Varieties of uncertainty

The Cambridge team has been exploring uncertainty in its many forms for a while now. Last year, they published a theory paper, reviewing related research. At a conference of uncertainty quantification specialists about two years ago, Dr. Freeman asked attendees to write definitions of uncertainty on Post-it notes and stick them on the wall. “Every one was different,” she said. “I have to say my favorite was: ‘Anything and everything that can **** up a decision’ [insert descriptor of your choice].”

The recent study, funded by the Nuffield Foundation, focused on people’s reactions to epistemic uncertainty: things we don’t know about the past and present but in theory could come to know, through measurement. The team is now researching perceptions of aleatory uncertainty — unknowns about the future due to randomness, indeterminacy, chance or luck. (In Latin, alea means dice or gambling.)

Most uncertainty is a mix of epistemic and aleatory elements. For instance: How many more people will get Covid-19? And once transmission is suppressed below the R0=1 threshold (the reproduction number required to rapidly reduce the number of cases to low levels), how will we avoid a rebound?

Common wisdom from the psychologist’s perspective is that people do not like uncertainty, especially about the future, and that it generates a negative response. (Psychologists call this “ambiguity aversion.”) From the statistician’s perspective, the hypothesis is that people have a positive reaction and trust information more when the communicator is being open about uncertainties in facts and figures.

“The motivation was to try to adjudicate between these competing hypotheses,” said Sander van der Linden, a principal investigator, and a psychologist and the director of the Cambridge Social Decision-Making Lab. “Ultimately we didn’t find support for the notion that communicating uncertainty enhances public trust, but it also didn’t substantially undermine it.”

Models are the maps, not the territory

Either way, there is little to assuage our most pressing existential uncertainty: When will the pandemic end?

In the early days of the outbreak, when data was beginning to emerge from China, we were in a state of “deep uncertainty,” also known as “radical uncertainty” or “Knightian uncertainty.” (The economist Frank Knight distinguished between risk and uncertainty about a century ago.) Deep uncertainty is the quagmire of unknown unknowns; there are no constraints.