I call it “epistemic latency” – when knowledge lags “what is” and what currently works. I call its opposite “knowledge dynamics” or “knowledge kinetics” – the desired state in managing knowledge.
As you point out, this is a huge vulnerability for “best practices” and institutional knowledge in general – which in practice, is typically stored in the form of (inherently static) information. Knowledge – instead of being a buffer against environmental change, as it should be -- then becomes a self-contained meta-reality that no longer accurately represents “real” reality.
At worse, knowledge (as practiced) can be a drag on forward progress! To wit, “We already know this, so there’s no need to re-assess the situation – we’ll just plug and play our canned solution.”
Naming this is important, I agree – but, more importantly, how do we fix it -- or prevent it in the first place?
In my opinion,
TIM WOOD POWELL | President, The Knowledge Agency® | Author, The Value of Knowledge |
New York City, USA | TEL +188.8.131.520 |
<main@SIKM.groups.io> on behalf of Stephen Bounds <km@...>
I am hoping to draw on your hivemind to see if there's a good term out there for a very particular phenomena that I am observing.
Most of us would be familiar with the "sunk cost fallacy", the idea that any decision should ignore past costs (either time or money) when making a future decision. It is common to stick with initiatives long past any rational reason to do so, typically for reasons of commitment bias and loss aversion.
The phenomenon I am seeking to explain is one rooted in a knowledge failure. It occurs when an organisation implements solutions in response to a problem, but then sustains those solution long past their useful life. I suspect that this is especially common after an extended period of process optimisation that is built on base knowledge which then becomes outdated.
After some reflection, I have reminded myself that the "double loop learning" process proposed by Argyris can be a solution to this problem. But I don't think this helpfully describes the failure. "Failure to engage in double loop learning" is gobbledygook to anyone outside of KM. "Retaining bad assumptions" is too vague for the situation.
The scenario I am particularly thinking of is:
1. The solution made sense and worked when it was devised
2. The environment changes, making some prior knowledge invalid and the previous solution ineffective or an outright failure (generally the failure must be partial or subtle, excusable as an "outlier" or "temporary" aberration)
3. The organisation is biased towards keeping the practice in place despite rising evidence to the contrary since everyone "knows it works"
A high-profile example of this failure was the shift to digital downloads at the turn of the millennium. The music industry lost nearly half its revenue during a consumer-led revolt against the traditional model of album-based, physical CD sales.
The problem is that while in a competitive marketplace such flawed reasoning gets exposed relatively quickly, in a monopolistic situation (particularly in government) there is less pressure to fix these issues. It is generally only after a significant number of patently absurd outcomes get publicised that serious reform is considered -- and until then, lots of unnecessary human suffering can occur.
So: I need a snappy name to describe this knowledge failure. Got any good ideas?
Executive, Information Management
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