Curated. The question of inside view vs outside-view and expert deference vs own models has been debated before on LessWrong (and EA Forum), but this post does a superb job of making the case for the “use your own models more, trust your own information, be willing to go against the crowd and experts”. It articulates the case clearly and crisply, in a way that I think is possibly more compelling than other sources.
A few points I particularly like:
The identification of selection bias on evidence in different directions:
The problem is that the bad consequences of underconfidence and under-ambition are severe but subtle, whereas the bad consequences of overconfidence and wishful thinking are milder but more obvious. If you’re overconfident, you’ll try things that fail, and people will laugh at you. If you’re underconfident, you’ll avoid making risky bets, and miss out on the potential upside, but nobody will know for sure what you missed.
That relying on mainstream/expert views won’t allow for finding outliers, and finding outliers is crucial to outsized impact:
In fact, outperforming low-info heuristics isn’t just possible; it’s practically mandatory if you want to have an outsized impact on the world. That’s because leaning too heavily on low-info heuristics pushes people away from being ambitious or trying to search for outliers.
Most important things in life—jobs, hires, companies, ideas, partners, etc.—have a distribution of outcomes where the best possible choices are outliers that are dramatically better than the typical ones. In my case, for example, choosing to work at Wave was probably 10x better than staying at my previous employer: I learned more, gained responsibility faster, had a bigger impact on the world, etc.
Curated. The question of inside view vs outside-view and expert deference vs own models has been debated before on LessWrong (and EA Forum), but this post does a superb job of making the case for the “use your own models more, trust your own information, be willing to go against the crowd and experts”. It articulates the case clearly and crisply, in a way that I think is possibly more compelling than other sources.
A few points I particularly like:
The identification of selection bias on evidence in different directions:
That relying on mainstream/expert views won’t allow for finding outliers, and finding outliers is crucial to outsized impact: