The ground forces were the last branch of the Army to incorporate OR into their efforts during World War II, lagging behind the Army Air Forces, the technical services, and the Navy. Where the Army was a step ahead, however, was in creating a robust wartime historical field history documentation program. (After the war, this enabled the publication of the U.S. Army in World War II series, known as the “Green Books,” which set a new standard for government sponsored military histories.)
As Shrader related, the first OR personnel the Army deployed forward in 1944-45 often crossed paths with War Department General Staff Historical Branch field historian detachments. They both engaged in similar activities: collecting data on real-world combat operations, which was then analyzed and used for studies and reports written for the use of the commands to which they were assigned. The only significant difference was in their respective methodologies, with the historians using historical methods and the OR analysts using mathematical and scientific tools.
History and OR after World War II
The usefulness of historical approaches to collecting operational data did not go unnoticed by the OR practitioners, according to Shrader. When the Army established the Operations Research Office (ORO) in 1948, it hired a contingent of historians specifically for the purpose of facilitating research and analysis using WWII Army records, “the most likely source for data on operational matters.”
When the Korean War broke out in 1950, ORO sent eight multi-disciplinary teams, including the historians, to collect operational data and provide analytical support for U.S. By 1953, half of ORO’s personnel had spent time in combat zones. Throughout the 1950s, about 40-43% of ORO’s staff was comprised of specialists in the social sciences, history, business, literature, and law. Shrader quoted one leading ORO analyst as noting that, “there is reason to believe that the lawyer, social scientist or historian is better equipped professionally to evaluate evidence which is derived from the mind and experience of the human species.”
Among the notable historians who worked at or with ORO was Dr. Hugh M. Cole, an Army officer who had served as a staff historian for General George Patton during World War II. Cole rose to become a senior manager at ORO and later served as vice-president and president of ORO’s successor, the Research Analysis Corporation (RAC). Cole brought in WWII colleague Forrest C. Pogue (best known as the biographer of General George C. Marshall) and Charles B. MacDonald. ORO also employed another WWII field historian, the controversial S. L. A. Marshall, as a consultant during the Korean War. Dorothy Kneeland Clark did pioneering historical analysis on combat phenomena while at ORO.
The Demise of ORO…and Historical Combat Analysis?
By the late 1950s, considerable institutional friction had developed between ORO, the Johns Hopkins University (JHU)—ORO’s institutional owner—and the Army. According to Shrader,
Continued distrust of operations analysts by Army personnel, questions about the timeliness and focus of ORO studies, the ever-expanding scope of ORO interests, and, above all, [ORO director] Ellis Johnson’s irascible personality caused tensions that led in August 1961 to the cancellation of the Army’s contract with JHU and the replacement of ORO with a new, independent research organization, the Research Analysis Corporation [RAC].
RAC inherited ORO’s research agenda and most of its personnel, but changing events and circumstances led Army OR to shift its priorities away from field collection and empirical research on operational combat data in favor of the use of modeling and wargaming in its analyses. As Chris Lawrence described in his history of federally-funded Defense Department “think tanks,” the rise and fall of scientific management in DOD, the Vietnam War, social and congressional criticism, and an unhappiness by the military services with the analysis led to retrenchment in military OR by the end of the 60s. The Army sold RAC and created its own in-house Concepts Analysis Agency (CAA; now known as the Center for Army Analysis).
By the early 1970s, analysts, such as RAND’s Martin Shubik and Gary Brewer, and John Stockfisch, began to note that the relationships and processes being modeled in the Army’s combat simulations were not based on real-world data and that empirical research on combat phenomena by the Army OR community had languished. In 1991, Paul Davis and Donald Blumenthal gave this problem a name: the “Base of Sand.”
Between 2001 and 2004, TDI undertook a series of studies on the effects of urban combat in cities for the U.S. Army Center for Army Analysis (CAA). These studies examined a total of 304 cases of urban combat at the divisional and battalion level that occurred between 1942 and 2003, as well as 319 cases of concurrent non-urban combat for comparison.
The primary findings of Phases I-III of the study were:
Urban terrain had no significantly measurable influence on the outcome of battle.
Attacker casualties in the urban engagements were less than in the non-urban engagements and the casualty exchange ratio favored the attacker as well.
One of the primary effects of urban terrain is that it slowed opposed advance rates. The average advance rate in urban combat was one-half to one-third that of non-urban combat.
There is little evidence that combat operations in urban terrain resulted in a higher linear density of troops.
Armor losses in urban terrain were the same as, or lower than armor losses in non-urban terrain. In some cases it appears that armor losses were significantly lower in urban than non-urban terrain.
Urban terrain did not significantly influence the force ratio required to achieve success or effectively conduct combat operations.
Overall, it appears that urban terrain was no more stressful a combat environment during actual combat operations than was non-urban terrain.
Overall, the expenditure of ammunition in urban operations was not greater than that in non-urban operations. There is no evidence that the expenditure of other consumable items (rations; water; or fuel, oil, or lubricants) was significantly different in urban as opposed to non-urban combat.
Since it was found that advance rates in urban combat were significantly reduced, then it is obvious that these two effects (advance rates and time) were interrelated. It does appear that the primary impact of urban combat was to slow the tempo of operations.
