I suggest renaming the “Incidental anchoring” section to something else, such as “irrelevant anchors” or “transparently random anchors”, since the term “incidental anchoring” is used to refer to something else.
Also, one of the classic 1970s Kahneman & Tversky anchoring studies used a (apparently) random wheel of fortune to generate a transparently irrelevant anchor value—the one on African countries in the UN. When this came up on LW previously, it turned out that Andrew Gelman used it as an in-class demo and (said that he) generally found effects in the predicted direction (though instead of spinning a viscerally random wheel they just handed each student a piece of paper that included the sentences “We chose (by computer) a random number between 0 and 100. The number selected and assigned to you is X = ___”).
A 2008 paper found anchoring effects from these kinds of “incidental environmental anchors”, but then a replication of one of its studies with a much larger sample size found no effect (see “9. Influence of incidental anchors on judgment (Critcher & Gilovich, 2008, Study 2)”).
So that at least says something about why the people running your forecasting workshop thought this would have an effect, and provides some entry points into the published research which someone could look into in more depth, but it still leaves it surprising/confusing that there was such a large difference.
Anna & Val taught goal factoring at the first CFAR workshop (May 2012). I’m not sure if they used the term “goal factoring” at the workshop (the title on the schedule was “Microeconomics 1: How to have goals”), but that’s what they were calling it before the workshop including in passing on LW. Geoff attended the third CFAR workshop as a participant and first taught goal factoring at the fourth workshop (November 2012), which was also the first time the class was called “Goal Factoring”. Geoff was working on similar stuff before 2012, but I don’t know enough of the pre-2012 history to know if there was earlier cross-pollination between Geoff & CFAR folks.
Critch developed aversion factoring.
The intense world theory sounds similar to Eysenck’s theory of introversion vs extraversion from the 1960s. One summary:
Eysenck (2008/1967) believed that differences in extraversion were due to physiological differences in brain systems that caused some people to be more easily aroused than others. Specifically, he proposed that the ascending reticular activating system (ARAS) regulated arousal. People differ in the sensitivity of this system, which causes them to respond differently to their environment. A moderately stimulating environment might cause people with a very sensitive ARAS to feel overstimulated and retreat (i.e., to behave like introverts), but people with a less sensitive ARAS to feel understimulated and seek out additional stimulation (i.e., to behave like extraverts).
The inverted U arousal graph is typically plotted as Performance vs. Arousal and called the Yerkes–Dodson law.
Another summary here; more findable with keywords like “optimal level of arousal”, Eysenck, and extraversion.
It sounds like you were relying pretty heavily on the amount of alarm in the media as one of your main indicators of how much to worry (while using an interpretive filter). What you took from the swine flu example is that the media tends to be too alarmist, but you also/instead could’ve concluded that the media is not very good at risk assessment (and maybe isn’t even trying that hard to do good risk assessments). The line of reasoning that this new virus is probably less dangerous than swine flu because it’s less media hype depends on the assumption that the level of alarm in the media is strongly correlated with the level of danger (with a systematic bias towards exaggerated alarm); I think the correlation between media alarm and danger is not that strong in which case this argument doesn’t go through. So, the media isn’t that functional as an alarm, and you need some other approach for figuring out if there’s a big problem. Really bad pandemics are possible, and the amount of alarm in the media isn’t that strong an indicator of whether a new virus is likely to turn into a bad pandemic, so how could I tell if one is coming? You maybe could still use the media as an initial alert: the media is alarmed about this thing, and it is the sort of thing that has the potential to be really bad, so I’ll take that as my cue to put effort into understanding what’s going on (via some other approach that doesn’t rely on the media). Or, you could try to be plugged into other information environments which are more reliable, such that you’d trust them more if they raised an alarm. I benefited from hearing things like this and this, and similar things by word-of-mouth & ephemeral Facebook posts.
