Science, expertise and ignorance

Ignorance is an important driver of scientific inquiry.  If only we could fill in the knowledge gaps, it is argued, then we would know the answers to questions that we currently don't know.  One recognised problem with this account is that sometimes 'we don't know what we don't know', in Donald Rumsfeld's memorable phrase.  Another is that sometimes we are wilfully blind to what we do know, such as that relentless economic growth and environmental destruction are related.  'Unknown knowns' as Zizek called it.

The brilliant Science, Technology & Society (STS) scholar, Brian Wynne, distinguished ‘risk’ (where the odds are known and quantifiable), from ‘uncertainty’ (with known system parameters but unknown odds), from ‘ignorance’ (where we don’t know what we don’t know), from ‘indeterminacy’ (which intersects with the first three but captures contextual socio-political factors as well as the conditionality of knowledge).  Andy Stirling has called the last of these 'ambiguity': 'when experts disagree over the framing of possible options, contexts, outcomes, benefits or harms'.

With COVID-19 we have all of these in bucketloads... what all the drivers and risk factors are, what the odds are of particular risks (they seem to vary widely across countries), a great deal of not knowing what matters and what we need to know, and a large dose of indeterminacy/ambiguity, linked to whether we see COVID-19 as a health problem, a public systems problem, a whole of society problem and so on.

Here's a post on expertise for Arena magazine, that I wrote today.

And here's an extremely interesting article from the New York Times asking why the impacts of COVID-19 have been so different in different places.