In order to broaden and deepen understanding of the effects of urban combat, TDI proposed several follow-up studies. To date, none of these have been funded:
Conduct a detailed study of the Battle of Stalingrad. Stalingrad may also represent one of the most intense examples of urban combat, so may provide some clues to the causes of the urban outliers.
Conduct a detailed study of battalion/brigade-level urban combat. This would begin with an analysis of battalion-level actions from the first two phases of this study (European Theater of Operations and Eastern Front), added to the battalion-level actions completed in this third phase of the study. Additional battalion-level engagements would be added as needed.
Conduct a detailed study of the outliers in an attempt to discover the causes for the atypical nature of these urban battles.
Conduct a detailed study of urban warfare in an unconventional warfare setting.
Details of the Phase I-III study reports and conclusions can be found below:
Now comes Phase III of this effort. The Phase I report was dated 11 January 2002 and covered the European Theater of Operations (ETO). The Phase II report [Part I and Part II] was dated 30 June 2003 and covered the Eastern Front (the three battles of Kharkov). Phase III was completed in 31 July 2004 and covered the Battle of Manila in the Pacific Theater, post-WWII engagements, and battalion-level engagements. It was a pretty far ranging effort.
In the case of Manila, this was the first time that we based our analysis using only one-side data (U.S. only). In this case, the Japanese tended to fight to almost the last man. We occupied the field of combat after the battle and picked up their surviving unit records. Among the Japanese, almost all died and only a few were captured by the U.S. So, we had fairly good data from the U.S. intelligence files. Regardless, the U.S. battle reports for Japanese data was the best data available. This allowed us to work with one-sided data. The engagements were based upon the daily operations of the U.S. Army’s 37th Infantry Division and the 1st Cavalry Division.
Conclusions (from pages 44-45):
The overall conclusions derived from the data analysis in Phase I were as follows, while those from this Phase III analysis are in bold italics.
Urban combat did not significantly influence the Mission Accomplishment (Outcome) of the engagements. Phase III Conclusion: This conclusion was further supported.
Urban combat may have influenced the casualty rate. If so, it appears that it resulted in a reduction of the attacker casualty rate and a more favorable casualty exchange ratio compared to non-urban warfare. Whether or not these differences are caused by the data selection or by the terrain differences is difficult to say, but regardless, there appears to be no basis to the claim that urban combat is significantly more intense with regards to casualties than is non-urban warfare. Phase III Conclusion: This conclusion was further supported. If urban combat influenced the casualty rate, it appears that it resulted in a reduction of the attacker casualty rate and a more favorable casualty exchange ratio compared to non-urban warfare. There still appears to be no basis to the claim that urban combat is significantly more intense with regards to casualties than is non-urban warfare.
The average advance rate in urban combat should be one-half to one-third that of non-urban combat. Phase III Conclusion: There was strong evidence of a reduction in the advance rates in urban terrain in the PTO data. However, given that this was a single extreme case, then TDI still stands by its original conclusion that the average advance rate in urban combat should be about one-half to one-third that of non-urban combat/
Overall, there is little evidence that the presence of urban terrain results in a higher linear density of troops, although the data does seem to trend in that direction. Phase III Conclusion: The PTO data shows the highest densities found in the data sets for all three phases of this study. However, it does not appear that the urban density in the PTO was significantly higher than the non-urban density. So it remains difficult to tell whether or not the higher density was a result of the urban terrain or was simply a consequence of the doctrine adopted to meet the requirements found in the Pacific Theater.
Overall, it appears that the loss of armor in urban terrain is the same as or less than that found in non-urban terrain, and in some cases is significantly lower. Phase III Conclusion: This conclusion was further supported.
Urban combat did not significantly influence the Force Ratio required to achieve success or effectively conduct combat operations. Phase III Conclusion: This conclusion was further supported.
Nothing could be determined from an analysis of the data regarding the Duration of Combat (Time) in urban versus non-urban terrain. Phase III Conclusion: Nothing could be determined from an analysis of the data regarding the Duration of Combat (Time) in urban versus non-urban terrain.
So, in Phase I we compared 46 urban and conurban engagements in the ETO to 91 non-urban engagements. In Phase II, we compared 51 urban and conurban engagements in an around Kharkov to 49 non-urban Kursk engagements. On Phase III, from Manila we compared 53 urban and conurban engagements to 41 non-urban engagements mostly from Iwo Jima, Okinawa and Manila. The next blog post on urban warfare will discuss our post-WWII data.
P.S. The picture is an aerial view of the destroyed walled city of Intramuros taken on May 1945
There was actually supposed to be a part 2 to this Phase II contract, which was analysis of urban combat at the army-level based upon 50 operations, of which a half-dozen would include significant urban terrain. This effort was not funded.
