Matt_Lerner's Quick takes

Has there been any formal probabilistic risk assessment on AI X-risk? e.g. fault tree analysis or event tree analysis — anything of that sort?

Under what circumstances is it potentially cost-effective to move money within low-impact causes?

This is preliminary and most likely somehow wrong.  I'd love for someone to have a look at my math and tell me if (how?) I'm on the absolute wrong track here.

Start from the assumption that there is some amount of charitable funding that is resolutely non-cause-neutral. It is dedicated to some cause area Y and cannot be budged. I'll assume for these purposes that DALYs saved per dollar is distributed log-normally within Cause Y:

I want to know how impactful it might, in general terms, be to shift money from the median funding opportunity in Cause Y to the 90th percentile opportunity. So I want the difference between the value of spending a dollar at those two points on the impact distribution.

The log-normal distribution has the following quantile function:

So the value to be gained by moving from p = 0.5 to p = 0.9 is given by

This simplifies down to

Or

Not a pretty formula, but it's easy enough to see two things which were pretty intuitive before this exercise. First, you can squeeze out more DALYs from moving money in causes where the  mean DALYs per dollar across all funding opportunities is higher, and, for a given average, moving money is higher-value where there's more variation across funding opportunities (roughly, since variance is proportional to but not precisely given by sigma). Pretty obvious so far.

Okay, what about making this money-moving exercise cost-competitive with a direct investment in an effective cause, with a benchmark of $100/DALY? For that, and for a given investment amount $x, and a value c such that an expenditure of $c causes the money in cause Y to shift from the median opportunity to the 90th-percentile one, we'd need to satisfy the following condition:

Moving things around a bit...

Which, given reasonable assumptions about the values of c and x, holds true trivially for larger means and variances across cause Y.  The catch, of course, is that means and variances of DALYs per dollar in a cause area are practically never large, let alone in a low-impact cause area. Still, the implication is that (a) if you can exert inexpensive enough leverage over the funding flows within some cause Y and/or (b) if funding opportunities within cause Y are sufficiently variable, cost-effectiveness is at least theoretically possible.

So just taking an example: Our benchmark is $100 per DALY, or 0.01 DALYs per dollar, so let's just suppose we have a low-impact Cause Y that is between three and six orders of magnitude less effective than that, with a 95% CI of [0.00000001,0.00001], or one for which you can preserve a DALY for between $100,000 and $100 million, depending on the opportunity. That gives mu = -14.97 and sigma = 1.76. Plugging those numbers into the above, we get approximately...

...suggesting, I think, that if you can get roughly 4000:1 leverage when it comes to spending money to move money, it can be cost-effective to influence funding patterns within this low-impact cause area.

There are obviously a lot of caveats here (does a true 90th percentile opportunity exist for any Cause Y?), but this is where my thinking is at right now, which is why this is in my shortform and not anywhere else.

The usual caveats apply here: cross-country comparisons are often BS, correlation is not causation, I'm presenting smoothed densities instead of (jagged) histograms, etc, etc...

I've combined data on electoral system design and covid response to start thinking about the possible relationships between electoral system and crisis response. Here's some initial stuff: the gap, in days, between first confirmed cases and first school and workplace closures. Note that n= ~80 for these two datasets, pending some cleaning and hopefully a fuller merge between the different datasets.

To me, the potentially interesting thing here is the apparently lower variability of PR government responses. But I think there's a 75% chance that this is an illusion... there are many more PR governments than others in the dataset, and this may just be an instance of variability decreasing with sample size.

If there's an appetite here for more like this, I'll try and flesh out the analysis with some more instructive stuff, with the predictable criticisms either dismissed or validated.

It seems like there's a significant need right now to identify what the plausible relationship is between mask-wearing and covid19 symptoms. The virus is now widespread enough that a very quick Mechanical Turk survey could provide useful information.

Collect the following:

• Age group (5 categories)

• Wear a mask in public 1 month ago? (y/n)

• If yes to above, type of mask? (bandana/N95+/surgical/cloth/other)

• Sick with covid19 symptoms in past month? (y/n)

• Know anyone in everyday life who tested positive for covid19 in past month? (y/n)

• Postal code (for pop. density info)

Based on figures from this Gallup piece, a back-of-the-envelope says we could get usable results from surveying 20,000 Americans -- but we could work with a much smaller sample if we survey in a country where the virus is more prevalent.

The EA movement is disproportionately composed of highly logical, analytically minded individuals, often with explicitly quantitative backgrounds. The intuitive-seeming folk explanation for this phenomenon is that that EA, with its focus on rigor and quantification, appeals to people with a certain mindset, and that the relative lack of diversity of thinking styles in the movement is a function of personality type.

I want to reframe this in a way that I think makes a little more sense: the case for an EA perspective is really only made in an analytic, quantitative way. In this sense, having a quantitative mindset is actually a soft prerequisite for "getting" EA, and therefore for getting involved.

I don't mean to say that only quantitative people can understand the movement, or that there's something intellectually very special about EAs.

Rather- very few people would disagree that charity should be effective. Even non-utilitarians readily agree that in most contexts we should help as many people as we can. But the essential concepts for understanding the EA perspective are highly unfamiliar to most people.

• Expected value
• Cost-benefit analysis
• Probability
• An awareness of the abilities and limitations of social science

You don't need to be an expert in any of these areas to "get" EA. You just need to be vaguely comfortable with them in the way that people who have studied microeconomics or analytic philosophy or mathematics are, and most other people aren't.

This may be a distinction without a difference, but I want to raise the perspective that the composition of the EA movement is less about personality types and more about intellectual preparation.

In general, I'm skeptical about software solutionism, but I wonder if there's a need/appetite for group decision-making tools. While it's unclear exactly what works for helping groups make decisions, it does seem like a structured format could provide value to lots of organizations. Moreover, tools like this could provide valuable information about what works (and doesn't).