Category Modeling, Simulation & Wargaming

Assessing The Assessments Of The Military Balance In The China Seas

“If we maintain our faith in God, love of freedom, and superior global airpower, the future [of the US] looks good.” — U.S. Air Force General Curtis E. LeMay (Commander, U.S. Strategic Command, 1948-1957)

Curtis LeMay was involved in the formation of RAND Corporation after World War II. RAND created several models to measure the dynamics of the US-China military balance over time. Since 1996, this has been computed for two scenarios, differing by range from mainland China: one over Taiwan and the other over the Spratly Islands. The results of the model results for selected years can be seen in the graphic below.

The capabilities listed in the RAND study are interesting, notable in that the air superiority category, rough parity exists as of 2017. Also, the ability to attack air bases has given an advantage to the Chinese forces.

Investigating the methodology used does not yield any precise quantitative modeling examples, as would be expected in a rigorous academic effort, although there is some mention of statistics, simulation and historical examples.

The analysis presented here necessarily simplifies a great number of conflict characteristics. The emphasis throughout is on developing and assessing metrics in each area that provide a sense of the level of difficulty faced by each side in achieving its objectives. Apart from practical limitations, selectivity is driven largely by the desire to make the work transparent and replicable. Moreover, given the complexities and uncertainties in modern warfare, one could make the case that it is better to capture a handful of important dynamics than to present the illusion of comprehensiveness and precision. All that said, the analysis is grounded in recognized conclusions from a variety of historical sources on modern warfare, from the air war over Korea and Vietnam to the naval conflict in the Falklands and SAM hunting in Kosovo and Iraq. [Emphasis added].

We coded most of the scorecards (nine out of ten) using a five-color stoplight scheme to denote major or minor U.S. advantage, a competitive situation, or major or minor Chinese advantage. Advantage, in this case, means that one side is able to achieve its primary objectives in an operationally relevant time frame while the other side would have trouble in doing so. [Footnote] For example, even if the U.S. military could clear the skies of Chinese escort fighters with minimal friendly losses, the air superiority scorecard could be coded as “Chinese advantage” if the United States cannot prevail while the invasion hangs in the balance. If U.S. forces cannot move on to focus on destroying attacking strike and bomber aircraft, they cannot contribute to the larger mission of protecting Taiwan.

All of the dynamic modeling methodology (which involved a mix of statistical analysis, Monte Carlo simulation, and modified Lanchester equations) is publicly available and widely used by specialists at U.S. and foreign civilian and military universities.” [Emphasis added].

As TDI has contended before, the problem with using Lanchester’s equations is that, despite numerous efforts, no one has been able to demonstrate that they accurately represent real-world combat. So, even with statistics and simulation, how good are the results if they have relied on factors or force ratios with no relation to actual combat?

What about new capabilities?

As previously posted, the Kratos Mako Unmanned Combat Aerial Vehicle (UCAV), marketed as the “unmanned wingman,” has recently been cleared for export by the U.S. State Department. This vehicle is specifically oriented towards air-to-air combat, is stated to have unparalleled maneuverability, as it need not abide by limits imposed by human physiology. The Mako “offers fighter-like performance and is designed to function as a wingman to manned aircraft, as a force multiplier in contested airspace, or to be deployed independently or in groups of UASs. It is capable of carrying both weapons and sensor systems.” In addition, the Mako has the capability to be launched independently of a runway, as illustrated below. The price for these vehicles is three million each, dropping to two million each for an order of at least 100 units. Assuming a cost of $95 million for an F-35A, we can imagine a hypothetical combat scenario pitting two F-35As up against 100 of these Mako UCAVs in a drone swarm; a great example of the famous phrase, quantity has a quality all its own.

A battery of Kratos Aerial Target drone ready for take off. One of the advantages of the low-cost Kratos drones are their ability to get into the air quickly. [Kratos Defense]

How to evaluate the effects of these possible UCAV drone swarms?

In building up towards the analysis of all of these capabilities in the full theater, campaign level conflict, some supplemental wargaming may be useful. One game that takes a good shot at modeling these dynamics is Asian Fleet.  This is a part of the venerable Fleet Series, published by Victory Games, designed by Joseph Balkoski to model modern (that is Cold War) naval combat. This game system has been extended in recent years, originally by Command Magazine Japan, and then later by Technical Term Gaming Company.