On the other hand, the quantitative analysis of battles of Kharkov only took up the first 41 pages of the report. A significant part of the rest of the report was a more detailed analysis and case study of the three fights over Kharkov in February, March and August of 1943. Kharkov was a large city, according to the January 1939 census, it has a population of 1,344,200, although a Soviet-era encyclopedia gives the pre-war population as 840,000. We never were able to figure out why there was a discrepancy. The whole area was populated with many villages. The January 1939 gives Kharkov Oblast (region) a population of 1,209,496. This is in addition to the city, so the region had a total population of 2,552,686. Soviet-era sources state that when the city was liberated in August 1943, the remaining population was only 190,000. Kharkov was a much larger city than any of the others ones covered in Phase I effort (except for Paris, but the liberation of that city was hardly a major urban battle).
The report then does a day-by-day review of the urban fighting in Kharkov. Doing a book or two on the battles of Kharkov is on my short list of books to write, as I have already done a lot of the research. We do have daily logistical expenditures of the SS Panzer Corps for February and March (tons of ammo fired, gasoline used and diesel used). In March when the SS Panzer Corps re-took Kharkov, we noted that the daily average for the four days of urban combat from 12 to 15 March was 97.25 tons of ammunition, 92 cubic meters of gasoline and 10 cubic meters of diesel. For the previous five days (7-11 March) the daily average was 93.20 tons of ammunition, 145 cubic meters of gasoline and 9 cubic meters of diesel. Thus it does not produce a lot of support for the idea that–as has sometimes been expressed (for example in RAND’s earlier reports on the subject)–that ammunition and other supplies will be consumed at a higher rate in urban operations.
We do observe from the three battles of Kharkov that (page 95):
There is no question that the most important lesson found in the three battles of Kharkov is that one should just bypass cities rather than attack them. The Phase I study also points out that the attacker is usually aware that faster progress can be made outside the urban terrain, and that the tendency is to weight one or both flanks and not bother to attack the city until it is enveloped. This is indeed what happened in two of the three cases at Kharkov and was also the order given by the Fourth Panzer Army that was violated by the SS Panzer Corps in March.
One must also note that since this study began the United States invaded Iraq and conducted operations in some major urban areas, albeit against somewhat desultory and ineffective opposition. In the southern part of Iraq the two major port cities Umm Qasar and Basra were first enveloped before any forces were sent in to clear them. In the case of Baghdad, it could have been enveloped if sufficient forces were available. As it was, it was not seriously defended. The recent operations in Iraq again confirmed that observations made in the two phases of this study.
P.S. The picture is of Kharkov in 1942, when it was under German occupation.
Our first urban warfare report that we did had a big impact. It clearly showed that the intensity of urban warfare was not what some of the “experts” out there were claiming. In particular, it called into question some of the claims being made by RAND. But, the report was based upon Aachen, Cherbourg, and a collection of mop-up operations along the Channel Coast. Although this was a good starting point because of the ease of research and availability of data, we did not feel that this was a fully representative collection of cases. We also did not feel that it was based upon enough cases, although we had already assembled more cases than most “experts” were using. We therefore convinced CAA (Center for Army Analysis) to fund a similar effort for the Eastern Front in World War II.
For this second phase, we again assembled a collection of Eastern Front urban warfare engagements in our DLEDB (Division-level Engagement Data Base) and compared it to Eastern Front non-urban engagements. We had, of course, a considerable collection of non-urban engagements already assembled from the Battle of Kursk in July 1943. We therefore needed a good urban engagement nearby. Kharkov is the nearest major city to where these non-urban engagements occurred and it was fought over three times in 1943. It was taken by the Red Army in February, it was retaken by the German Army in March, and it was taken again by the Red Army in August. Many of the units involved were the same units involved in the Battle of Kursk. This was a good close match. It has the additional advantage that both sides were at times on the offense.
Furthermore, Kharkov was a big city. At the time it was the fourth biggest city in the Soviet Union, being bigger than Stalingrad (as measured by pre-war population). A picture of its Red Square in March 1943, after the Germans retook it, is above.
We did have good German records for 1943 and we were able to get access to Soviet division-level records from February, March and August from the Soviet military archives in Podolsk. Therefore, we were able to assembled all the engagements based upon the unit records of both sides. No secondary sources were used, and those that were available were incomplete, usually one-sided, sometimes biased and often riddled with factual errors.
So, we ended up with 51 urban and conurban engagements from the fighting around Kharkov, along with 65 non-urban engagements from Kursk (we have more now).
The Phase II effort was completed on 30 June 2003. The conclusions of Phase II (pages 40-41) were similar to Phase I:
.Phase II Conclusions:
Mission Accomplishment: This [Phase I] conclusion was further supported. The data does show a tendency for urban engagements not to generate penetrations.
Casualty Rates: This [Phase I] conclusion was further supported. If urban combat influenced the casualty rate, it appears that it resulted in a reduction of the attacker casualty rate and a more favorable casualty exchange ratio compared to nonurban warfare. There still appears to be no basis to the claim that urban combat is significantly more intense with regards to casualties than is nonurban warfare.
Advance Rates: There is no strong evidence of a reduction in the advance rates in urban terrain in the Eastern Front data. TDI still stands by its original conclusion that the average advance rate in urban combat should be one-half to one-third that of nonurban combat.
Linear Density: Again, there is little evidence that the presence of urban terrain results in a higher linear density of troops, but unlike the ETO data, the data did not show a tendency to trend in that direction.
