Jeffrey H Michaels, a Senior Lecturer in Defence Studies at the British the Joint Services Command and Staff College, has published a detailed look at how Hackett and several senior NATO and diplomatic colleagues constructed the scenario portrayed in the book. Scenario construction is an important aspect of institutional war gaming. A war game will only be as useful if the assumptions that underpin it are valid. As Michaels points out,
Regrettably, far too many scenarios and models, whether developed by military organizations, political scientists, or fiction writers, tend to focus their attention on the battlefield and the clash of armies, navies, air forces, and especially their weapons systems. By contrast, the broader context of the war – the reasons why hostilities erupted, the political and military objectives, the limits placed on military action, and so on – are given much less serious attention, often because they are viewed by the script-writers as a distraction from the main activity that occurs on the battlefield.
Modelers and war gamers always need to keep in mind the fundamental importance of context in designing their simulations.
It is quite easy to project how one weapon system might fare against another, but taken out of a broader strategic context, such a projection is practically meaningless (apart from its marketing value), or worse, misleading. In this sense, even if less entertaining or exciting, the degree of realism of the political aspects of the scenario, particularly policymakers’ rationality and cost-benefit calculus, and the key decisions that are taken about going to war, the objectives being sought, the limits placed on military action, and the willingness to incur the risks of escalation, should receive more critical attention than the purely battlefield dimensions of the future conflict.
These are crucially important points to consider when deciding how to asses the outcomes of hypothetical scenarios.
This is like saying, “A team can’t score in football unless it has the ball.” Although subsequent verities stress the strength, value, and importance of defense, this should not obscure the essentiality of offensive action to ultimate combat success. Even in instances where a defensive strategy might conceivably assure a favorable war outcome—as was the case of the British against Napoleon, and as the Confederacy attempted in the American Civil War—selective employment of offensive tactics and operations is required if the strategic defender is to have any chance of final victory. [pp. 1-2]
Only offensive action achieves decisive results. Offensive action permits the commander to exploit the initiative and impose his will on the enemy. The defensive may be forced on the commander, but it should be deliberately adopted only as a temporary expedient while awaiting an opportunity for offensive action or for the purpose of economizing forces on a front where a decision is not sought. Even on the defensive the commander seeks every opportunity to seize the initiative and achieve decisive results by offensive action. [Original emphasis]
Interestingly enough, the offensive no longer retains its primary place in current Army doctrinal thought. The Army consigned its list of the principles of war to an appendix in the 2008 edition of FM 3-0 Operations and omitted them entirely from the 2017 revision. As the current edition of FM 3-0 Operations lays it out, the offensive is now placed on the same par as the defensive and stability operations:
Unified land operations are simultaneous offensive, defensive, and stability or defense support of civil authorities’ tasks to seize, retain, and exploit the initiative to shape the operational environment, prevent conflict, consolidate gains, and win our Nation’s wars as part of unified action (ADRP 3-0)…
At the heart of the Army’s operational concept is decisive action. Decisive action is the continuous, simultaneous combinations of offensive, defensive, and stability or defense support of civil authorities tasks (ADRP 3-0). During large-scale combat operations, commanders describe the combinations of offensive, defensive, and stability tasks in the concept of operations. As a single, unifying idea, decisive action provides direction for an entire operation. [p. I-16; original emphasis]
It is perhaps too easy to read too much into this change in emphasis. On the very next page, FM 3-0 describes offensive “tasks” thusly:
Offensive tasks are conducted to defeat and destroy enemy forces and seize terrain, resources, and population centers. Offensive tasks impose the commander’s will on the enemy. The offense is the most direct and sure means of seizing and exploiting the initiative to gain physical and cognitive advantages over an enemy. In the offense, the decisive operation is a sudden, shattering action that capitalizes on speed, surprise, and shock effect to achieve the operation’s purpose. If that operation does not destroy or defeat the enemy, operations continue until enemy forces disintegrate or retreat so they no longer pose a threat. Executing offensive tasks compels an enemy to react, creating or revealing additional weaknesses that an attacking force can exploit. [p. I-17]
The change in emphasis likely reflects recent U.S. military experience where decisive action has not yielded much in the way of decisive outcomes, as is mentioned in FM 3-0’s introduction. Joint force offensives in 2001 and 2003 “achieved rapid initial military success but no enduring political outcome, resulting in protracted counterinsurgency campaigns.” The Army now anticipates a future operating environment where joint forces can expect to “work together and with unified action partners to successfully prosecute operations short of conflict, prevail in large-scale combat operations, and consolidate gains to win enduring strategic outcomes” that are not necessarily predicated on offensive action alone. We may have to wait for the next edition of FM 3-0 to see if the Army has drawn valid conclusions from the recent past or not.