Screenshot of Asian Fleet module by Bryan Taylor [vassalengine.org]

More to follow on how this game transpires!

C-WAM 3

Now, in the article by Michael Peck introducing C-WAM, there was a quote that got our attention:

“We tell everybody: Don’t focus on the various tactical outcomes,” Mahoney says. “We know they are wrong. They are just approximations. But they are good enough to say that at the operational level, ‘This is a good idea. This might work. That is a bad idea. Don’t do that.’”

Source: https://www.govtechworks.com/how-a-board-game-helps-dod-win-real-battles/#gs.ifXPm5M

I am sorry, but this line of argument has always bothered me.

While I understand that no model is perfect, that is the goal that modelers should always strive for. If the model is a poor representation of combat, or parts of combat, then what are you teaching the user? If the user is professional military, then is this negative training? Are you teaching them an incorrect understanding of combat? Will that understanding only be corrected after real combat and loss of American lives? This is not being melodramatic…..you fight as you train.

We have seen the argument made elsewhere that some models are only being used for training, so…….

I would like to again bring your attention to the “base of sand” problem:

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

As always, it seems that making the models more accurate seems to take lower precedence to whatever. Validating models tends to never be done. JICM has never been validated. COSAGE and ATCAL as used in JICM have never been validated. I don’t think C-WAM has ever been validated.

Just to be annoyingly preachy, I would like to again bring your attention to the issue of validation:

Military History and Validation of Combat Models

 

 

C-WAM 2

Here are two C-WAM documents: their rule book and a CAA briefing, both from 2016:

C-WAM’s rule book: https://paxsims.files.wordpress.com/2016/10/c-wam-rules-version-7-29-jul-2016.docx

CAA briefing on C-WAM: https://paxsims.files.wordpress.com/2016/10/mors-wargame-cop-brief-20-apr-16.pptx

A few highlights (rule book):

  1. Grid size from 2 to 10 km, depending on terrain (section 2.2)
    1. Usually 5 km to a grid.
  2. There is an air-to-air combat table based upon force ratios (section 3.6.4).
  3. There is a naval combat table based upon force ratios (section 3.9.4).
  4. There are combat values of ground units (section 3.11.5.B)
  5. There is a ground combat table based upon force ratios (section 3.11.5.E)
  6. There is a “tactics degrade multiplier” which effectively divides one sides’ combat power by up to 4 (section 3.11.5.P).
  7. These tables use different types of dice for probability generation (showing the influence of Gary Gygax on DOD M&S).

A few highlights (briefing)

  1. Executes in 24 or 72 hours time steps (slide 3)
  2. Brigade-level (slide 18)
  3. Breakpoint at 50% strength (can only defend), removed at 30% strength (slide 18 and also rule book, section 5.7.2).

Anyhow, interesting stuff, but still basically an old style board-game, like Avalon Hill or SPI.

 

C-WAM 1

Linked here is an article about a wargame called C-WAM, the Center for Army Analysis (CAA) Wargaming Analysis Model: https://www.govtechworks.com/how-a-board-game-helps-dod-win-real-battles/#gs.ifXPm5M

A few points:

  1. It is an old-style board game.
  2. Results are feed into RAND’s JICM (Joint Integrated Contingency Model).
    1. Battle attrition is done using CAA’s COSAGE and ATCAL.
  3. Ground combat is brigade-level.

More to come.

Perla On Dupuy

Dr. Peter Perla, noted defense researcher, wargame designer and expert, and author of the seminal The Art of Wargaming: A Guide for Professionals and Hobbyists, gave the keynote address at the 2017 Connections Wargaming Conference last August. The topic of his speech, which served as his valedictory address on the occasion of his retirement from government service, addressed the predictive power of wargaming. In it, Perla recalled a conversation he once had with Trevor Dupuy in the early 1990s:

Like most good stories, this one has a beginning, a middle, and an end. I have sort of jumped in at the middle. So let’s go back to the beginning.

As it happens, that beginning came during one of the very first Connections. It may even have been the first one. This thread is one of those vivid memories we all have of certain events in life. In my case, it is a short conversation I had with Trevor Dupuy.

I remember the setting well. We were in front of the entrance to the O Club at Maxwell. It was kind of dark, but I can’t recall if it was in the morning before the club opened for our next session, or the evening, before a dinner. Trevor and I were chatting and he said something about wargaming being predictive. I still recall what I said.

