By Matthew Welsh. Originally published on the Integration and Implementation Insights blog.
How can we distinguish between knowledge and ignorance and our meta-knowledge of these – that is, whether we are aware that we know or don’t know any particular thing? The common answer is the 2×2 trope of: known knowns; unknown knowns; known unknowns; and unknown unknowns.
For those interested in helping people navigate a complex world, unknown unknowns are perhaps the trickiest of these to explain – partly because the moment you think of an example, the previously “unknown unknown” morphs into a “known unknown”.
My interest here is to demonstrate that this 2×2 division of knowledge and ignorance is far less crisp than we often assume.
This is because knowledge is not something that exists in the world but rather in individual minds. That is, whether something is ‘known’ depends not on whether someone, somewhere, knows it; but on whether this person, here-and-now does.
What an individual ‘knows’, however, is not static. Obviously, we learn new things: unknowns becoming known. But we also forget things: knowns becoming unknown, whether permanently or temporarily.
Further, whether we remember particular things is contextual – how questions are posed to us and other factors affect our memory processes. This alters what parts of our memory we search and how – changing the likelihood of our ‘finding’ different pieces of knowledge. That is, we can (and do) fail to recall things that we, in another sense, ‘know’.
To take a simple example, if I ask you to list all possible reasons for your car not starting, you will produce a list of possibilities informed by recent experience and other contextual effects. This list is unlikely to be exhaustive. Instead, it will be a subset of the potential list you could produce with a perfect search of your memory (and rational extrapolation). This larger list will, itself, be a subset of a complete list that would include mechanisms that you have never encountered or don’t understand.
When I then ask you to decide how likely each of these possible causes is, the missing items are all, epistemologically, unknown unknowns – things you are unaware you need to estimate. Some, however, would have been known unknowns in different circumstances – had your cognitive processing been triggered from a different starting point and turned up a different set of alternatives. For example, if you forget to include “out of petrol” on your list, the probability of this as a reason for your car not starting is an unknown unknown – despite you knowing (in another sense) that this is a possible reason.
The literature on decision making is rife with examples of context affecting people’s cognition and, by extension, what they ‘know’ at any given point in time. This complexity is too easily swept under the rug when we think about knowledge as if it exists in the world rather than in the mind.
Given this, asking ‘what do you know?’ seems an oversimplification. To understand what we know, we have to consider when we know things and the processes underlying how we know things – and that is before we even start on the thorny problem of differentiating knowledge from belief.
Is the 2×2 structure more of a help or a hindrance in thinking about knowns and unknowns? What other categories/distinctions might a complete model need to include?
You’re invited to add your thoughts to the discussion on the Integration and Implementation Insights blog.
|Matthew Welsh PhD is a Senior Research Fellow at the Australian School of Petroleum, University of Adelaide, Australia. His research focuses on how people’s inherent cognitive processes affect their judgements, estimates and decisions and the implications of this for real-world decision making. He is the author of Bias in Science and Communication: a field guide – a guide for scientists and science communicators interested in understanding how biases arise, can be identified and countered.|
Article source: What do you know? And how is it relevant to unknown unknowns?, republished by permission.
This blog post is part of a series on unknown unknowns as part of a collaboration between the Australian National University and Defence Science and Technology.
For further articles in the series see the Integration and Implementation Insights blog.