The reason I bring it up in context of this article for blundering, is that he studies the chaotic movement of stock markets from the abstract perspective of "roughness". That is, he is interested in categorizing patterns that are in some kind of grey zone between smooth and completely chaotic.
One of the features I recall him mentioning in one of his introductions was related to "black swan" type events in markets. He suggests that only a few stock movements over time account for the majority of loss/gain. This is a feature he was interested in exploring and recreating using mathematical models and it lead him to investigate sampling from stochastic distributions that are not Gaussian.
This view is forcing me to evaluate some of this startup/business advice in a new light. This article seems to assume that both in chess and business that "blunders" are distributed in a normal way (probably not the correct mathematical term but I hope it communicates what I mean). But in reality, some blunders are tiny and some are massive.
Consider, you can avoid 99 out of 100 blunders but if the 1 blunder you make is that black swan blunder then you are dead. Conversely, you can make 99 out of 100 blunders but if you avoid that 1 black swan blunder then you can survive and even thrive. Of course, avoiding all blunders just happens to ensure that you also miss the catastrophic ones.
I haven't fully digested this idea but I think it is the basis for some profound advancement in our understanding. The problem is we can't really tell at any given moment which events are the ones that will end up being the most impactful, that seems to only come in hindsight. But even just realizing that there is an unequal distribution to the contribution of events over time feels pretty important to me.