3 ms·

I’ve said on here before, an additional extremely negative incentive that comes from an overemphasis on measurable metrics is that a lot of (important, subtle,

I’ve said on here before, an additional extremely negative incentive that comes from an overemphasis on measurable metrics is that a lot of (important, subtle, meaningful) metrics are very difficult to dumb down for non-technical audiences, or even technical audiences lacking domain knowledge. As a result the metrics that tend to be tracked are not just the measurable ones, but the ones for which the importance of them and the meaning of their values are comprehended by sometimes a very low common denominator audience. Usually needing to be tied directly in some way to money. (INdirectly doesn’t cut it, that ends up being too confusing a lot of the time).

burnafter187 5y ago
Thinking about it, in the instance of important but esoteric metrics, do you think that the most frequent case has to do with the [expected] ability to impact or exploit it meaningfully? I don't do a lot of work with numbers, but it seems to me that in terms of affectation, those dumb statistics are the low hanging fruit with low investment cost in terms of manipulation, thus yielding "better" results and isolating them to the use case while alienating the complex metrics. It's a lot easier to take a metric that isn't contextually affected than the obverse, and up/downregulate it. I think my example would be the quasi-metric of the single-sample workup you'd get at a hospital. You get a single datapoint. Compare that to a longer term study where samples are taken at 4 hour intervals. The latter brings up a lot of subsequent questions and has a lot of interconnections with the world at large and the individual's biological characteristics. E.g. getting caught by a train 2/5 days, boosting cortisol and norepinephrine, or low receptor affinity for X, thus producing the illusion of high X levels. As you modify you also complicate with an increasingly large list of rules. Or you just write a script for statins and pat Tom on the ass, noting next visit that serum cholesterol is down.

burnafter187 5y ago

Thinking about it, in the instance of important but esoteric metrics, do you think that the most frequent case has to do with the [expected] ability to impact or exploit it meaningfully? I don't do a lot of work with numbers, but it seems to me that in terms of affectation, those dumb statistics are the low hanging fruit with low investment cost in terms of manipulation, thus yielding "better" results and isolating them to the use case while alienating the complex metrics. It's a lot easier to take a metric that isn't contextually affected than the obverse, and up/downregulate it. I think my example would be the quasi-metric of the single-sample workup you'd get at a hospital. You get a single datapoint. Compare that to a longer term study where samples are taken at 4 hour intervals. The latter brings up a lot of subsequent questions and has a lot of interconnections with the world at large and the individual's biological characteristics. E.g. getting caught by a train 2/5 days, boosting cortisol and norepinephrine, or low receptor affinity for X, thus producing the illusion of high X levels. As you modify you also complicate with an increasingly large list of rules. Or you just write a script for statins and pat Tom on the ass, noting next visit that serum cholesterol is down.