over area have been roughly proportional to the strength over an area from 1600 through WWI. Starting with WWII, it appears that people have dispersed faster than weapons lethality, and this trend has continued.
In effect, people dispersed in direct relation to increased firepower from 1815 through 1920, and then after that time dispersed faster than the increase in lethality.
It appears that since WWII, people have gone back to dispersing (reducing their chance to be hit) at the same rate that firepower is increasing.
Effectively, there are four patterns of casualties in modem war:
Period 1 (1600 – 1815): Period of Stability
Short battles
Short frontages
High attrition per day
Constant dispersion
Dispersion decreasing slightly after late 1700s
Attrition decreasing slightly after mid-1700s.
Period 2 (1816 – 1905): Period of Adjustment
Longer battles
Longer frontages
Lower attrition per day
Increasing dispersion
Dispersion increasing slightly faster than lethality
Period 3 (1912 – 1920): Period of Transition
Long battles
Continuous frontages
Lower attrition per day
Increasing dispersion
Relative lethality per kilometer similar to past, but lower
Dispersion increasing slightly faster than lethality
Period 4 (1937 – present): Modern Warfare
Long battles
Continuous frontages
Low attrition per day
High dispersion (perhaps constant?)
Relatively lethality per kilometer much lower than the past
Dispersion increased much faster than lethality going into the period.
Dispersion increased at the same rate as lethality within the period.
Chris based his study on previous work done by Trevor Dupuy and his associates, which established a pattern in historical combat between lethality, dispersion, and battlefield casualty rates.
There is no way to accurately predict the future relationship between weapon lethality and dispersion on the battlefield, but we should question whether or not current conception of combat reflect consideration of the historical trends.
The Military Operations Research Society (MORS) publishes a periodical journal called the Phalanx. In the December 2018 issue was an article that referenced one of our blog posts. This took us by surprise. We only found out about thanks to one of the viewers of this blog. We are not members of MORS. The article is paywalled and cannot be easily accessed if you are not a member.
[This piece was originally posted on 13 July 2016.]
Armies have historically responded to the increasing lethality of weapons by dispersing mass in frontage and depth on the battlefield. Will combat see a new period of adjustment over the next 50 years like the previous half-century, where dispersion continues to shift in direct proportion to increased weapon range and precision, or will there be a significant change in the character of warfare?