## Flashbulb Pop!

I’ve went through more of that math for life sciences book. It’s taking forever and it’s at that point where you want it to end, but there is one more chapter taunting you. The discussion is about how samples tend to the normal, and how the sum of normals is another normal. It sounds straightforward enough. But, there was other reading, other thinking, and a surprise.

I’ve talked about how a distribution defines a world, a rectangular world, and how a black swan chops off the smooth convergence and creates a thick tail. It moves the convergences of that world defining distribution, so you end up with a smaller world. Wall street puts a valuation on those worlds, so smaller means a massively lower price for your stock.

I’ve been reading about random walks and Levy flights. The length of the jump and the direction of the jumps in these things is controlled by several parameters each under their own distribution. So instead of having one distribution we have several. And, that black swan cuts through them as well. If we are making a jump in a Levy flight, and we’re not there yet, that black swan would force us to backtrack and make a different jump. We’d stop drilling in the Arctic. That black swan is operating on our networks.

I’ve also come across the notion of causation. Correlation is not causation. We hear that all the time. But, what is correlation? Correlation is a pair of nodes in a network. Causation is an arc between nodes in a network. The network is a collection of distributions connected by other distributions. This was the lesson of “Wetware.” [I tried to find the link, but I’m not certain at this time.] In biochemistry, we had the Krebs cycle, a nice circular pathway describing metabolism. Alas, the cycle isn’t real. It’s a name we put on a collection of random walks constrained by physical shapes.

Our networks include value chains, and they get cut by our black swan as well. That smaller world that the black swan brings us involves all of our networks, not just the one describing our progress across the technology adoption lifecycle, or across our market populations. What we really have is a multinormal collection of distributions all being sliced at the same time. We can’t make the strategic jumps we intended to make. We can’t continue our daily routine in the tactical space either.

The multinormal distribution is also the best way to think about populations for discontinuous innovation. Innovating for the same and adjacent populations, the populations of our economies of scale is continuous–one of our normals. Discontinuous innovation has us addressing and tuning ourselves to a population beyond our economies of scale, a yet to be discovered population–another normal. Keeping those normals separate is essential, but Christensen couldn’t sell that idea to cost accountants, despite it being the way to creating economic wealth, rather than just capturing more cash. Keeping those normals separate would be essential to our organizations, because our current position in our category’s technology adoption lifecycle is tuned into built organizational structure.

You can’t sell to late market pragmatists and early adopters with the same sales force, or same marketing, or same business model. Is it any wonder that existing companies don’t do discontinuous innovation? Is it any wonder that the typical analysis of an F2000 company doesn’t work for discontinuous innovation? The first assumption would be our experience, our templates, our economies of scale matter. Well, no, and that’s long before you get to the differences in the geometries of the pre-six sigma company and the 42 sigma company. It fundamentally boils down to separate populations and their normals. And, that huge paper slicer we call the black swan. Chop. Opportunities gone in a pop of a flashbulb, in a moment unsuspected, but delusionally well known.