Armor Losses: This conclusion was further supported (Phase I conclusion was: Overall, it appears that the loss of armor in urban terrain is the same as or less than that found in nonurban terrain, and in some cases is significantly lower.)
Force Ratios: The conclusion was further supported (Phase I conclusion was: Urban combat did not significantly influence the Force Ratio required to achieve success or effectively conduct combat operations).
Duration of Combat: Nothing could be determined from an analysis of the data regarding the Duration of Combat (Time) in urban versus nonurban terrain.
There is a part 2 to this effort that I will pick up in a later post.
“Catalina Kid,” a M4 medium tank of Company C, 745th Tank Battalion, U.S. Army, drives through the entrance of the Aachen-Rothe Erde railroad station during the fighting around the city viaduct on Oct. 20, 1944. [Courtesy of First Division Museum/Daily Herald]
In 2002, TDI submitted a report to the U.S. Army Center for Army Analysis (CAA) on the first phase of a study examining the effects of combat in cities, or what was then called “military operations on urbanized terrain,” or MOUT. This first phase of a series of studies on urban warfare focused on the impact of urban terrain on division-level engagements and army-level operations, based on data drawn from TDI’s DuWar database suite.
This included engagements in France during 1944 including the Channel and Brittany port cities of Brest, Boulogne, Le Havre, Calais, and Cherbourg, as well as Paris, and the extended series of battles in and around Aachen in 1944. These were then compared to data on fighting in contrasting non-urban terrain in Western Europe in 1944-45.
The data appears to support a null hypothesis, that is, that the urban terrain had no significantly measurable influence on the outcome of battle.
The Effect of Urban Terrain on Casualties
Overall, any way the data is sectioned, the attacker casualties in the urban engagements are less than in the non-urban engagements and the casualty exchange ratio favors the attacker as well. Because of the selection of the data, there is some question whether these observations can be extended beyond this data, but it does not provide much support to the notion that urban combat is a more intense environment than non-urban combat.
The Effect of Urban Terrain on Advance Rates
It would appear that one of the primary effects of urban terrain is that it slows opposed advance rates. One can conclude that the average advance rate in urban combat should be one-half to one-third that of non-urban combat.
The Effect of Urban Terrain on Force Density
Overall, there is little evidence that combat operations in urban terrain result in a higher linear density of troops, although the data does seem to trend in that direction.
The Effect of Urban Terrain on Armor
Overall, it appears that armor losses in urban terrain are the same as, or lower than armor losses in non-urban terrain. And in some cases it appears that armor losses are significantly lower in urban than non-urban terrain.
The Effect of Urban Terrain on Force Ratios
Urban terrain did not significantly influence the force ratio required to achieve success or effectively conduct combat operations.
The Effect of Urban Terrain on Stress in Combat
Overall, it appears that urban terrain was no more stressful a combat environment during actual combat operations than was non-urban terrain.
The Effect of Urban Terrain on Logistics
Overall, the evidence appears to be that the expenditure of artillery ammunition in urban operations was not greater than that in non-urban operations. In the two cases where exact comparisons could be made, the average expenditure rates were about one-third to one-quarter the average expenditure rates expected for an attack posture in the European Theater of Operations as a whole.
The evidence regarding the expenditure of other types of ammunition is less conclusive, but again does not appear to be significantly greater than the expenditures in non-urban terrain. Expenditures of specialized ordnance may have been higher, but the total weight expended was a minor fraction of that for all of the ammunition expended.
There is no evidence that the expenditure of other consumable items (rations, water or POL) was significantly different in urban as opposed to non-urban combat.
The Effect of Urban Combat on Time Requirements
It was impossible to draw significant conclusions from the data set as a whole. However, in the five significant urban operations that were carefully studied, the maximum length of time required to secure the urban area was twelve days in the case of Aachen, followed by six days in the case of Brest. But the other operations all required little more than a day to complete (Cherbourg, Boulogne and Calais).
However, since it was found that advance rates in urban combat were significantly reduced, then it is obvious that these two effects (advance rates and time) are interrelated. It does appear that the primary impact of urban combat is to slow the tempo of operations.
This in turn leads to a hypothetical construct, where the reduced tempo of urban operations (reduced casualties, reduced opposed advance rates and increased time) compared to non-urban operations, results in two possible scenarios.
The first is if the urban area is bounded by non-urban terrain. In this case the urban area will tend to be enveloped during combat, since the pace of battle in the non-urban terrain is quicker. Thus, the urban battle becomes more a mopping-up operation, as it historically has usually been, rather than a full-fledged battle.
The alternate scenario is that created by an urban area that cannot be enveloped and must therefore be directly attacked. This may be caused by geography, as in a city on an island or peninsula, by operational requirements, as in the case of Cherbourg, Brest and the Channel Ports, or by political requirements, as in the case of Stalingrad, Suez City and Grozny.
Of course these last three cases are also those usually included as examples of combat in urban terrain that resulted in high casualty rates. However, all three of them had significant political requirements that influenced the nature, tempo and even the simple necessity of conducting the operation. And, in the case of Stalingrad and Suez City, significant geographical limitations effected the operations as well. These may well be better used to quantify the impact of political agendas on casualties, rather than to quantify the effects of urban terrain on casualties.
