I spend most of time forecasting stuff related to the pandemic. Unfortunately this probably makes my conversations and comments less interesting.

I also write on Quora.

Linch's Comments

Are there superforecasts for existential risk?

I find it hard to parse where our disagreement is. Can you point me to what quote you disagree with?

Are there superforecasts for existential risk?

I'm not sure. I mentioned as a reply to that comment that I was unimpressed with the ability of existing "good" forecasters to think about low-probability and otherwise out-of-distribution problems. My guess is that they'd change their minds if "exposed" to all the arguments, and specifically have views very close to the median FHI view, if "exposed" -> reading the existing arguments very carefully and put lots of careful thought into them. However, I think this is a very tough judgement call, and does seem like the type of thing that'd be really bad if we get it wrong!

My beliefs here are also tightly linked to me thinking that the median FHI view is more likely to be correct than Will's view, and it is a well-known bias that people think their views are more common/correct than they actually are.

Are there superforecasts for existential risk?

As someone who is one of the most active users of Metaculus (particularly 2 months ago), I find a lot of the Metaculus estimates on existential risk pretty suspicious:

  • There's no incentive to do well on those questions.
  • The feedback loops are horrible
    • Indeed, some people have actually joked betting low on the more existential questions since they won't get a score if we're all dead (at least, I hope they're joking)
  • At the object-level, I just think people are really poorly calibrated about x-risk questions
    • My comment here arguably changed the community's estimates by ~10%
      • It wasn't a very insightful comment by EA standards
  • People are not well-trained on low probabilities
    • Both that they literally have no way of doing so on Metaculus (can't go <1% or >99%) and that they haven't predicted enough questions to be calibrated at those levels.
  • My inside view is that people on Metaculus are more well-read than most people on existential risk matters, but probably on average less well-read than, eg, your typical organizer of a EA local or college group.
  • My guess is that as of summer 2020, there's no strong reason to think Metaculus will do better than a group of well-read EAs on existential risk questions.
I'm Linch Zhang, an amateur COVID-19 forecaster and generalist EA. AMA
Is forecasting plausibly a high-value use of one's time if one is a top-5% or top-1% forecaster?

Yes, it's plausible.

What are the most important/valuable questions or forecasting tournaments for top forecasters to forecast or participate in?

My sense is that right now there's a market mismatch with an oversupply of high forecasting talent relative to direct demand/actual willingness/ability to use said talent. I'm not sure why this is, intuitively there are so many things in the world where having a higher-precision understanding of our uncertainty is just extremely helpful.

One thing I'd love to do is help figure out how to solve this and find lots of really useful things for people to forecast on.

I'm Linch Zhang, an amateur COVID-19 forecaster and generalist EA. AMA

Footnote on why this scenario

an expert (or a prediction market median) is much stronger than you, but you have a strong inside view

I think is in practice uncommon:

I think the ideal example in my head for showcasing what you describe goes something like this:

  • An expert/expert consensus/prediction market median that I respect strongly (as predictors) have high probability on X
  • I strongly believe not X. (or equivalently, very low probability on X).
  • I have strong inside views for why I believe not X.
  • X is the answer to a well-operationalized question
  • with a specific definition...
  • that everybody on the definition of.
  • I learned about the expert view very soon after they made it
  • I do not think there is new information that the experts are not updating on
  • This question's answer has a resolution in the near future, in a context that I have both inside-view and outside-view confidence in our relative track records (in either direction).

I basically think that there are very few examples of situations like this, for various reasons:

  • For starters, I don't think I have very strong inside views on a lot of questions.
  • Though sometimes the outside views look something like "this simple model predicts stuff around X, and the outside view is that this class of simple models outpredict both experts and my own more complicated models "
  • Eg, 20 countries have curves that look like this, I don't have enough Bayesian evidence that this particular country's progression will be different.
  • There are also weird outside views on people's speech acts, for example "our country will be different" is on a meta-level something that people from many countries believe, and this conveys almost no information
  • These outsideish views can of course be wrong (for example I was wrong about Japan and plausibly Pakistan).
  • Unfortunately, what is and isn't a good outside view is often easy to self-hack by accident.
  • Note that outside view doesn't necessarily look like expert deference.
  • Usually if there are experts or other aggregations whose opinion as forecasters that I strongly respect, I will just defer to them and not think that much myself
  • For example I'm deferring serious thinking around the 2020 election because I basically think has "got this."
  • I mostly select easier/relatively neglected domains to forecast on, at least with "ease" defined as "the market looks basically efficient"
  • Eg, I stay away from financial and election forecasts
  • A lot of the time, when experts say something that I think is wildly wrong and I dig into it further, it turns out they said it Y days/weeks ago, and I've already heard contradictory evidence that updated my internal picture since (and presumably the experts as well).

A caveat to all this is that I'm probably not as good at deferring to the right experts as many EA Forum users. Perhaps if I was better at it ("it" being identifying/deeply interpreting the right experts), I will feel differently.

