Bryan Caplan does not like the Austrian concept of “radical uncertainty”. After thinking about this for a bit (I’ve been interested in uncertainty and judgment for a few months; my reading list so far is here), I have come to a view similar to Arnold Kling’s. I do think the concept of “radical uncertainty” (at least as I understand it; I have not read any Austrians on this topic) is useful, but I also think that the subjective (Bayesian) probability perspective is useful.
As I understand it, “radical uncertainty” is closely related to the idea of “unknown unknown.” An Unknown Unknown is an event that occurs but was totally outside of a person’s event space of possible outcomes. For example, if you make the decision to steal to 2nd base in a softball game and fall into a sinkhole under second base, falling into a sinkhole was an unknown unknown event. Falling into a sinkhole in that circumstance is an event which you would probably not have considered even if you had given the decision a lot of thought; it was totally out of your event space. Radical uncertainty is the uncertainty that comes from having nonexhaustive event space.
Part of the planning process is comming up with possible events. I don’t really know how this works, but our brains certainly don’t come up with exhaustive event spaces in most circumstances, and in many cases we seem to miss important potential events. Our brains seem to use useful but flawed heuristics to generate event spaces, much the same way that our brains generate subjective probabilities.
Events outside of a person’s subjective event space are not given a probability of zero, they are simply outside of the event space. If an event was previously outside of a good Bayesian’s subjective event space, and something else suggests the possibility of the event to them, the they would then give the event a nonzero probability. From a Bayesian perspective, it doesn’t make sense to say that the person gives the event a probability of zero because all probabilities are subjective, and a person cannot give a probability to an event that has not even crossed their minds.
Even a good Bayesian, who uses evidence to update all their subjective probabilities and who is well calibrated, can still be radically uncertain for planning purposes, because they can’t always generate a relatively complete subjective event space. They can even still be radically uncertain, even if they know they are radically uncertain. When you are radically uncertain, you have to make a judgment about how complete your event space is and what the expected utility is of events outside your event space, this is often very difficult, which explains why radical uncertainty poses such a challenge. However, recognizing you are radically uncertain is the first step towards generating a more complete event space, therebye turning radical uncertainty into normal uncertainty.