The effects of urban terrain at the operational level, and the effect of urban terrain on the tempo of operations, will be further addressed in Phase II of this study.
Consistent Scoring of Weapons and Aggregation of Forces: The Cornerstone of Dupuy’s Quantitative Analysis of Historical Land Battles by
James G. Taylor, PhD,
Dept. of Operations Research, Naval Postgraduate School
Introduction
Col. Trevor N. Dupuy was an American original, especially as regards the quantitative study of warfare. As with many prophets, he was not entirely appreciated in his own land, particularly its Military Operations Research (OR) community. However, after becoming rather familiar with the details of his mathematical modeling of ground combat based on historical data, I became aware of the basic scientific soundness of his approach. Unfortunately, his documentation of methodology was not always accepted by others, many of whom appeared to confuse lack of mathematical sophistication in his documentation with lack of scientific validity of his basic methodology.
The purpose of this brief paper is to review the salient points of Dupuy’s methodology from a system’s perspective, i.e., to view his methodology as a system, functioning as an organic whole to capture the essence of past combat experience (with an eye towards extrapolation into the future). The advantage of this perspective is that it immediately leads one to the conclusion that if one wants to use some functional relationship derived from Dupuy’s work, then one should use his methodologies for scoring weapons, aggregating forces, and adjusting for operational circumstances; since this consistency is the only guarantee of being able to reproduce historical results and to project them into the future.
Implications (of this system’s perspective on Dupuy’s work) for current DOD models will be discussed. In particular, the Military OR community has developed quantitative methods for imputing values to weapon systems based on their attrition capability against opposing forces and force interactions.[1] One such approach is the so-called antipotential-potential method[2] used in TACWAR[3] to score weapons. However, one should not expect such scores to provide valid casualty estimates when combined with historically derived functional relationships such as the so-called ATLAS casualty-rate curves[4] used in TACWAR, because a different “yard-stick” (i.e. measuring system for estimating the relative combat potential of opposing forces) was used to develop such a curve.
Overview of Dupuy’s Approach
This section briefly outlines the salient features of Dupuy’s approach to the quantitative analysis and modeling of ground combat as embodied in his Tactical Numerical Deterministic Model (TNDM) and its predecessor the Quantified Judgment Model (QJM). The interested reader can find details in Dupuy [1979] (see also Dupuy [1985][5], [1987], [1990]). Here we will view Dupuy’s methodology from a system approach, which seeks to discern its various components and their interactions and to view these components as an organic whole. Essentially Dupuy’s approach involves the development of functional relationships from historical combat data (see Fig. 1) and then using these functional relationships to model future combat (see Fig, 2).
At the heart of Dupuy’s method is the investigation of historical battles and comparing the relationship of inputs (as quantified by relative combat power, denoted as Pa/Pd for that of the attacker relative to that of the defender in Fig. l)(e.g. see Dupuy [1979, pp. 59-64]) to outputs (as quantified by extent of mission accomplishment, casualty effectiveness, and territorial effectiveness; see Fig. 2) (e.g. see Dupuy [1979, pp. 47-50]), The salient point is that within this scheme, the main input[6] (i.e. relative combat power) to a historical battle is a derived quantity. It is computed from formulas that involve three essential aspects: (1) the scoring of weapons (e.g, see Dupuy [1979, Chapter 2 and also Appendix A]), (2) aggregation methodology for a force (e.g. see Dupuy [1979, pp. 43-46 and 202-203]), and (3) situational-adjustment methodology for determining the relative combat power of opposing forces (e.g. see Dupuy [1979, pp. 46-47 and 203-204]). In the force-aggregation step the effects on weapons of Dupuy’s environmental variables and one operational variable (air superiority) are considered[7], while in the situation-adjustment step the effects on forces of his behavioral variables[8] (aggregated into a single factor called the relative combat effectiveness value (CEV)) and also the other operational variables are considered (Dupuy [1987, pp. 86-89])
Figure 1.
Moreover, any functional relationships developed by Dupuy depend (unless shown otherwise) on his computational system for derived quantities, namely OLls, force strengths, and relative combat power. Thus, Dupuy’s results depend in an essential manner on his overall computational system described immediately above. Consequently, any such functional relationship (e.g. casualty-rate curve) directly or indirectly derivative from Dupuy‘s work should still use his computational methodology for determination of independent-variable values.
Fig l also reveals another important aspect of Dupuy’s work, the development of reliable data on historical battles, Military judgment plays an essential role in this development of such historical data for a variety of reasons. Dupuy was essentially the only source of new secondary historical data developed from primary sources (see McQuie [1970] for further details). These primary sources are well known to be both incomplete and inconsistent, so that military judgment must be used to fill in the many gaps and reconcile observed inconsistencies. Moreover, military judgment also generates the working hypotheses for model development (e.g. identification of significant variables).