It is my understanding that he was the person who was responsible for making sure that DACM (Dupuy Air Combat Model) was funded by AFSC. He then retired from the Air Force in 1994. We completed the demonstration phase of the DACM and quite simply, there was no one left in the Air Force who was interested in funding it. So, work stopped. I never met General McInerney and was not involved in the marketing of the initial effort.
But, this is typical of the problems with doing business with the Pentagon, where an officer will take an interest in your work, generate funding for it, but by the time the first steps are completed, that officer has moved on to another assignment. This has happened to us with other projects. One of these efforts was a joint research project that was done by TDI and former Army surgeon on casualty rates. It was for J-4 of the Joint Staff. The project officer there was extremely interested and involved in the work, but then moved to another assignment. By the time we got original effort completed, the division was headed by an Air Force Colonel who appeared to be only interested in things that flew. Therefore, the project died (except that parts of it were used for Chapter 15: Casualties, pages 193-198, in War by Numbers).
Our experience in dealing with the U.S. defense establishment is that sometimes research efforts that takes longer than a few months will die……because the people interested in it have moved on. This sometimes leads to simple, short-term analysis and fewer properly funded long-term projects.
Last night, as I was watching the Capitals trash Tampa Bay in the third round of the Stanley Cup play-offs….the announcer mentioned that only twice in the last 41 years (or cases) has a team won the third round of the play-offs after loosing the first two games. Tampa Bay was lost the first two games. So, historically, in only 4.878% (say 5%) of the cases has someone come back from loosing the first two play-off games to win. Note that the Capitals did this against Columbus in the first round.
Now, there are seven games in a play-off round. So with five games left, Tampa Bay has to win 4 of the 5 games. So, assuming the teams are equal (50% chance of either winning a game), then the odds of Tampa Bay winning 4 of the next 5 games I calculate as .09375 (mathematicians…please check me on this) or 9%. So if the teams are equal, then Tampa Bay should statistically have a 9% chance of coming back and winning the round. Historically, it has only happened 5% of the time.
Suppose Tampa Bay is the better team. Lets say their odds of winning are 60% for each game, then their odds of winning 4 out of 5 rises to 18.144%. They did win 2 out of 3 games against the Capitals in the regular season, so maybe their odds of winning any single game is really 67%. This is a 26.34% chance of coming back. Let us say they are really good and motivated and have a 75% of winning each game, then the odds are 39.55%. On the other hand, to get to the historical 5% win rate in this situation, then team that is behind had to have around a 42% chance of winning each game.
[This post was originally published on 3 April 2017.]
Displayed across the top of my book is the phrase “Largest Tank Battle in History.” Apparently some people dispute that.
What they put forth as the largest tank battle in history is the Battle of Brody in 23-30 June 1941. This battle occurred right at the start of the German invasion of the Soviet Union and consisted of two German corps attacking five Soviet corps in what is now Ukraine. This rather confused affair pitted between 750 to 1,000 German tanks against 3,500 to 5,000 Soviet tanks. Only 3,000 Soviet tanks made it to the battlefield according to Glantz (see video at 16:00). The German won with losses of around a 100 to 200 tanks. Sources vary on this, and I have not taken the time to sort this out (so many battles, so little time). So, total tanks involved are from 3,750 to up to 6,000, with the lower figure appearing to be more correct.
Now, is this really a larger tank battle than the Battle of Kursk? My book covers only the southern part of the German attack that started on 4 July and ended 17 July. This offensive involved five German corps (including three Panzer corps consisting of nine panzer and panzer grenadier divisions) and they faced seven Soviet Armies (including two tank armies and a total of ten tank and mechanized corps).