It helps to think in terms of probabilities & expected values. Scott wrote about this in some depth in A Failure, But Not of Prediction. For example: If swine flu turned out to be unimportant after the media hyped it up, that gives reason to think that the probability that the next media-hyped virus will be really bad is more like 25% than 75%. But it doesn’t give much reason to think that the probability is more like 1% than 25% - not enough data for that. And if you see the probability that a novel coronavirus will turn into a really bad pandemic as being as high as 25%, then it’s worth investigating & preparing for.
It sounds like a big part of your lack of concern is that you thought the illness wasn’t that serious, so that even if the virus became widespread it wouldn’t affect you much. My memory is that this is different from the reasoning of most people who weren’t very concerned, as it was more common to think that the virus wouldn’t become widespread. So (a) this mismatch seems like a clue that something might be up, and worth looking into. And (b) I think there were reasons to think that the virus would be a big deal if it became widespread, e.g. the lives of people in Wuhan had changed in pretty drastic ways as a result of the virus.
A few things that seem relevant (5 things, but maybe not crystallized in the right way to be 5 separate answers to the OP’s question):
Quantity: mismatch between natural quantity produced and quantity desired. Maybe I can plant an apple tree & pick the apples, but I don’t want a whole treeful of apples. Maybe the most efficient way to make t-shirts is to build a big machine that makes a million shirts, and a process for making just 10 of them is wildly inefficient in terms of resources per shirt. (related keywords: economies of scale, capital investments)
Timing: maybe I want something now & it would take half an hour for me to make it (and what I’ll want is unpredictable, I can’t keep a giant inventory of everything I might want—though maybe the “all of us liked exactly the same objects exactly the same amount” supposition erases this issue). If I need to plant a tree and wait for it to grow apples that’ll take years. Building the giant t-shirt machine might involve more than a single lifetime of person-hours of work.
Some things you can’t do for yourself: if I Matrix-learn how to give good massages, that won’t let me get a good massage—I need someone else to do that. That requires making some kind of deal with another person (maybe I give them a massage some other time?), which is at least kind of like trade. What counts as “trade”? Some simple cases seem not that trade-like, e.g. I play tennis with someone because you can’t play a tennis match by yourself, although we could frame it as trade-like: I’m providing them with a tennis opponent and in exchange they’re providing me with a tennis opponent. The massage exchange seems more trade-like (because asynchronous?), other cases where we aren’t just exchanging the exact same service even more so.
Some production involves multiple people: Maybe it takes multiple people to carry a heavy object, or to pick apples (one in the tree & one on the ground with a basket?), or to operate a giant machine. Or people decide they’d rather do it together with other people (e.g. because the total quantity produced is more than any one person wants, or to finish the job more quickly). So there needs to be some kind of deal between the people on how to divide up what they produce. That also seems kind of like trade. Less so in some simple cases (a group of people dividing up the work equally and then dividing up the output equally), more so as it gets more complicated.
Complicated many-person coordination: Maybe a bunch of people work together to build, supply, and operate the giant t-shirt machine and divide the t-shirts between themselves. But the most efficient way to get the screws for the machine is with a giant machine that produces way more screws than the t-shirt machine needs, so most of the screws are used for other purposes. And the most efficient way to make the fertilizer for the cotton fields involves making way more fertilizer than is needed for the t-shirt cotton. Etc. So we have multiple giant teams, partially overlapping, e.g the screw-machine-makers have a small role in the t-shirt production & in the production of many other things, and so get a little share of each. With long supply chains this might look more like screw-machine-makers bartering screws for t-shirts rather than screw-machine-makers being part of the t-shirt team. And if you think about schemes for arranging all of this, those can start to look more like trade and an economy, e.g. all these people communicating & coordinating to figure out how much to make of each thing and where to send it and so on might want to use something that looks a lot like prices (related keywords: the knowledge problem).
Also in early 2019, Kelsey Piper’s article Biologists are trying to make bird flu easier to spread. Can we not? was published at Vox (Future Perfect).
That’s a Nas sample. You might like Illmatic.
