tl;dr: To make good strategic decisions, we need more sophisticated ways of understanding uncertainty. This research project introduces a new conceptual framework for understanding different types of uncertainty (types of not-knowing) — and develops a practical toolkit for relating well to not-knowing.
I’m working on a new research project on not-knowing and what we can do about it. Some of my work in progress is linked below, but you can read everything here. Concurrently, I’m also running a monthly discussion series about not-knowing which is open to all.
The starting point for this research is the (new) concept of different types of “not-knowing” — each represents a different source of uncertainty that is not quantifiable risk.
Why is this important? Because anything that is fully knowable and fully known becomes routine and doable by machines. We are only distinctively human when we confront situations of not-knowing. Understanding not-knowing is how we understand being human — and this is essential in the context of the current landscape of machine learning and artificial intelligence. Learning how to relate well to not-knowing is a path to surviving and flourishing in an increasingly uncertain world: It enables more innovation and is a path to being happier.
We also have no choice in the matter. Every day we confront situations of not-knowing in which we must still do something. The most important human work is navigating significant not-knowing. This is why we reward leaders who lead their companies and countries through unpredictable situations, why we respect startup founders who work with not-yet-understood technology and markets to create new companies, and why we esteem researchers who work at the edge of the known to create new knowledge.
The last few years (global pandemic, crazy weather, geopolitical insecurity, rapid technological change, economic upheaval, etc) show that these situations of not-knowing are growing in number, scope, and impact.
Even those who are charged professionally with navigating these diverse situations of not-knowing often fuck it up. Most recently, the rapid ascent and even faster implosion of Effective Altruism, FTX, and Alameda Research is an object lesson in how poorly not-knowing is understood even by those nominally in the business of managing it — philosophers, venture capitalists, finance industry professionals, and financial journalists. To say nothing of politicians and policymakers.
Even professionals are confused by not-knowing because there are many obstacles in the way of understanding. First, we rarely recognise that not-knowing is emotionally distressing, which prevents clear thinking about it. And even when we get over that emotional hurdle, there is a huge amount of confusion and muddled thinking still in the way. Risk and uncertainty are conceptually mixed up (as part 1, part 2, and part 3 of this series of articles explains), and the words “risk” and “uncertainty” are also used in misleading ways that add to the confusion.
After cutting through the confusion, what remains is clarity about four key types of not-knowing: Not-knowing about actions, not-knowing about outcomes, not-knowing about the connection between actions and outcomes (i.e., causation), and not-knowing about the relative value of different outcomes.
This clarity reveals how relating well to not-knowing requires a mindset that recognises different types of not-knowing and has access to the appropriate tools for decision-making when faced with each type of not-knowing.
My first book was about uncertainty and how organizations can use uncertainty strategically, as a design principle. You can find out more about The Uncertainty Mindset here.