I’ve been reading a wonderful book; ‘the score’, by C.Thi Nguyen, a professor of philosophy with a thing about games in general, and board games in particular. He spends a while on our relationship with metrics and the nature of ‘value capture’; finding ourselves seeing the world through the lens of what we choose to measure, rather than what is there. The hype around ai is a good example. Learning to use it quickly is a very metrics driven obsession, as though if we don’t it will somehow evaporate. I think Stafford Beer has a point when it comes to the black box of complexity; don’t stare into it, it will drive you mad. Look at the relationship between inputs and outputs, and work with that. Observation and orientation beats fretting about the box.
As I said the other day, studying Economics back in the 1970s was a perfect grounding for looking at complexity. You can only see bits and pieces of how the economy works (microeconomics) but that knowledge doesn’t scale. To see more broadly (macroeconomics) you can only look at the inputs and outputs and try to figure out how they relate to each other, and you don’t have perfect information on those anyway.
It is precisely because Economics started to believe the micro view was more important and that modelling the economy (econometrics) could give you predictive power that it went mad, missed the GFC (which it still can’t explain) and became a useless tool, overly influenced by politics. It looked deep inside the box and went mad.
Of all the stuff in my inbox reminding me of new content waiting to be looked at, your content is the only one that consistently leads me to read to the end even when I, supposedly, don't have time for it. It is, indeed, obvious you are not using AI. If only for a sentence like "That not what we want to read", which ALSO made me smile. Great content presented beautifully. Thanks once again.
I’ve been reading a wonderful book; ‘the score’, by C.Thi Nguyen, a professor of philosophy with a thing about games in general, and board games in particular. He spends a while on our relationship with metrics and the nature of ‘value capture’; finding ourselves seeing the world through the lens of what we choose to measure, rather than what is there. The hype around ai is a good example. Learning to use it quickly is a very metrics driven obsession, as though if we don’t it will somehow evaporate. I think Stafford Beer has a point when it comes to the black box of complexity; don’t stare into it, it will drive you mad. Look at the relationship between inputs and outputs, and work with that. Observation and orientation beats fretting about the box.
As I said the other day, studying Economics back in the 1970s was a perfect grounding for looking at complexity. You can only see bits and pieces of how the economy works (microeconomics) but that knowledge doesn’t scale. To see more broadly (macroeconomics) you can only look at the inputs and outputs and try to figure out how they relate to each other, and you don’t have perfect information on those anyway.
It is precisely because Economics started to believe the micro view was more important and that modelling the economy (econometrics) could give you predictive power that it went mad, missed the GFC (which it still can’t explain) and became a useless tool, overly influenced by politics. It looked deep inside the box and went mad.
Of all the stuff in my inbox reminding me of new content waiting to be looked at, your content is the only one that consistently leads me to read to the end even when I, supposedly, don't have time for it. It is, indeed, obvious you are not using AI. If only for a sentence like "That not what we want to read", which ALSO made me smile. Great content presented beautifully. Thanks once again.
Thanks Jorn! That’s much appreciated.
I definitely do not use AI. I enjoy the process of writing it myself, that’s the whole point of doing it.
Although that wasn’t a deliberate error I put in to make the point!