A Paul Graham essay which is more directly related to this topic is How to Write Usefully:
Useful writing makes claims that are as strong as they can be made without becoming false.For example, it’s more useful to say that Pike’s Peak is near the middle of Colorado than merely somewhere in Colorado. But if I say it’s in the exact middle of Colorado, I’ve now gone too far, because it’s a bit east of the middle.Precision and correctness are like opposing forces. It’s easy to satisfy one if you ignore the other. The converse of vaporous academic writing is the bold, but false, rhetoric of demagogues. Useful writing is bold, but true.
Useful writing makes claims that are as strong as they can be made without becoming false.
For example, it’s more useful to say that Pike’s Peak is near the middle of Colorado than merely somewhere in Colorado. But if I say it’s in the exact middle of Colorado, I’ve now gone too far, because it’s a bit east of the middle.
Precision and correctness are like opposing forces. It’s easy to satisfy one if you ignore the other. The converse of vaporous academic writing is the bold, but false, rhetoric of demagogues. Useful writing is bold, but true.
The Nudgerism section seems to be mushing together various psychology-related things which don’t have much to do with nudging.
Things like downplaying risks in order to prevent panic are at most very loosely related to nudging, and at least as ancient as the practice of placing objects at eye-level. Seems like an over-extension of focusing on “morale” and other Leaders of Men style attributes.
The main overlaps between the book Nudge and the awful The Cognitive Bias That Makes Us Panic About Coronavirus Bloomberg article are 1) they were both written by Cass Sunstein and 2) the one intervention that’s explicitly recommended in the Bloomberg article is publicizing accurate information about coronavirus risk probabilities.
One of the main themes of the nudge movement is that human behavior is an empirical field that can be studied, and one of the main flaws of the thing being called “nudgerism” is making up ungrounded (and often inaccurate) stories about how people will behave (such as what things will induce a “false sense of security”). These stories often are made by people without relevant expertise who don’t even seem to be trying very hard to make accurate predictions.
The British government has a Behavioural Insights Team which is colloquially known as the Nudge Unit; I’d guess that they didn’t have much to do with the screwups that are being called “nudgerism.”
I expect it will be easier to get Metaculus users to make forecasts on pundits’ questions than to get pundits to make forecasts on each other’s questions.
Suggested variant (with dates for concreteness):
Dec 1: deadline for pundits to submit their questionsDec 10: metaculus announces the final version of all the questions they’re using, but does not open marketsDec 20: deadline for pundits & anyone else to privately submit their forecasts (maybe hashed), and metaculus markets openDec 31: current metaculus consensus becomes the official metaculus forecast for the questions, and pundits (& anyone else) can publicize the forecasts that they made by Dec 20
Contestants (anyone who submitted forecasts by Dec 20) mainly get judged based on how they did relative to the Dec 31 metaculus forecast. I expect that they will mostly be pundits making forecasts on their own questions, plus forecasting aficionados.
(We want contestants & metaculus to make their forecasts simultaneously, with neither having access to the other’s forecasts, which is tricky since metaculus is a public platform. That’s why I have the separate deadlines on Dec 20 & Dec 31, with contestants’ forecasts initially private—hopefully that’s a short enough time period so that not much new information should arise, and long enough for people to have time to make forecasts.)
With only a small sample size of questions, it may be more meaningful to evaluate contestants based on how close they came to the official metaculus forecast rather than on how accurate they were (there’s a bias-variance tradeoff). As a contestant does more questions (this year or over multiple years), the comparison with what actually happened becomes more meaningful.
Maybe a nitpick, but the driver’s license posterior of 95% seems too high. (Or at least the claim isn’t stated precisely.) I’d have less than a 95% success rate at guessing the exact name string that appears on someone’s driver’s license. Maybe there’s a middle name between the “Mark” and the “Xu”, maybe the driver’s license says “Marc” or “Marcus”, etc.
I think you can get to 95% with a phone number or a wifi password or similar, so this is probably just a nitpick.
Although maybe not that disproportionate—one recent post was throwing off the search results. Without it, rationalish subreddits still show up a few times on the first couple pages of search results, but not overwhelmingly.