At the heart of Dupuy’s quantitative investigation of historical battles and subsequent model development is his own weapons-scoring methodology, which slowly evolved out of study efforts by the Historical Evaluation Research Organization (HERO) and its successor organizations (cf. HERO [1967] and compare with Dupuy [1979]). Early HERO [1967, pp. 7-8] work revealed that what one would today call weapons scores developed by other organizations were so poorly documented that HERO had to create its own methodology for developing the relative lethality of weapons, which eventually evolved into Dupuy’s Operational Lethality Indices (OLIs). Dupuy realized that his method was arbitrary (as indeed is its counterpart, called the operational definition, in formal scientific work), but felt that this would be ameliorated if the weapons-scoring methodology be consistently applied to historical battles. Unfortunately, this point is not clearly stated in Dupuy’s formal writings, although it was clearly (and compellingly) made by him in numerous briefings that this author heard over the years.
Figure 2.
In other words, from a system’s perspective, the functional relationships developed by Colonel Dupuy are part of his analysis system that includes this weapons-scoring methodology consistently applied (see Fig. l again). The derived functional relationships do not stand alone (unless further empirical analysis shows them to hold for any weapons-scoring methodology), but function in concert with computational procedures. Another essential part of this system is Dupuy‘s aggregation methodology, which combines numbers, environmental circumstances, and weapons scores to compute the strength (S) of a military force. A key innovation by Colonel Dupuy [1979, pp. 202- 203] was to use a nonlinear (more precisely, a piecewise-linear) model for certain elements of force strength. This innovation precluded the occurrence of military absurdities such as air firepower being fully substitutable for ground firepower, antitank weapons being fully effective when armor targets are lacking, etc‘ The final part of this computational system is Dupuy’s situational-adjustment methodology, which combines the effects of operational circumstances with force strengths to determine relative combat power, e.g. Pa/Pd.
To recapitulate, the determination of an Operational Lethality Index (OLI) for a weapon involves the combination of weapon lethality, quantified in terms of a Theoretical Lethality Index (TLI) (e.g. see Dupuy [1987, p. 84]), and troop dispersion[9] (e.g. see Dupuy [1987, pp. 84- 85]). Weapons scores (i.e. the OLIs) are then combined with numbers (own side and enemy) and combat- environment factors to yield force strength. Six[10] different categories of weapons are aggregated, with nonlinear (i.e. piecewise-linear) models being used for the following three categories of weapons: antitank, air defense, and air firepower (i.e. c1ose—air support). Operational, e.g. mobility, posture, surprise, etc. (Dupuy [1987, p. 87]), and behavioral variables (quantified as a relative combat effectiveness value (CEV)) are then applied to force strength to determine a side’s combat-power potential.
Requirement for Consistent Scoring of Weapons, Force Aggregation, and Situational Adjustment for Operational Circumstances
The salient point to be gleaned from Fig.1 and 2 is that the same (or at least consistent) weapons—scoring, aggregation, and situational—adjustment methodologies be used for both developing functional relationships and then playing them to model future combat. The corresponding computational methods function as a system (organic whole) for determining relative combat power, e.g. Pa/Pd. For the development of functional relationships from historical data, a force ratio (relative combat power of the two opposing sides, e.g. attacker’s combat power divided by that of the defender, Pa/Pd is computed (i.e. it is a derived quantity) as the independent variable, with observed combat outcome being the dependent variable. Thus, as discussed above, this force ratio depends on the methodologies for scoring weapons, aggregating force strengths, and adjusting a force’s combat power for the operational circumstances of the engagement. It is a priori not clear that different scoring, aggregation, and situational-adjustment methodologies will lead to similar derived values. If such different computational procedures were to be used, these derived values should be recomputed and the corresponding functional relationships rederived and replotted.
However, users of the Tactical Numerical Deterministic Model (TNDM) (or for that matter, its predecessor, the Quantified Judgment Model (QJM)) need not worry about this point because it was apparently meticulously observed by Colonel Dupuy in all his work. However, portions of his work have found their way into a surprisingly large number of DOD models (usually not explicitly acknowledged), but the context and range of validity of historical results have been largely ignored by others. The need for recalibration of the historical data and corresponding functional relationships has not been considered in applying Dupuy’s results for some important current DOD models.
Implications for Current DOD Models
A number of important current DOD models (namely, TACWAR and JICM discussed below) make use of some of Dupuy’s historical results without recalibrating functional relationships such as loss rates and rates of advance as a function of some force ratio (e.g. Pa/Pd). As discussed above, it is not clear that such a procedure will capture the essence of past combat experience. Moreover, in calculating losses, Dupuy first determines personnel losses (expressed as a percent loss of personnel strength, i.e., number of combatants on a side) and then calculates equipment losses as a function of this casualty rate (e.g., see Dupuy [1971, pp. 219-223], also [1990, Chapters 5 through 7][11]). These latter functional relationships are apparently not observed in the models discussed below. In fact, only Dupuy (going back to Dupuy [1979][12] takes personnel losses to depend on a force ratio and other pertinent variables, with materiel losses being taken as derivative from this casualty rate.
