Complex Adaptive Systems: A Primer

Several years ago, a friend and mentor recommended The Origin of Wealth by Eric Beinhocker, a book that looks at the economy as a complex adaptive system in which physical and social technologies are constantly evolving. This view of economics, according to Beinhocker, calls into question every fundamental assumption of classical economics and has profound implications for the ways in which we engage instrumentally with the economic environment.

I was astonished by the work and the concepts it presented, and I've been wrestling with complex adaptive systems as a minor obsession ever since, with a particular focus on the implications for my own fields of military operations research and force structure analysis (a subject on which I'll have more to say another day).

In this intellectual wrestling match, I found myself in need of a working definition of these systems, and because of the applications I was pondering I needed that definition to work from the bottom up. In other words, I needed a list of the desiderata that characterize what a complex adaptive system is and does (to provide a heuristic for classifying such a system). In the end, I arrived at the following:

A complex adaptive system is any system comprised of diverse, interdependent, adaptive elements interacting nonlinearly and exhibiting systemic behaviors including emergence, coevolution, and path dependence across multiple scales.

As a standalone definition, this has served me fairly well in pulling together the various descriptive and behavioral elements of complex adaptive systems as they've been articulated by those far more expert than I (e.g., Brian Arthur, Yaneer Bar-Yam, Eric Beinhocker, John Holland, Melanie Mitchell, Scott Page, and too many more to name). What it doesn't do, however, is lay out in detail what each of these seven characteristics mean for how we understand and engage complex systems. More depth is really needed. Then enters opportunity ...

In writing a thesis (now a short book on the subject of complex systems as a lens for understanding (material) military force structure published by Air University Press and available for free download here), I've put together a short and (I hope) useful primer on complex adaptive systems. In the interest of adding a verse to the powerful complexity play, that primer is available as a standalone document here ... Complex Adaptive Systems: A Primer.

Data Worship and Duty

If you spend more than a few minutes working as an analyst--operations, program, logistics, personnel, or otherwise--it is almost inevitable that some wise military soul will offer trenchant historical lessons about undue trust in analytics for decision making derived from the performance of Robert McNamara as Secretary of Defense. Too often, these criticisms are intended to deflect and deflate criticisms and conclusions of analysis without addressing the analysis itself (an ad hominem approach without so much of the hominem). But that doesn't mean there aren't common mistakes made in the conduct of analysis and worthwhile lessons to be learned from McNamara.

This short article from the MIT Technology Review is a bit old, but it also makes a number of useful points. The "body count" metric, for example, is a canonical case of making important what we can measure rather than measuring what's important (if what is important is usefully measurable at all). Is the number of enemy dead (even if we can count it accurately) an effective measure of progress in a war that is other than total? So, why collect and report it? And what second-order effects are induced by a metric like this one? What behavior do we incentivize by the metrics we choose, whether its mendacious reporting of battlefield performance in Vietnam or the tossing of unused car parts in the river? 

There's something more fundamental going on in the worship of data, though. We gather more and more detailed information on the performance of ours and our adversaries' systems and think that by adding decimals we add to our "understanding." Do we, though? In his Foundations of Science, Henri Poincaré writes:

If we could know exactly the laws of nature and the situation of the universe at the initial instant, we should be able to predict exactly the situation of this same universe at a subsequent interest. But even when the natural laws should have no further secret for us, we could know the initial situation only approximately. If that permits us to foresee the subsequent situation with the same degree of approximation, this is all we require, we say the phenomenon has been predicted, that it is ruled by laws. But this is not always the case; it may happen that slight differences in the initial conditions produce very great differences in the final phenomenon; a slight error in the former would make an enormous error in the latter. Prediction becomes impossible and we have the fortuitous phenomenon. 

Poincare is describing here what would later be dubbed the butterfly effect for nonlinear systems (with the comparison to predicting the weather made explicit in a later chapter). In systems such as these, chasing data is to pursue a unicorn and the end of the rainbow. Rather, it is structure we should chase. Modeling isn't about populating our tools with newer and better data (though this may be important, if secondary). Rather, modeling is about understanding the underlying relationships between the data.

We often hear or read that some General or other should have fought harder against the dictates of the McNamara Pentagon, but one wonders if perhaps such a fight is also the duty of a military analyst.