In trying to answer this postulation we must go back to examine the eternal challenge of good modeling: how to create a simplified representation of reality that in ignoring many real-world features still manages to convey an inherently robust facsimile of the real thing.  For example, one of those maps of New England you buy at Exxon gas stations can serve as a good model for getting you from Hartford, CT to Boston, MA even if it ignores most of the streets and alleyways and other real-world detail that exist along the route.  In their book “Complex Adaptive Systems: An Introduction to Computational Models of Social Life” John H. Miller and Scott E. Page observe that the “ability to ignore is a crucial component of scientific progress”, using the image of a parent’s being able to respond to the incessant “why” questions of a three year old child by saying “just because”.  The trick, as the authors point out, is knowing when (and perhaps more importantly when not) to say “just because”.

While I wholeheartedly agree with that assertion I don’t think that it quite gets us to an adequate level of comfort in applying foresight to the creation of good models.  In his fascinating book “The Origin of Wealth” Eric D. Beinhocker points out that economic modeling took what many consider to be a wrong turn back in the latter years of the 19th century when leading thinkers of the day like Leon Walras and William Stanley Jevons borrowed heavily from the referential context of physics to create models for explaining economic activity, including such notable concepts as a mathematically representable state of equilibrium that continue to serve as the conceptual foundations of modern economics textbooks.  As Beinhocker elaborates, the problem with these models was that some of their fundamental assumptions – like the perfect, robot-like rationality of human beings in making economic choices – didn’t seem to simplify reality as much as contradict reality.  Thus we find ourselves in the present ruminating over the precise, mathematically elegant language of physics and the complex, evolutionary language of biology and debating whether a choice of the wrong science by the founding fathers of economics back in the 19th century led to the failure of models to adequately explain much of what is going on in the economy today and in particular the string of boom-bust upheavals that have become part and parcel of the last 20-odd years of economic activity.

I still don’t think we are there yet in getting closure on the foresight question, but we may be getting closer.  To tie in the strands of thought presented by Miller & Page and Beinhocker, when we get to those basic defining assumptions, when we decide what the model is going to ignore and what it is going to retain, when we say “just because”, that is the point where we have the chance to either build a robust foundation that can withstand the slings and arrows of a continual barrage of economic X-factors or an elegant, commercially viable sandcastle that gets washed away with the next high tide.  If we are going to apply foresight, this is where we have to apply it.

Not that applying foresight is easy, of course, and that is why at Sentrana we are engaged in a continual process of building better models by going back to the basics, asking seemingly simple questions that have complex answers, and trying to ensure that we get it right when we get to that point where it is time to say “just because”.  In our world of Scientific Micromarket Management saying “just because” at the right time can, for example, open up the vista of a complex demand environment to reveal a unique configuration of product mix opportunities at optimal pricing points.  It’s good that we are seeing a heightened level of public discourse about these issues: models may seem to be abstract notions for abstemious minds, but they have real-world economic consequences.  Getting it right has never been more important.