For example, TACWAR determines personnel losses[13] by computing a force ratio and then consulting an appropriate casualty-rate curve (referred to as empirical data), much in the same fashion as ATLAS did[14]. However, such a force ratio is computed using a linear model with weapon values determined by the so-called antipotential-potential method[15]. Unfortunately, this procedure may not be consistent with how the empirical data (i.e. the casualty-rate curves) was developed. Further research is required to demonstrate that valid casualty estimates are obtained when different weapon scoring, aggregation, and situational-adjustment methodologies are used to develop casualty-rate curves from historical data and to use them to assess losses in aggregated combat models. Furthermore, TACWAR does not use Dupuy’s model for equipment losses (see above), although it does purport, as just noted above, to use “historical data” (e.g., see Kerlin et al. [1975, p. 22]) to compute personnel losses as a function (among other things) of a force ratio (given by a linear relationship), involving close air support values in a way never used by Dupuy. Although their force-ratio determination methodology does have logical and mathematical merit, it is not the way that the historical data was developed.
Moreover, RAND (Allen [1992]) has more recently developed what is called the situational force scoring (SFS) methodology for calculating force ratios in large-scale, aggregated-force combat situations to determine loss and movement rates. Here, SFS refers essentially to a force- aggregation and situation-adjustment methodology, which has many conceptual elements in common with Dupuy‘s methodology (except, most notably, extensive testing against historical data, especially documentation of such efforts). This SFS was originally developed for RSAS[16] and is today used in JICM[17]. It also apparently uses a weapon-scoring system developed at RAND[18]. It purports (no documentation given [citation of unpublished work]) to be consistent with historical data (including the ATLAS casualty-rate curves) (Allen [1992, p.41]), but again no consideration is given to recalibration of historical results for different weapon scoring, force-aggregation, and situational-adjustment methodologies. SFS emphasizes adjusting force strengths according to operational circumstances (the “situation”) of the engagement (including surprise), with many innovative ideas (but in some major ways has little connection with previous work of others[19]). The resulting model contains many more details than historical combat data would support. It also is methodology that differs in many essential ways from that used previously by any investigator. In particular, it is doubtful that it develops force ratios in a manner consistent with Dupuy’s work.
Final Comments
Use of (sophisticated) mathematics for modeling past historical combat (and extrapolating it into the future for planning purposes) is no reason for ignoring Dupuy’s work. One would think that the current Military OR community would try to understand Dupuy’s work before trying to improve and extend it. In particular, Colonel Dupuy’s various computational procedures (including constants) must be considered as an organic whole (i.e. a system) supporting the development of functional relationships. If one ignores this computational system and simply tries to use some isolated aspect, the result may be interesting and even logically sound, but it probably lacks any scientific validity.
REFERENCES
P. Allen, “Situational Force Scoring: Accounting for Combined Arms Effects in Aggregate Combat Models,” N-3423-NA, The RAND Corporation, Santa Monica, CA, 1992.
L. B. Anderson, “A Briefing on Anti-Potential Potential (The Eigen-value Method for Computing Weapon Values), WP-2, Project 23-31, Institute for Defense Analyses, Arlington, VA, March 1974.
B. W. Bennett, et al, “RSAS 4.6 Summary,” N-3534-NA, The RAND Corporation, Santa Monica, CA, 1992.
B. W. Bennett, A. M. Bullock, D. B. Fox, C. M. Jones, J. Schrader, R. Weissler, and B. A. Wilson, “JICM 1.0 Summary,” MR-383-NA, The RAND Corporation, Santa Monica, CA, 1994.
P. K. Davis and J. A. Winnefeld, “The RAND Strategic Assessment Center: An Overview and Interim Conclusions About Utility and Development Options,” R-2945-DNA, The RAND Corporation, Santa Monica, CA, March 1983.
T.N, Dupuy, Numbers. Predictions and War: Using History to Evaluate Combat Factors and Predict the Outcome of Battles, The Bobbs-Merrill Company, Indianapolis/New York, 1979,
T.N. Dupuy, Numbers Predictions and War, Revised Edition, HERO Books, Fairfax, VA 1985.
T.N. Dupuy, Understanding War: History and Theory of Combat, Paragon House Publishers, New York, 1987.
T.N. Dupuy, Attrition: Forecasting Battle Casualties and Equipment Losses in Modem War, HERO Books, Fairfax, VA, 1990.
General Research Corporation (GRC), “A Hierarchy of Combat Analysis Models,” McLean, VA, January 1973.
Historical Evaluation and Research Organization (HERO), “Average Casualty Rates for War Games, Based on Historical Data,” 3 Volumes in 1, Dunn Loring, VA, February 1967.
E. P. Kerlin and R. H. Cole, “ATLAS: A Tactical, Logistical, and Air Simulation: Documentation and User’s Guide,” RAC-TP-338, Research Analysis Corporation, McLean, VA, April 1969 (AD 850 355).
E.P. Kerlin, L.A. Schmidt, A.J. Rolfe, M.J. Hutzler, and D,L. Moody, “The IDA Tactical Warfare Model: A Theater-Level Model of Conventional, Nuclear, and Chemical Warfare, Volume II- Detailed Description” R-21 1, Institute for Defense Analyses, Arlington, VA, October 1975 (AD B009 692L).
R. McQuie, “Military History and Mathematical Analysis,” Military Review 50, No, 5, 8-17 (1970).
S.M. Robinson, “Shadow Prices for Measures of Effectiveness, I: Linear Model,” Operations Research 41, 518-535 (1993).