My tank counts for the southern attack staring 4 July 1943 was 1,707 German tanks (1,709 depending if you count the two Panthers that caught fire on the move up there). The Soviets at 4 July in the all formations that would eventually get involved has 2,775 tanks with 1,664 tanks in the Voronezh Front at the start of the battle. Our count of total committed tanks is slightly higher, 1,749 German and 2,978 Soviet. This includes tanks that were added during the two weeks of battle and mysterious adjustments to strength figures that we cannot otherwise explain. This is 4,482 or 4,727 tanks. So depending on which Battle of Brody figures being used, and whether all the Soviet tanks were indeed ready-for-action and committed to the battle, then the Battle of Brody might be larger than the attack in the southern part of the Kursk salient. On the other hand, it probably is not.
But, this was just one part of the Battle of Kursk. To the north was the German attack from the Orel salient that was about two-thirds the size of the attack in the south. It consisted of the Ninth Army with five corps and six German panzer divisions. This offensive fizzled at the Battle of Ponyiri on 12 July.
The third part to the Battle of Kursk began on 12 July the Western and Bryansk Fronts launched an offensive on the north side of the Orel salient. A Soviet Front is equivalent to an army group and this attack initially consisted of five armies and included four Soviet tank corps. This was a major attack that added additional forces as it developed and went on until 23 August.
The final part of the Battle of Kursk was the counter-offensive in the south by Voronezh, Southwestern and Steppe Fronts that started on 3 August, took Kharkov and continued until 23 August. The Soviet forces involved here were larger than the forces involved in the original defensive effort, with the Voronezh Front now consisting of eight armies, the Steppe Front consisting of three armies, and there being one army contributed by the Southwestern Front to this attack.
The losses in these battles were certainly more significant for the Germans than at the Battle of Brody. For example, in the southern offensive by our count the Germans lost 1,536 tanks destroyed, damaged or broken down. The Soviets lost 2,471 tanks destroyed, damaged or broken down. This compares to 100-200 German tanks lost at Brody and the Soviet tank losses are even more nebulous, but the figure of 2,648 has been thrown out there.
So, total tanks involved in the German offensive in the south were 4,482 or 4,727 and this was just one of four parts of the Battle of Kursk. Losses were higher than for Brody (and much higher for the Germans). Obviously, the Battle of Kursk was a larger tank battle than the Battle of Brody.
What some people are comparing the Battle of Brody to is the Battle of Prokhorovka. This was a one- to five-day event during the German offensive in the south that included the German SS Panzer Corps and in some people’s reckoning, all of the III Panzer Corps and the 11th Panzer Division from the XLVIII Panzer Corps. So, the Battle of Brody may well be a larger tank battle than the Battle of Prokhorovka, but it was not a larger tank battle than the Battle of Kursk. I guess it depends all in how you define the battles.
“…war alone cannot account for the vast number of Afghan migrants or the great distance they are travelling.”
“Globally, up until 1960, the ratio of refuges to fatalities in conflict zones was below 5:1.”
“…in 2015 there was an almost unprecedented 50 as asylum applicants for every civilian killed.”
“Whereas in 1979 over 90% of the Afghan refugees travelled less than 500 km and cross one border, now more than 90% travel over 5,000 km to seek asylum…”
“There are now 1.3 million internally displaced Afghans, with the total increasing by 400,000 a year.”
“The pull of economic opportunity plays a large part in the decision to migrate.”
“In 2015, the population of Afghanistan was 32 million.”
“…it is nonetheless obliged to import enough wheat to feed 10 million people…”
“…Afghanistan’s population will pass 40 million in ten years.”
“the natural growth rate of 2.3% a year added 700,000 to the Afghan population in 2015.”
“Unless there is a dramatic improvement in the economy and security in that time, 16 million will depend on food aid…”
Zentralbild, II. Weltkrieg 19139-45 Der von der faschistischen deutschen Wehrmacht während des Krieges entwickelte neue Panzerkampfwagen Typ “Panther”. UBz: die Verladung neuer “Panther”-Panzerkampfwagen zum Transport an die Front (1943).