I'm Linch Zhang, an amateur COVID-19 forecaster and generalist EA. AMA

I know this isn't the answer you want, but I think the short answer here is that I really don't know, because I don't think this situation is common. so I don't have a good reference class/list of case studies to describe how I'd react in this situation.

If this were to happen often for a specific reference class of questions (where some people just very obviously do better than me for those questions), I imagine I'd quickly get out of the predictions business for those questions, and start predicting on other things instead.

As a forecaster, I'm mostly philosophically opposed to updating strongly (arguably at all) based on other people's predictions. If I updated strongly, I worry that this will cause information cascades.

However, if I was in a different role, eg making action-relevant decisions myself, or "representing forecasters" to decision-makers, I might try to present a broader community view, or highlight specific experts.

Past work on this includes comments on Greg Lewis's excellent EA forum article on epistemic modesty, Scott Sumner on why the US Fed should use market notions of monetary policy rather than what the chairperson of the Fed believes and notions of public vs. private uses of reason by Immanuel Kant.

I also raised this question on Metaculus.

3 suggestions about jargon in EA

Is discourse around lying/concealing information out of altruistic concern really that rare in Western cultures?

I'm Linch Zhang, an amateur COVID-19 forecaster and generalist EA. AMA
How correlated is skill at forecasting and strategy games?

I’m not very good at strategy games, so hopefully not much!

The less quippy answer is that strategy games are probably good training grounds for deliberate practice and quick optimization loops, so that likely counts for something (see my answer to Nuno about games). There are also more prosaic channels, like general cognitive ability and willingness to spend time in front of a computer.

Does playing strategy games make you better at forecasting?

I’m guessing that knowing how to do deliberate practice and getting good at a specific type of optimization is somewhat generalizable, and it's good to do that in something you like (though getting good at things you dislike is also plausibly quite useful). I think specific training usually trumps general training, so I very much doubt playing strategy games is the most efficient way to get better at forecasting, unless maybe you’re trying to forecast results of strategy games.

I'm Linch Zhang, an amateur COVID-19 forecaster and generalist EA. AMA
How is your experience acquiring expertise at forecasting similar/different to acquiring expertise in other domains, e.g. obscure board-games? How so?

Just FYI, I do not consider myself an "expert" on forecasting. I haven't put my 10,000 hours in, and my inside view is that there's so much ambiguity and confusion about so many different parameters. I also basically think judgmental amateur forecasting is a nascent field and there are very few experts[1], with the possible exception of the older superforecasters. Nor do I actually think I'm an expert in those games, for similar reasons. I basically think "amateur, but first (or 10th, or 100th, as the case might be) among equals" is a healthier and more honest presentation.

That said, I think the main commonalities for acquiring skill in forecasting and obscure games include:

  • Focus on generalist optimization for a well-specified score in a constrained system
    • I think it's pretty natural for both humans and AI to do better in more limited scenarios.
    • However, I think in practice, I am much more drawn to those types of problems than my peers (eg I have a lower novelty instinct and I enjoy optimization more).
  • Deliberate practice through fast feedback loops
    • Games often have feedback loops on the order of tens of seconds/minutes (Dominion) or hundreds of milliseconds/seconds (Beat Saber)
    • Forecasting has slower feedback loops, but often you can form an opinion in <30 minutes (sometimes <3 if it's a domain you're familiar with), and have it checked in a few days.
    • In contrast, the feedback loops for other things EA are interested in are often much slower. For example, research might have initial projects on the span of months and have it checked in the span of years, architecture in software engineering might take days to do and weeks to check (and sometimes the time to check is never)
  • Focus on easy problems
    • For me personally, it's often easier for me to get "really good" on less-contested domains than kinda good on very contested domains
      • For example, I got quite good at Dominion but I bounced pretty quickly off Magic, and I bounced (after a bunch of frustration) off chess.
      • Another example: in Beat Saber rather than trying hard to beat the harder songs, I spent most of my improving time on getting very high scores for the easier songs
      • In forecasting, this meant that making covid-19 forecasts 2-8 weeks out was more appealing than making geopolitical forecasts on the timescale of years, or technological forecasts on the timescale of decades
    • This allowed me to slowly and comfortably move into harder questions
      • For example now I have more confidence and internal models on predicting covid-19 questions multiple months out.
      • If I were to get back into Beat Saber, I'd be a lot less scared of the harder songs than I used to be (after some time ramping back up).
    • I do think not being willing to jump into harder problems directly is something of a character flaw. I'd be interested in hearing other people's thoughts on how they do this.

The main difference, to me is that:

  • Forecasting relies on knowledge of the real world
    • As opposed to games (and for that matter programming challenges) the "system" that you're forecasting on is usually much more unbounded.
    • So knowledge acquisition and value-of-information is much more important per question
    • This is in contrast to games, where knowledge acquisition is important on the "meta-level" but for any specific game,
      • balancing how much knowledge you need to acquire is pretty natural/intuitive.
      • and you probably don't need much new knowledge anyway.

