Hi folks,

I'd like to start a discussion on comparing/contrasting various methodologies for modeling excellence.

Recently John Grinder, Carmen Bostic St. Clair, and Robert Dilts released a statement that suggests another distinction in what they describe as analytic modeling vs NLP modeling.

This distinction statement was released essentially just after Grinder wrote a response to having read a translation of a French-published interview with Dilts in which Dilts presumably made statements with which John Grinder took issue. If you ask me, it sounded like a recipe for a misunderstanding based on two translations back & forth, but then, I haven't seen a transcript of Dilts' original interview in English before it was translated to French; nor have I read the French article yet & translated it myself.

In any case, while this distinction may be useful for some, for the vast majority of NLP-trained people who I believe don't fully understand the distinctions between implicit & explicit modeling (which is of rather greater importance than this latest published distinction), I suspect it will fall on mostly deaf ears.

What interests me in creating this thread is exploring some differences and similarities between the results generated from various modeling methods out there.

I am a publisher of one such approach to modeling (Knowledge Engineering). I teach this in a 3-day course and in a home-study course. While this method is grounded in over a decade of work in the Artificial Intelligence field, I have blended elements of that field with NLP and I would doubt that John Grinder would identify my KE method as equating to NLP modeling.

Close enough for many people's purposes though, which is partly why several trainers have included my KE approach as their explicit-modeling section of their Master Practitioner programs. And unlike whatever other methods are out there, my KE method is helping people to make sense of the opaque subject of modeling.

However, there are other approaches out there that students report enjoying and using. I would like to learn more about the pros & cons and comparisons between these systems.

For example, perhaps one of the most well known styles of modeling is that taught by David Gordon (he just co-authored a new book on the subject which includes a DVD). My schedule never seems to synch up with David's, which is why I haven't yet attended his modeling training. I will be going through his book & DVD though.

Wyatt Woodsmall presumably teaches modeling.

And John Grinder teaches modeling on occasion.

Problem is, I'm not hearing of any people who've taken those courses, and done any modeling with those methods.

If you've done any real modeling projects, using any of these systems in a rigorous way, please tell us about your methods.

As for my Knowledge Engineering approach, just as I have some KE students that haven't done any big modeling projects, I do have KE students who report that they now feel comfortable doing so (and it ties together many of their other once-linear NLP skillsets into more of an integrated view of how to communicate with people and create changes).

No method is perfect, nor am I saying my method beats everything else out there. I'm sure any modeling methods that are continually growing in popularity all have merit and have their pros and cons. I'd like to begin a dialogue on this critical, core NLP skillset, that seems to be a highly coveted art, but one not as well understood as it could or should be.

Let's explore.