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When trying to coordinate with others, we often assume the default should be full cooperation ("stag hunting"). Raemon argues this isn't realistic - the default is usually for people to pursue their own interests ("rabbit hunting"). If you want people to cooperate on a big project, you need to put in special effort to get buy-in.
When disagreements persist despite lengthy good-faith communication, it may not just be about factual disagreements – it could be due to people operating in entirely different frames — different ways of seeing, thinking and/or communicating.
Nine parables, in which people find it hard to trust that they've actually gotten a "yes" answer.
Concerningly, it can be much easier to spot holes in the arguments of others than it is in your own arguments. The author of this post reflects that historically, he's been too hasty to go from "other people seem very wrong on this topic" to "I am right on this topic".
Integrity isn't just about honesty - it's about aligning your actions with your stated beliefs. But who should you be accountable to? Too broad an audience, and you're limited to simplistic principles. Too narrow, and you miss out on opportunities for growth and collaboration.
Building gears-level models is expensive - often prohibitively expensive. Black-box approaches are usually cheaper and faster. But black-box approaches rarely generalize - they need to be rebuilt when conditions change, don’t identify unknown unknowns, and are hard to build on top of. Gears-level models, on the other hand, offer permanent, generalizable knowledge which can be applied to many problems in the future, even if conditions shift.
Building gears-level models is expensive - often prohibitively expensive. Black-box approaches are usually cheaper and faster. But black-box approaches rarely generalize - they need to be rebuilt when conditions change, don’t identify unknown unknowns, and are hard to build on top of. Gears-level models, on the other hand, offer permanent, generalizable knowledge which can be applied to many problems in the future, even if conditions shift.
I've wrestled with applying ideas like "conservation of expected evidence," and want to warn others about some common mistakes. Some of the "obvious inferences" that seem to follow from these ideas are actually mistaken, or stop short of the optimal conclusion.
Ben and Jessica discuss how language and meaning can degrade through four stages as people manipulate signifiers. They explore how job titles have shifted from reflecting reality, to being used strategically, to becoming meaningless.
This post kicked off subsequent discussion on LessWrong about simulacrum levels.
It might be the case that what people find beautiful and ugly is subjective, but that's not an explanation of ~why~ people find some things beautiful or ugly. Things, including aesthetics, have causal reasons for being the way they are. You can even ask "what would change my mind about whether this is beautiful or ugly?". Raemon explores this topic in depth.
Since middle school I've thought I was pretty good at dealing with my emotions, and a handful of close friends and family have made similar comments. Now I can see that though I was particularly good at never flipping out, I was decidedly not good "healthy emotional processing".
Some people use the story of manioc as a cautionary tale against innovating through reason. But is this really a fair comparison? Is it reasonable to expect a day of untrained thinking to outperform hundreds of years of accumulated tradition? The author argues that this sets an unreasonably high bar for reason, and that even if reason sometimes makes mistakes, it's still our best tool for progress.
Many people in the rationalist community are skeptical that rationalist techniques can really be trained and improved at a personal level. Jacob argues that rationality can be a skill that people can improve with practice, but that improvement is difficult to see in aggregate and requires consistent effort over long periods.
Collect enough data about the input/output pairs for a system, and you might be able predict future input-output pretty well. However, says John, such models are vulnerable. In particular, they can fail on novel inputs in a way that models that describe what actually is happening inside the system won't; and people can make pretty bad inferences from them, e.g. economists in the 70s about inflation/unemployment. See the post for more detail.