There's a kind of bait-and-switch going on here. > A small research community of unusually smart/competent/well-informed people can relatively-easily outperfor

There's a kind of bait-and-switch going on here. > A small research community of unusually smart/competent/well-informed people can relatively-easily outperform a whole field, by having better internal memetic selection pressures. Sure, that's true. > In particular, LessWrong sure seems like such a community. HOLD ON THERE. The first thing you said: easily true. The second thing: show your proof! Here it is: > We have a user base with probably-unusually-high intelligence, community norms which require basically everyone to be familiar with statistics and economics, we have fuzzier community norms explicitly intended to avoid various forms of predictable stupidity, and we definitely have our own internal meme population. Uh, let's rework that into a more plausible, pliable form - "steelman" if you will. > We have a user base with probably-unusually-high intelligence, community norms which require basically everyone to be familiar with statistics and economics, and fuzzier community norms explicitly intended to avoid various forms of predictable stupidity. Line by line: > We have a user base with probably-unusually-high intelligence What, for like... all fields? This needs to be compared with specific fields. And shown in a less hand-wavy way how lesswrong scores meaningfully better than academic researchers with more subject matter expertise. > community norms which require basically everyone to be familiar with statistics and economics The problem is right there in the text you cited: memeticity [on lesswrong] is mostly determined, not by the most competent researchers, but instead by roughly-median researchers. Also "familiar" != learned well. > fuzzier community norms explicitly intended to avoid various forms of predictable stupidity I award zero points for this, especially when compared to an academic community which has similar (plausibly better) training, which better understands the pitfalls of data collection in their field.