I was more after some discontinuity than a simple nonlinearity, like a quadratic or even an exponential dependence. And you are right, the selection effect is at work, but it’s not a negative in this case. We want to select similar phenomena and find a common model for them, in order to be able to classify new phenomena as potentially leading to the same effects.
For example, if you look at some new hypothetical government policy which legislates indexing the minimum savings account rate to, say, inflation, you should be able to tell whether after a sizable chunk of people shift their savings to this guaranteed investment, the inflation rate will suddenly skyrocket (it happened before in some countries).
Or if you connect billions of computers together, whether it will give rise to a hive mind which takes over the world (it has not happened, despite some dire predictions, mostly in fictional scenarios).
Another example: if you trying to “level up”, what factors would hasten this process, so you don’t have to spend 10k hours mastering something, but only, say, 1000.
If you pay attention to this leveling effect happening in various disparate areas, you might get your clues from something like stellar formation, where increasing metallicity significantly decreases the mass required for a star to form (a dust cloud “leveling up”).
Classifying, modeling and constructing successful predictions for this “quantity to quality conversion” would be a great example of useful applied philosophy.
There are (at least) two different things going on here that I think it’s valuable to separate.
One is, as you say, the general category of systems whose growth rate expressed in delivered value “skyrockets” in some fashion (positive or negative) at an unexpected-given-our-current-model inflection point. I don’t know if that’s actually a useful reference class for analysis (that is, I don’t know if an analysis of the causes of, say, runaway inflation will increase our understanding of the causes, say, a runaway greenhouse effect), any more than the class of systems with linear growth rates is, but I’ll certainly agree that our ability to not be surprised by such systems when we encounter them is improved by encountering other such systems (that is, studying runaway inflation may teach me to not simply assume that the greenhouse effect is linear).
The other has to do with perceptual thresholds and just-noticable differences. I may experience a subjective “quantity to quality” transition just because a threshold is crossed that makes me pay attention, even if there’s no significant inflection point in the growth curve of delivered value.
I don’t know if that’s actually a useful reference class for analysis
I don’t know, either, but I feel that some research in this direction would be justified, given the potential payoff.
The other has to do with perceptual thresholds and just-notic[e]able differences.
This might, in fact, be one of the models: the metric being observed hides the “true growth curve”. So a useful analysis, assuming it generalizes, would point to a more sensitive metric.
I was more after some discontinuity than a simple nonlinearity, like a quadratic or even an exponential dependence. And you are right, the selection effect is at work, but it’s not a negative in this case. We want to select similar phenomena and find a common model for them, in order to be able to classify new phenomena as potentially leading to the same effects.
For example, if you look at some new hypothetical government policy which legislates indexing the minimum savings account rate to, say, inflation, you should be able to tell whether after a sizable chunk of people shift their savings to this guaranteed investment, the inflation rate will suddenly skyrocket (it happened before in some countries).
Or if you connect billions of computers together, whether it will give rise to a hive mind which takes over the world (it has not happened, despite some dire predictions, mostly in fictional scenarios).
Another example: if you trying to “level up”, what factors would hasten this process, so you don’t have to spend 10k hours mastering something, but only, say, 1000.
If you pay attention to this leveling effect happening in various disparate areas, you might get your clues from something like stellar formation, where increasing metallicity significantly decreases the mass required for a star to form (a dust cloud “leveling up”).
Classifying, modeling and constructing successful predictions for this “quantity to quality conversion” would be a great example of useful applied philosophy.
There are (at least) two different things going on here that I think it’s valuable to separate.
One is, as you say, the general category of systems whose growth rate expressed in delivered value “skyrockets” in some fashion (positive or negative) at an unexpected-given-our-current-model inflection point. I don’t know if that’s actually a useful reference class for analysis (that is, I don’t know if an analysis of the causes of, say, runaway inflation will increase our understanding of the causes, say, a runaway greenhouse effect), any more than the class of systems with linear growth rates is, but I’ll certainly agree that our ability to not be surprised by such systems when we encounter them is improved by encountering other such systems (that is, studying runaway inflation may teach me to not simply assume that the greenhouse effect is linear).
The other has to do with perceptual thresholds and just-noticable differences. I may experience a subjective “quantity to quality” transition just because a threshold is crossed that makes me pay attention, even if there’s no significant inflection point in the growth curve of delivered value.
I don’t know, either, but I feel that some research in this direction would be justified, given the potential payoff.
This might, in fact, be one of the models: the metric being observed hides the “true growth curve”. So a useful analysis, assuming it generalizes, would point to a more sensitive metric.