[1] For reasons I might go into later in a different answer

I'm Linch Zhang, an amateur COVID-19 forecaster and generalist EA. AMA
What do you think helps make you a better forecaster than the other 989+ people?

I'll instead answer this as:

What helps you have a higher rating than most of the people below you on the leaderboard?
  • I probably answered more questions than most of them.
  • I update my forecasts more quickly than most of them, particularly in March and April
    • Activity has consistently been shown to be one of (often, the) strongest predictors of overall accuracy in the academic literature.
  • I suspect I have a much stronger intuitive sense of probability/calibration.
    • For example, 17% (1:5) intuitively feels very different to me than 20% (1:4), and my sense is that this isn't too common
    • This could just be arrogance however, there isn't enough data for me to actually check this for actual predictions (as opposed to just calibration games)
  • I feel like I actually have lower epistemic humility compared to most forecasters who are top 100 or so on Metaculus. "Epistemic humility" defined narrowly as "willingness to make updates based on arguments I don't find internally plausible just because others believed them."
    • Caveat is that I'm making this comparison solely to top X% (in either activity or accuracy) forecasters.
      • I suspect a fair number of other forecasters are just wildly overconfident (in both senses of the term)
      • Certainly, non-forecasters (TV pundits, say, or just people I see on the internet) frequently seem very overconfident for what seems to me like bad reasons.
      • A certain epistemic attitude that I associate with both Silicon Valley and Less Wrong/rationalist culture is "strong opinions, held lightly"
        • This is where you believe concrete, explicit and overly specific models of the world strongly, but you quickly update whenever someone points out a flaw
        • I suspect this attitude is good for things like software design and maybe novel research, but is bad for having good explicit probabilities for Metaculus-style questions.
  • I'm a pretty competitive person, and I care about scoring well.
    • This might be surprising, but I think a lot of forecasters don't.
    • Some forecasters just want to record their predictions publicly and be held accountable to them, or want to cultivate more epistemic humility by seeing themselves be wrong
      • I think these are perfectly legitimate uses of forecasting, and I actively encourage my friends to use Metaculus and other prediction platforms to do this.
      • However, it should not be surprising that people who want to score well end up on average scoring better.
    • So I do a bunch of things like meditate on my mistakes and try really hard to do better. I think most forecasters, including good ones, do this much less than I do.
  • I know more facts about covid-19.
    • I think the value of this is actually exaggerated, but it probably helps a little.


What do you think other forecasters do to make them have a higher rating than you? [Paraphrased]

Okay, a major caveat here is that I think there is plenty of heterogeneity among forecasters. Another is that I obviously don't have clear insight into why other forecasters are better than me (otherwise I'd have done better!) However, in general I'm guessing they:

  • Have more experience with forecasting.
    • I started in early March and I think many of them have already been forecasting for a year or more (some 5+ years!).
    • I think experience probably helps a lot in building intuition and avoiding a lot of subtle (and not-so-subtle!) mistakes.
  • They usually forecast more questions.
    • It takes me some effort to forecast on new questions, particularly if the template is different from other questions I've forecasted on before, and they aren't something I've thought about before in a non-forecasting context
    • I know some people in the Top 10 literally forecast all questions on Metaculus, which seems like a large time commitment to me.
  • They update forecasts more quickly than me, particularly in May and June.
    • Back in March and April, I was *super* "on top of my game." But right now I have a backlog of old predictions, of which I'm >30 days behind on the earliest one (as in, the last time I updated that prediction was 30+ days ago).
    • This is partially due to doing more covid forecasting on day job, partially due to having some other hobbies, and partially due to general fatigue/loss of interest (akin to lockdown fatigue from others)
  • On average, they're more inclined to do simple mathematical modeling (Guesstimate, Excel, Google Sheets, foretold etc), whereas personally I'm often (not always) satisfied with a few jotted notes on a Google Doc plus a simple arithmetic calculator.

There are also more specific reasons some other forecasters are better than me, but I don't think all or even most of the forecasters better than me have:

  • JGalt seems to read the news both more and more efficiently than I do, and probably knows much more factual information than me.
    • In particular, I recall many times where I see interesting news on Twitter or other places, want to bring it Metaculus, and bam, JGalt has already linked it ahead of me.
      • This is practically a running meme among Metaculus users that JGalt has read all the news.
  • Lukas Gloor and Pablo Stafforini plausibly has a stronger internal causal model of various covid-19 related issues.
  • datscilly often decomposes questions more cleanly than me, and (unlike me and several other forecasters), appears to aggressively prioritize not updating on irrelevant information.
    • He also cares about scores more than I do.
  • I think Pablo, datscilly and some others started predicting on covid-19 questions almost as soon as the pandemic started, so they built up more experience than me not only on general forecasting, but also on forecasting covid-19 related questions specifically.

At least this is what I can gather from their public comments and (in some cases) private conversations. It's much harder for me to interpret how forecasters higher than me on the leaderboard but are otherwise mostly silent think.

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