“Good grief, Trevor, we can’t even predict the outcome of a Super Bowl game much less that of a battle!” He seemed taken by surprise that I felt that way, and he replied, “Well, if that is true, what are we doing? What’s the point?”

I had my usual stock answers. We wargame to develop insights, to identify issues, and to raise questions. We certainly don’t wargame to predict what will happen in a battle or a war. I was pretty dogmatic in those days. Thank goodness I’m not that way any more!

The question of prediction did not go away, however.

For the rest of Perla’s speech, see here. For a wonderful summary of the entire 2017 Connections Wargaming conference, see here.

 

Spotted In The New Books Section Of The U.S. Naval Academy Library…

Christopher A. Lawrence, War by Numbers: Understanding Conventional Combat (Lincoln, NE: Potomac Books, 2017) 390 pages, $39.95

War by Numbers assesses the nature of conventional warfare through the analysis of historical combat. Christopher A. Lawrence (President and Executive Director of The Dupuy Institute) establishes what we know about conventional combat and why we know it. By demonstrating the impact a variety of factors have on combat he moves such analysis beyond the work of Carl von Clausewitz and into modern data and interpretation.

Using vast data sets, Lawrence examines force ratios, the human factor in case studies from World War II and beyond, the combat value of superior situational awareness, and the effects of dispersion, among other elements. Lawrence challenges existing interpretations of conventional warfare and shows how such combat should be conducted in the future, simultaneously broadening our understanding of what it means to fight wars by the numbers.

The book is available in paperback directly from Potomac Books and in paperback and Kindle from Amazon.

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

The late, great Carl Sagan.

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

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

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

Screw Theory! We Need More Prediction in Security Studies!

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

Predictions

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

Military History and Validation of Combat Models

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

Validating Trevor Dupuy’s Combat Models

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

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

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

His argument:

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

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

Happy Holidays to all!

TDI Friday Read: The Lanchester Equations

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

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

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

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

Lanchester equations have been weighed….

Really…..Lanchester?

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

Human Factors In Warfare: Interaction Of Variable Factors

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

Military History and Validation of Combat Models

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

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

Validating Trevor Dupuy’s Combat Models

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

A Summation of QJM/TNDM Validation Efforts

By Christopher A. Lawrence

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

The Two Original Validations of the QJM

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

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

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

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

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

The Development of the TNDM and Desert Storm

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

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

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

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

The Battalion-Level Validation of the TNDM

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

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

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

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

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

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

The Corps- and Division-level Validations of the TNDM

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

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

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

Summation

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

NOTES

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

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

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

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

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

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

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

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

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

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

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

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

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

The 3-to-1 Rule in Histories

I was reading a book this last week, The Blitzkrieg Legend: The 1940 Campaign in the West by Karl-Heinz Frieser (originally published in German in 1996). On page 54 it states:

According to a military rule of thumb, the attack should be numerically superior to the defender at a ratio of 3:1. That ratio goes up if the defender can fight from well developed fortification, such as the Maginot Line.

This “rule” never seems to go away. Trevor Dupuy had a chapter on it in Understanding War, published in 1987. It was Chapter 4: The Three-to-One Theory of Combat. I didn’t really bother discussing the 3-to-1 rule in my book, War by Numbers: Understanding Conventional Combat. I do have a chapter on force ratios: Chapter 2: Force Ratios. In that chapter I show a number of force ratios based on history. Here is my chart from the European Theater of Operations, 1944 (page 10):

Force Ratio…………………..Result……………..Percentage of Failure………Number of Cases

0.55 to 1.01-to-1.00…………Attack Fails………………………….100……………………………………5

1.15 to 1.88-to-1.00…………Attack usually succeeds………21…………………………………..48

1.95 to 2.56-to-1.00…………Attack usually succeeds………10…………………………………..21

2.71 to 1.00 and higher….Attack advances……………………..0…………………………………..42

 

We have also done a number of blog posts on the subject (click on our category “Force Ratios”), primarily:

Trevor Dupuy and the 3-1 Rule

You will also see in that blog post another similar chart showing the odds of success at various force ratios.

Anyhow, I kind of think that people should probably quit referencing the 3-to-1 rule. It gives it far more weight and attention than it deserves.