For example, the lack of sufficient (i.e. a multiple of the adversaries armour power, the multiple depending on the type of attack and the terrain present) anti-armour potential (contributed by all combat arms) would tend to increase the perceived combat power of adversary armour and thus contribute to a better force ratio for him.

This interplay between combat power (actual) and anti-combat potential for both sides, leads to non-linear performance effects (synergies of combined arms) as well as interesting (game theory like) resource allocation (ie optimal combined arms mix) problems.

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Originally posted by A_Gill:
The Situational Force Scoring technique from the RAND corporation (Allen, 1992)...

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I've never looked into how RAND has done its situational scoring, so I cannot really say. The issue is not entirely ignored in the QJM and TNDM, and shows up in the mobility equation (which I believe is discussed in depth in a much earlier post), which is in different forms in the QJM and the TNDM. This equation more specifically addresses the advantage gained by a more mobile (and armor heavy force) over a less mobile one. Beyond that, there is nothing to address the value of the "synergistic" effects of weapons...nor do I believe that there is any rigorous analysis done for which to base such a factor on (a significant point).

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What is the QJM communities opinion of the RAND technique, and has there been an effort to combine the two ?!

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To answer the second part....there has been almost no interaction between Trevor Dupuy, HERO, DMSI or TDI and RAND. It has been mostly casual and social. As such, we have never looked in depth at what RAND has done.

Now, RAND (Paul Davis) openly acknowledges that the QJM served as the inspiration for the RSAS/JICM model and that it borrowed from it. How much has been borrowed I do not know, as I have never seen the documentation on how the internals of the model specifically works (nor do I think it has been published).

I do have some fundamental disagreements with the RAND RSAS/JICM approach....they are:

1. They have not documented specifically how their model works. As such, it is still a black box.

2. They have never validated their model.

3. They appear to have used some of the QJM charts and table, and HERO data in their model, but how it is used and in what context, we do not know....and they have added to that their additions, a different weapon scoring system and other elements. The QJM and TNDM have both been validated with all the elements together. Cherry picking out some elements...and mixing them with other elements from wherever....results in something which I do not know exactly what it does. Without rigorous validation...this is conceptionally flawed.

4. They use WEI/WUVs (as opposed to OLIs) for their weapon scoring. WEI/WUVs were an old weapon scoring system based upon a BOGSAT methodology that was discarded by their inventor, CAA, about 20 years ago. As such, their updates have had to be made by RAND by a different BOGSAT methodology. (BOGSAT = Bunch of Guys Sitting Around a Table).

Now....whether you agree or disagree with Trevor Dupuy's formulas, OLIs have the advantage that they are consistently developed and applied from an unchanging formula. Furthermore, they have been validated for use within the QJM and TNDM (it is difficult to validate a scoring system outside of the model used). Obviously, WEI/WUVs have not been validated.

So now, we have taken a scoring system created by different people for a different purpose and plugged it into QJM like tables inside of RSAS/JCIM. Again, there appears to be a conceptual flaw here and certianly a lack of rigor.

5. They have added elements like situational scoring and density of front equations to their model that as far as I know are not supported by any rigorous studies (at least none that I have seen).

I, for one, am mystified by the approach that RAND has taken, and it would appear that the RSAS/JICM is in some respects a QJM-like model done with less rigor. Still, I cannot speak for certian on this, as I really don't know what is inside the model or how their factors were developed.