Searching for the phrase on Reddit does turn up a disproportionate number of hits from /r/slatestarcodex. So not LW-exclusive, but maybe unusually common around here. Possibly traceable to Weak Men Are Superweapons:
What is the problem with statements like this?First, they are meant to re-center a category. Remember, people think in terms of categories with central and noncentral members – a sparrow is a central bird, an ostrich a noncentral one. But if you live on the Ostrich World, which is inhabited only by ostriches, emus, and cassowaries, then probably an ostrich seems like a pretty central example of ‘bird’ and the first sparrow you see will be fantastically strange.Right now most people’s central examples of religion are probably things like your local neighborhood church. If you’re American, it’s probably a bland Protestant denomination like the Episcopalians or something.The guy whose central examples of religion are Pope Francis and the Dalai Lama is probably going to have a different perception of religion than the guy whose central examples are Torquemada and Fred Phelps. If you convert someone from the first kind of person to the second kind of person, you’ve gone most of the way to making them an atheist.
What is the problem with statements like this?
First, they are meant to re-center a category. Remember, people think in terms of categories with central and noncentral members – a sparrow is a central bird, an ostrich a noncentral one. But if you live on the Ostrich World, which is inhabited only by ostriches, emus, and cassowaries, then probably an ostrich seems like a pretty central example of ‘bird’ and the first sparrow you see will be fantastically strange.
Right now most people’s central examples of religion are probably things like your local neighborhood church. If you’re American, it’s probably a bland Protestant denomination like the Episcopalians or something.
The guy whose central examples of religion are Pope Francis and the Dalai Lama is probably going to have a different perception of religion than the guy whose central examples are Torquemada and Fred Phelps. If you convert someone from the first kind of person to the second kind of person, you’ve gone most of the way to making them an atheist.
It’s not a LW-distinctive phrase. Try searching Google News, for instance. It falls out of spatial models of concepts such as prototype theory, e.g. a robin is a central example of a bird while an ostrich is not.
The “all other money moved” bars on the first GiveWell graph (which I think represent donations from individual donors) do look a lot like exponential growth. Except 2015 was way above the trend line (and 2014 & 2016 a bit above too).
If you take the first and last data points (4.1 in 2011 & 83.3 in 2019), it’s a 46% annual growth rate.
If you break it down into four two-year periods (which conveniently matches the various little sub-trends), it’s:
2011-13: 46% annual growth (4.1 to 8.7)2013-15: 123% annual growth (8.7 to 43.4)2015-17: 3% annual growth (43.4 to 45.7)2017-19: 35% annual growth (45.7 to 83.3)
2019 “all other money moved” is exactly where you’d project if you extrapolated the 2011-13 trend, although it does look like the trend has slowed a bit (even aside from the 2015 outlier) since 35% < 46%.
If GiveWell shares the “number of donors” count for each year that trend might be smoother (less influenced by a few very large donations), and more relevant for this question of how much EA has been growing.
Funding from Open Phil / Good Ventures looks more like a step function, with massive ramping up in 2013-16 and then a plateau (with year-to-year noise). Which is what you might expect from a big foundation—they can ramp up spending much faster than what you’d see with organic growth, but that doesn’t represent a sustainable exponential trend (if Good Ventures had kept ramping up at the same rate then they would have run out of money by now).
The GWWC pledge data look like linear growth since 2014, rather than exponential growth or a plateau.
On the whole it looks like there has been growth over the past few years, though the growth rate is lower than it was in 2012-16 and the amount & shape of the growth differs between metrics.
It appears Operation Warp Speed had to be funded by raiding other sources because Congress couldn’t be bothered to fund it. As MR points out, this is a scandal because it was necessary, rather than because it was done. It’s scary, because it implies that under a different administration Operation Warp Speed could easily have not happened at all.
There are gaps in the reporting on Operation Warp Speed funding, because apparently a bunch of the money that Congress did allocate for vaccines hasn’t been spent yet. I don’t understand why the White House spent other money but not that money.
Voting is like donating thousands of dollars to charity
If you care about social impact, why is voting important?