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The Danger of Incorrectly Mapping Between Scientific Measures and Truth
Transcript:
Speaker 1
And it's a problem when scientific culture tolerates too much ambiguity. There's always a caveat there, which is that at the early stage of theory development, sometimes you need ambiguity because you don't actually know really what you're talking about Yet. And so you need to allow for multiple interpretations to be possible until you can figure out what you mean. But a mature theory should be minimally ambiguous. This is at odds with things like metrics in terms of let's say how to evaluate something because people think, oh, well, it's scientific. Therefore, I want to use this to then therefore impose a value judge on something. It's better because it has a higher score on it. But that's not what science is actually able to do. Science can say, it has this score and it measures this thing because what it measures is this. If you say what it measures is this, and therefore it means this other thing, that's a problem because that's a false mapping. And it's not really about ambiguity versus precision. It's about, I think, the imprecision of the mapping between the measure and the term. So if you want to measure something like happiness or economic prosperity, you can say, well, we'll measure the genie coefficient, we'll measure GDP. But those are rigorous, clearly unambiguous measures. They have a meaning. This is what they are. This is how we measure them. We can compare things on this measure. And that's not problematic until you then say, and it is better to have a higher GDP full stop.
Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale
COMPLEXITY: Physics of Life
The danger, and you see it often in investing, is when people become too McNamara-like – so obsessed with data and so confident in their models that they leave no room for error or surprise. No room for things to be crazy, dumb, unexplainable, and to remain that way for a long time. Always asking, “Why is this happening?” and expecting there to be a rational answer. Or worse, always mistaking what happened for what you think should have happened.
The ones who thrive long term are those who understand the real world is a neverending chain of absurdity, confusions, messy relationships, and imperfect people.
Does Not Compute
collabfund.com
The Factors That Hinder Knowledge Transfer Are Often Structural
Summary:
Barriers to knowledge transfer or knowledge sharing are often structural, rather than merely the result of skill set limitations.
Lessons can be transferred through storytelling, lessons with direction reviews and debriefs, analysis and research, and by allocating workload strategically. Placing the workload at the top, instead of burdening lower-level employees with excessive reading, is crucial.
It is essential to identify the structural barriers causing the hindrance and address them.
Transcript:
Speaker 1
And if something is learnt at one end of the state, I need to transfer that to the other. Now you can do that with story, but you can also do that with lessons without direction reviews and debriefs. You can do that through analysis and research. And you can do that by putting the workload where it should be, you know, up at the top, rather than on the poor people down below expected to read 160 documents a year about everything Because they're going to remember that really, they're going to remember that, not in my experience. So what we have to do is take those really important lessons and then think, well, what are the structures that are causing that to happen?
Speaker 2
And now we've already kind of hindered around this. What are the barriers to knowledge transfer or knowledge sharing? And is it just the skill set of listening and conversation? Is that the biggest barrier?
Speaker 1
I think a lot of the barriers that we deal with are structural.
Organizational Structures That Enable Knowledge Flow With Stuart French
Because You Need to Know Podcast ™
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