J.G. Taylor, Lanchester Models of Warfare. Vols, I & II. Operations Research Society of America, Alexandria, VA, 1983. (a)
J.G. Taylor, “A Lanchester-Type Aggregated-Force Model of Conventional Ground Combat,” Naval Research Logistics Quarterly 30, 237-260 (1983). (b)
NOTES
[1] For example, see Taylor [1983a, Section 7.18], which contains a number of examples. The basic references given there may be more accessible through Robinson [I993].
[2] This term was apparently coined by L.B. Anderson [I974] (see also Kerlin et al. [1975, Chapter I, Section D.3]).
[3] The Tactical Warfare (TACWAR) model is a theater-level, joint-warfare, computer-based combat model that is currently used for decision support by the Joint Staff and essentially all CINC staffs. It was originally developed by the Institute for Defense Analyses in the mid-1970s (see Kerlin et al. [1975]), originally referred to as TACNUC, which has been continually upgraded until (and including) the present day.
[4] For example, see Kerlin and Cole [1969], GRC [1973, Fig. 6-6], or Taylor [1983b, Fig. 5] (also Taylor [1983a, Section 7.13]).
[5] The only apparent difference between Dupuy [1979] and Dupuy [1985] is the addition of an appendix (Appendix C “Modified Quantified Judgment Analysis of the Bekaa Valley Battle”) to the end of the latter (pp. 241-251). Hence, the page content is apparently the same for these two books for pp. 1-239.
[6] Technically speaking, one also has the engagement type and possibly several other descriptors (denoted in Fig. 1 as reduced list of operational circumstances) as other inputs to a historical battle.
[7] In Dupuy [1979, e.g. pp. 43-46] only environmental variables are mentioned, although basically the same formulas underlie both Dupuy [1979] and Dupuy [1987]. For simplicity, Fig. 1 and 2 follow this usage and employ the term “environmental circumstances.”
[8] In Dupuy [1979, e.g. pp. 46-47] only operational variables are mentioned, although basically the same formulas underlie both Dupuy [1979] and Dupuy [1987]. For simplicity, Fig. 1 and 2 follow this usage and employ the term “operational circumstances.”
[9] Chris Lawrence has kindly brought to my attention that since the same value for troop dispersion from an historical period (e.g. see Dupuy [1987, p. 84]) is used for both the attacker and also the defender, troop dispersion does not actually affect the determination of relative combat power PM/Pd.
[10] Eight different weapon types are considered, with three being classified as infantry weapons (e.g. see Dupuy [1979, pp, 43-44], [1981 pp. 85-86]).
[11] Chris Lawrence has kindly informed me that Dupuy‘s work on relating equipment losses to personnel losses goes back to the early 1970s and even earlier (e.g. see HERO [1966]). Moreover, Dupuy‘s [1992] book Future Wars gives some additional empirical evidence concerning the dependence of equipment losses on casualty rates.
[12] But actually going back much earlier as pointed out in the previous footnote.
[13] See Kerlin et al. [1975, Chapter I, Section D.l].
[14] See Footnote 4 above.
[15] See Kerlin et al. [1975, Chapter I, Section D.3]; see also Footnotes 1 and 2 above.
[16] The RAND Strategy Assessment System (RSAS) is a multi-theater aggregated combat model developed at RAND in the early l980s (for further details see Davis and Winnefeld [1983] and Bennett et al. [1992]). It evolved into the Joint Integrated Contingency Model (JICM), which is a post-Cold War redesign of the RSAS (starting in FY92).
[17] The Joint Integrated Contingency Model (JICM) is a game-structured computer-based combat model of major regional contingencies and higher-level conflicts, covering strategic mobility, regional conventional and nuclear warfare in multiple theaters, naval warfare, and strategic nuclear warfare (for further details, see Bennett et al. [1994]).
[18] RAND apparently replaced one weapon-scoring system by another (e.g. see Allen [1992, pp. 9, l5, and 87-89]) without making any other changes in their SFS System.
[19] For example, both Dupuy’s early HERO work (e.g. see Dupuy [1967]), reworks of these results by the Research Analysis Corporation (RAC) (e.g. see RAC [1973, Fig. 6-6]), and Dupuy’s later work (e.g. see Dupuy [1979]) considered daily fractional casualties for the attacker and also for the defender as basic casualty-outcome descriptors (see also Taylor [1983b]). However, RAND does not do this, but considers the defender’s loss rate and a casualty exchange ratio as being the basic casualty-production descriptors (Allen [1992, pp. 41-42]). The great value of using the former set of descriptors (i.e. attacker and defender fractional loss rates) is that not only is casualty assessment more straight forward (especially development of functional relationships from historical data) but also qualitative model behavior is readily deduced (see Taylor [1983b] for further details).
Between 2001 and 2004, TDI undertook a series of studies on the effects of urban combat in cities for the U.S. Army Center for Army Analysis (CAA). These studies examined a total of 304 cases of urban combat at the divisional and battalion level that occurred between 1942 and 2003, as well as 319 cases of concurrent non-urban combat for comparison.
Our 
