LessWrong 2.0 Reader

The Brownian motion assumption is rather strong but not required for the conclusion. Consider the stock market, which famously has heavy-tailed, bursty returns. It happens all the time for the S&P 500 to move 1% in a week, but a 10% move in a week only happens a couple of times per decade. I would guess (and we can check) that most weeks have >0.6x of the average per-week variance of the market, which causes the median weekly absolute return to be well over half of what it would be if the market were Brownian motion with the same long-term variance.

Also, Lawrence tells me that in Tetlock's studies, superforecasters tend to make updates of 1-2% every week, which actually improves their accuracy.

Clive Wearing's story might be interesting to you: https://m.youtube.com/watch?v=k_P7Y0-wgos&feature=youtu.be

O man, wait until you discover nmda antagonists and anti-cholinergics.  There are trip reports on erowid from people who took drugs with amnesia as a side effect so...happy reading I guess?

I'm going to summarize this post with "Can one of you take an online IQ test after dropping a ton of benzos and report back?  Please do this several times, for science."

Not the stupidest or most harmful 'lets get high and...' suggestion, but I can absolutely assure you that if trying this leads you into the care of a medical or law enforcement professional, they will likely say something to the effect of 'so the test told you that you were retarded right?'  In response to this, you, with bright naive eyes, should say 'HOW DID YOU KNOW?!' as earnestly as you can.  You might be able to make a run for it while they're laughing.

I think this is a really interesting idea, but I'm not comfortable enough with drugs to test it myself. If anyone is doing this and wants psychometric advice, though, I am offering to join your project.

I talked about this with Lawrence, and we both agree on the following:

• There are mathematical models under which you should update >=1% in most weeks, and models under which you don't.
• Brownian motion gives you 1% updates in most weeks. In many variants, like stationary processes with skew, stationary processes with moderately heavy tails, or Brownian motion interspersed with big 10%-update events that constitute <50% of your variance, you still have many weeks with 1% updates. Lawrence's model where you have no evidence until either AI takeover happens or 10 years passes does not give you 1% updates in most weeks, but this model is almost never the case for sufficiently smart agents.
• Superforecasters empirically make lots of little updates, and rounding off their probabilities to larger infrequent updates make their forecasts on near-term problems worse.
• Thomas thinks that AI is the kind of thing where you can make lots of reasonable small updates frequently. Lawrence is unsure if this is the state that most people should be in, but it seems plausibly true for some people who learn a lot of new things about AI in the average week (especially if you're very good at forecasting). 
• In practice, humans often update in larger discrete chunks. Part of this is because they only consciously think about new information required to generate new numbers once in a while, and part of this is because humans have emotional fluctuations which we don't include in our reported p(doom).
• Making 1% updates in most weeks is not always just irrational emotional fluctuations; it is consistent with how a rational agent would behave under reasonable assumptions. However, we do not recommend that people consciously try to make 1% updates every week, because fixating on individual news articles is not the right way to think about forecasting questions, and it is empirically better to just think about the problem directly rather than obsessing about how many updates you're making.

I think the proposed method could still work though. A substantial fraction of the pseudorandomness may be consistent on the individual person level.

The type of pseudorandomness you describe here ought to be independent at the level of individual items, so it ought to be part of the least-reliable variance component (not part of the general trait measured and not stable over time). It's possible to use statistics to estimate how big an effect it has on the scores, and it's possible to drive it arbitrarily far down in effect simply by making the test longer.

The title is strong with this one. I like it.

I wish there were more discussion posts on LessWrong.

Right now it feels like it weakly if not moderately violates some sort of cultural norm to publish a discussion post (similar but to a lesser extent on the Shortform). Something low effort of the form "X is a topic I'd like to discuss. A, B and C are a few initial thoughts I have about it. Thoughts?"

It seems to me like something we should encourage though. Here's how I'm thinking about it. Such "discussion posts" currently happen informally in social circles. Maybe you'll text a friend. Maybe you'll bring it up at a meetup. Maybe you'll post about it in a private Slack group.

But if it's appropriate in those contexts, why shouldn't it be appropriate on LessWrong? Why not benefit from having it be visible to more people? The more eyes you get on it, the better the chance someone has something helpful, insightful, or just generally useful to contribute.

The big downside I see is that it would screw up the post feed. Like when you go to lesswrong.com and see the list of posts, you don't want that list to have a bunch of low quality discussion posts you're not interested in. You don't want to spend time and energy sifting through the noise to find the signal.

But this is easily solved with filters. Authors could mark/categorize/tag their posts as being a low-effort discussion post, and people who don't want to see such posts in their feed can apply a filter to filter these discussion posts out.

The LessWrong Review [? · GW] runs every year to select the posts that have most stood the test of time. This post is not yet eligible for review, but will be at the end of 2024. The top fifty or so posts are featured prominently on the site throughout the year. Will this post make the top fifty?

It definitely should not move by anything like a Brownian motion process. At the very least it should be bursty and updates should be expected to be very non-uniform in magnitude.

In practice, you should not consciously update very often since almost all updates will be of insignificant magnitude on near-irrelevant information. I expect that much of the credence weight turns on unknown unknowns, which can't really be updated on at all until something turns them into (at least) known unknowns.

But sure, if you were a superintelligence with practically unbounded rationality then you might in principle update very frequently.