Another interpretation of that website would conclude S=12,000 (though I think just using the big categories S=1,388 works better). Thank you for the link. I originally thought S=100 might be too high but now I feel it might be too low.
Looking for pre-existing career paths is doing the opposite of what my article recommends because pre-existing career paths are, by definition, domains with competition. To follow the advice in this article, you usually must discover or “invent a new meaningful combination of skills”.
Many meaningful combinations are not being competed on. For example, nobody is writing good Harry Potter Rationalist fanfiction right now and that’s just n=2 (fiction writing and rationalism).
Maybe there are better ways to come to agreement on the feasibility of discovery. Perhaps we should look at the success rate of startups? Do you have other ideas?
From lsusr’s response to my comment above, I think they’re saying that learning new skills makes you more able to recognize the right problems to solve, as well as to execute on potential solutions.
So there is a prediction here. Learning a new skill will be more positively correlated with identifying better ideas than they had before, as compared with an equal investment of time deepening a skill they already possessed.
How can we operationalize this?
One way is that we could create a dataset of startup founders. We’d look at their age and the number of previous companies they’d founded or worked for. Optimally, we’d find a way to quantify how different from each other these companies were.
lsusr’s framework might predict that, controlling for age, founders who’d worked for a greater number and diversity of companies would achieve greater personal wealth than those who’d worked for a smaller number and less diverse range of companies.
Because this is a loose heuristic, with a solid argument in both directions, I think we should insist on careful data-gathering in a carefully operationalized manner before we accept anything as more substantial evidence than the plausibility-supporting anecdotes we already have access to here.
I base my estimate of the feasibility of discovery on my personal experience of startup (and non-startup) entrepreneurship. The number of worthwhile projects I can pursue is way higher than the number of projects I have time to pursue. For example, I coded and deployed an ML-based language learning app that I use for self-study but I don’t open it up to the public because I’m working on more important projects.
Another interpretation of that website would conclude S=12,000 (though I think just using the big categories S=1,388 works better). Thank you for the link. I originally thought S=100 might be too high but now I feel it might be too low.
Looking for pre-existing career paths is doing the opposite of what my article recommends because pre-existing career paths are, by definition, domains with competition. To follow the advice in this article, you usually must discover or “invent a new meaningful combination of skills”.
Many meaningful combinations are not being competed on. For example, nobody is writing good Harry Potter Rationalist fanfiction right now and that’s just n=2 (fiction writing and rationalism).
Our double crux seems to be about the feasibility of finding new combinations that are meaningful.
Using previously discovered combinations as examples doesn’t count, because discovery is easy in hindsight.
Could you come up with a bunch of novel combinations, right here and now, that sound like they might be useful to the world at large?
I could, but that would be a recipe for ideas that sound good but are actually bad.
Maybe there are better ways to come to agreement on the feasibility of discovery. Perhaps we should look at the success rate of startups? Do you have other ideas?
From lsusr’s response to my comment above, I think they’re saying that learning new skills makes you more able to recognize the right problems to solve, as well as to execute on potential solutions.
So there is a prediction here. Learning a new skill will be more positively correlated with identifying better ideas than they had before, as compared with an equal investment of time deepening a skill they already possessed.
How can we operationalize this?
One way is that we could create a dataset of startup founders. We’d look at their age and the number of previous companies they’d founded or worked for. Optimally, we’d find a way to quantify how different from each other these companies were.
lsusr’s framework might predict that, controlling for age, founders who’d worked for a greater number and diversity of companies would achieve greater personal wealth than those who’d worked for a smaller number and less diverse range of companies.
Because this is a loose heuristic, with a solid argument in both directions, I think we should insist on careful data-gathering in a carefully operationalized manner before we accept anything as more substantial evidence than the plausibility-supporting anecdotes we already have access to here.
I base my estimate of the feasibility of discovery on my personal experience of startup (and non-startup) entrepreneurship. The number of worthwhile projects I can pursue is way higher than the number of projects I have time to pursue. For example, I coded and deployed an ML-based language learning app that I use for self-study but I don’t open it up to the public because I’m working on more important projects.