The Voice of America (VOA) interviewed me about Kursk and the current Russian Army for some articles they were working on. The interviewer, Alex Grigoryev, was a journalist in Russia before he immigrated to the United States. The first interview, on Kursk, is on video here, with me speaking in English with Russian subtitles: https://www.golos-ameriki.ru/a/ag-kursk-battle-book-of-cristopher-lawrance/4384650.html
A few things I would change, but I don’t think I completely embarrassed myself.
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).
It appears that the Army has lowered its recruiting goals for 2018. In the first six months of the recruiting year, they brought in only 28,000 new soldiers. The goal for the year was 80,000. The overall goal is to grow the Army to 483,500. They have been able to maintain strength by retaining current soldiers (86% retention, compared to 81% in past years). Of course, the problem is the strong economy reduced recruits and “the declining quality of the youth market.”
What the article does not state is that there is a limit to how long they can maintain the force through retention. At some point, they need to recruit more.
There is one interesting statement towards the end of the article that gets my attention: “Defense officials have also complained that despite the last 16 years of war in Afghanistan, Iraq and Syria, the American public is increasingly disconnected from the military, and they say many people have misperceptions about serving and often don’t personally know any service members.”
Back in 2003-2005 we did some contracts for the state of Pennsylvania in preparation for the upcoming round of base closures. This was not our normal line of business, but some people who knew us contacted us and asked if we could help. It then got weird, because some people in Pennsylvania wondered why they were using a historical think-tank for this as opposed to all their politically connected lobbyists and consultants. So, they replaced us, except for Pittsburg, who independently maintained us as a contractor (The Military Affairs Council of Western Pennsylvania) . The end result was that all the bases targeted for closure in Eastern Pennsylvania were shut down, but Pittsburg managed to justify and keep their bases open (for the time being).
Anyhow, one of the arguments I was developing for Pennsylvania is that the U.S. military needed to maintain a presence in the Northeastern United States. As we pointed out in our first report we did in 2003 for the “Pennsylvania Department of Community and Economic Development Base Retention and Conversion-Pennsylvania Action Committee”:
As of 31 March 1943 New York, Pennsylvania and New Jersey were among the top ten states in terms of War Department civilian employment….By fiscal year 2001 no Northeastern Sates was in the top ten in terms of Army and Air Force civilian employment….One side effect of Department of Defense downsizing and the BRAC process has been a continued shrinkage of the presence of the United States Armed Forces in the Northeastern U.S. and concurrent with that, the representation of Northeasterners in the Armed Forces….The U.S. Armed Forces is in danger of being transformed from a truly national force to a force with unusually strong regional representations: with a significant portion of the U.S. Armed Forces based, oriented and recruited from the Southeast. The Dupuy Institute does not believe that these trends are healthy, either for the nation as a whole or for the Armed Forces themselves.
Just to drive home point:
The Northeast has always played a significant part in the defense of the United States. Some of America’s most famous military figures have come from the region…and yet, since World War II it appears that this pattern has shifted. For example an examination of the biographies of nineteen of the senior commanders in the U.S. military show that now only two are from the Northeast, and this from a region that constitutes one-fifth of the population of the United States….Currently (as of 2000) only 14.6 percent of all personnel recruited annually in the U.S. military are from the Northeast.
And listed under possible reasons for this shift in participation:
A stronger economy in the Northeast. U.S. military recruiting tends to be more successful in those area that have a lower per capita income. The Northeast has historically been one of the wealthiest areas of the U.S.
A lack of major U.S. military presence in the Northeast…..They all reduce the visibility of the military in the region relative to other regions of the U.S.
A lack of military families in the Northeast…..And since volunteers for military service often come from military families the reduced presence of the military in the Northeast has probably led to a decline in recruitment from the region…
Cultural differences. For a variety of personal, political and economic reasons the citizens of the Northeast may be less likely to join the military.
Anyhow, this is part of a larger concern that I have had with our all-volunteer military becoming increasing regionally based and not being representative of the United States population as a whole.
This map is from 2003.