The Problem With the Leading Edge

 

In 1962, Thomas Kuhn wrote The Structure of Scientific Revolutions. In it, he posited that advances in science tend to assume an observable pattern, that of leaping ahead, interspersed with periods of stagnation and conflict. One of the examples he uses is cosmology.

When Copernicus first asserted that the Earth was not the center of the Universe, his ideas were widely scorned and disputed. The Ptomely model of the cosmos, of course, had Earth as its center, with the Sun and the planets orbiting its fixed position. This model assumed a series of set cycles: when observations were made that seemed to contradict the Ptomely theory, the model was modified through the additions of epicycles to explain the new data. However, Copernicus observed data that did not appear to fit the Ptomely model, which led him to develop another model, one with the Sun in the center of the Universe. He published his new model, but its acceptance was not immediately forthcoming. It was not until the invention of the telescope, in 1608, that the amount of additional data being collected that disputed Ptomely, while supporting Copernicus, became so overwhelming that the scientific community adopted Copernicus’ version of the cosmos. As the technology that allowed us to collect even more observational data advanced – no doubt, experiencing its own Kuhnsian cycles – we came to understand that the Solar System is actually part of a vast, swirling arm of the Milky Way, far, far from its center. And – wouldn’t you just know it? – the adaptation of that theory was not immediate. It took time for the majority of the scientific community to accept the next leap forward. Similar patterns have manifested in the other sciences, from biology to chemistry to physics to medicine. While it is somewhat natural for people to look back at history and come to the conclusion that advances in the sciences were made at a relatively steady pace, Kuhn’s work overturns this notion.

As I discuss in my recently-released, must-have book, Game Theory in Management (http://www.gowerpublishing.com/isbn/9781409442417),we start to have difficulties when we talk about management “science.” In order for a theory to be considered scientific, it must have two characteristics:

·         It must be observable, and

·         It must be repeatable in an experimental setting, inside or outside of a laboratory.

Otherwise, it’s rank speculation. There may or may not be a Loch Ness Monster, a plesiosaur left over from ages ago that somehow frequents a particular Scottish lake. Nessie has met the first condition of being “scientific,” with intermittent and spotty observations throughout the modern era. However, without a specimen available for repeated observations, she cannot be considered real, at least not in purely scientific terms. Bill Nye, the “Science Guy,” has recently come out with strong criticism against any who would doubt that the theory of evolution should be considered valid. However, seeing as how the theory of evolution is neither observable (there is no single instance of an observed new species coming from a mutation of an existing, yet identifiably different, one), nor repeatable in an experimental setting, it could actually be argued that, strictly and scientifically speaking, the theory that Nessie exists is more “scientific” than Darwinism. Don’t get me wrong – the macro evolutionists may very well have the inside track on how specific species were introduced into – wait for it! – creation. But when they assume the high ground of “scientific” backing, they presume much.

And now, back to management science. Can we expect management science to advance in ways similar to the other, harder sciences? The macro economical environment – or, even, the micro economical one, really – is so vast, with so many actors and parameters, that it is, essentially, rank speculation for anyone to connect causal factors into a universal hypothesis on exactly how organizations succeed in the marketplace, and precisely why failures fail. Far away from experimental settings, we connect those dots based on our experience and observations, and the more studious amongst us will attempt to verify our particular narratives with analogous situations or stories. Sometimes we managers will swerve into an analysis that is particularly prescient, and can leverage that insight into success, only to find that that narrative is no longer effective in new environs – the parameters of the experiment have changed. The unfortunate among us will have to work for an executive who has realized success in the past, but has inappropriately transferred the structure of the reasons for that success into an organization or project situation where it doesn’t belong, but cannot be persuaded to perform the kind of humble introspection that would allow a mid-course correction. Arrogance in selecting a particular technical approach can produce terrible masters.

As we explore, together, throughout the remainder of September, the makings of early identification of the leading edge of project management theory, I want to remind my readers that much rank speculation will present itself as “scientific,” and, after further review, it very well may be. I just want to be able to reproduce it in an experimental setting prior to complete, blind acceptance. That’s all.