Innovation and revolution in science

A lot has been said already about the role of science as an essential underpinning of innovation. But apart from this perspective of innovation through science, there’s another aspect of the science-innovation relation I’d like to cast some light on: How does innovation in science work?

The best point of departure for such a discussion, I believe, is Thomas Kuhn’s The Structure of Scientific Revolutions. More than half a century ago, Kuhn presented a detailed account of the inner workings of science, which is still valid today. He essentially splits the scientific endeavour into two different regimes: one is what he calls normal science, the other is revolution (as indicated in the book’s title).

Kuhn describes normal science as an act of solving puzzles. Scientific investigation is then focused on only three classes of problems; in Kuhn’s own words:

Determination of significant fact,
matching of facts with theory,
and articulating theory
exhaust, I think, the literature of normal science,
both empirical and theoretical.

Normal science according to Kuhn is executed within a given scientific paradigm, that is to say, within an established scientific tradition. A paradigm guides the research activity of a scientific community and defines the criteria for choosing the puzzles to be solved; a paradigm thus shapes a field of science and its trajectory into the future. However, a paradigm is not explicitly layed down in writing; on the contrary, open-endedness as an important characteristic of a paradigm. This allows for rules and methods to evolve over time, without challenging the paradigm itself.

The paradigm provides a stable reference frame that allows the scientists to build on previously solved puzzles and to accumulate knowledge. The paradigm thus makes normal-science puzzle-solving a focused and highly efficient endeavour. However, that focus also limits the perspective; as Kuhn points out, normal science has no interest to find new phenomena or to challenge the underlying paradigm. Rather, any solved puzzle helps to articulate the paradigm, thereby increasing its validity.

On the other hand it is obvious that normal science will occasionally encounter anomalies that cannot be explained within the given paradigm. In many cases, the scientific community is inclined to ignore such anomalies, especially if they cannot easily be reconciled with the paradigm, or if the puzzle-solving activity that found the anomaly is considered not important enough to deserve additional attention. Only over a longer period of time (which might be decades) will the community realize that either an anomaly is blocking the solution of important puzzles, or that a number of anomalies actually hint at a weakness of the paradigm. At that point, a growing number of members of the scientific community will invest their attention in the understanding of the anomaly, trying to develop an alternative paradigm that can explain and incorporate the anomaly.

This is when normal science under the paradigm loses its solid reference frame, its focus and efficiency; the situation shifts from business as usual to crisis until a new paradigm is found. That new paradigm must be able to incorporate the (then former) anomaly, and it must surely carry forward the vast majority of the solved puzzles of the previous paradigm. Crisis will continue until the community finally adopts on a new paradigm. And that new paradigm then becomes the basis for the new tradition of the community. This change from one paradigm to another is what Kuhn calls a scientific revolution. That’s what often is referred to as a paradigm shift. After such a shift, the community will execute normal science again, only under the new paradigm.

This might seem a mild adjustment at first glance, but for the scientists the shift has drastic effects, because it fundamentally changes the scientists’ view of the world. They don’t just perceive the world differently, they actually see a different world. Thomas Kuhn paints a vivid picture of this transformation:

What were ducks in the scientist’s world before the revolution
are rabbits afterwards.

Such change doesn’t come easy. On the contrary, there is considerable resistance. This is partially a result of personal investment of scientist who have built their entire career and reputation on and within the existing paradigm. But there is an additional, less personal and further reaching effect: the new paradigm is unlikely to contain all the previously solved puzzles. As a result, some previously accepted knowledge could turn out invalid (think about the Copernican Revolution and the geocentric model as a case in point). This loss presents a cost of change that must be outweighed by the superior explanation power of the new paradigm. This loss furthermore illustrates that there is no simple accumulation of knowledge from one paradigm to the next. Rather, knowledge is lost in the transformation: Ducks cease to exist. But if you are more interested in rabbits, that’s a price you’ll be willing to pay.

As an alternative, you might look for an economic comparison: with a paradigm shift, a science community fundamentally reinvents its business model, transforming from a well known value proposition to a more promising. If that reminds you of creative destruction, I’d agree with you.

Note that all this is for the scientists and the scientists only: no politician, no business man, no society involved. All of this happens within the professional community of practitioners of science, it is not visible to the outside world. Kuhn actually devotes an entire chapter to the invisibility of revolutions, pointing out the role of educational textbooks in shaping the tradition of normal science. Because the textbooks are focused on training the next generation, they favour straightforward story lines over a historically correct description of the genesis of ideas. They achieve their immediate objectives by circumventing times of crisis in their storytelling. And of course that simplifying approach has the collateral effect of evoking grossly simplistic and unrealistic expectations.

That seems to be a good starting point to investigate the concept of innovation literacy: what should everybody know about innovation, the role of science, the evolution of technology domains? A promising topic for a future post …


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