levin's Quick takes

I think some of the AI safety policy community has over-indexed on the visual model of the "Overton Window" and under-indexed on alternatives like the "ratchet effect," "poisoning the well," "clown attacks," and other models where proposing radical changes can make you, your allies, and your ideas look unreasonable.

I'm not familiar with a lot of systematic empirical evidence on either side, but it seems to me like the more effective actors in the DC establishment overall are much more in the habit of looking for small wins that are both good in themselves and shrink the size of the ask for their ideal policy than of pushing for their ideal vision and then making concessions. Possibly an ideal ecosystem has both strategies, but it seems possible that at least some versions of "Overton Window-moving" strategies executed in practice have larger negative effects via associating their "side" with unreasonable-sounding ideas in the minds of very bandwidth-constrained policymakers, who strongly lean on signals of credibility and consensus when quickly evaluating policy options, than the positive effects of increasing the odds of ideal policy and improving the framing for non-ideal but pretty good policies.

In theory, the Overton Window model is just a description of what ideas are taken seriously, so it can indeed accommodate backfire effects where you argue for an idea "outside the window" and this actually makes the window narrower. But I think the visual imagery of "windows" actually struggles to accommodate this -- when was the last time you tried to open a window and accidentally closed it instead? -- and as a result, people who rely on this model are more likely to underrate these kinds of consequences.

Would be interested in empirical evidence on this question (ideally actual studies from psych, political science, sociology, econ, etc literatures, rather than specific case studies due to reference class tennis type issues).

I sometimes say, in a provocative/hyperbolic sense, that the concept of "neglectedness" has been a disaster for EA. I do think the concept is significantly over-used (ironically, it's not neglected!), and people should just look directly at the importance and tractability of a cause at current margins.

Maybe neglectedness useful as a heuristic for scanning thousands of potential cause areas. But ultimately, it's just a heuristic for tractability: how many resources are going towards something is evidence about whether additional resources are likely to be impactful at the margin, because more resources mean its more likely that the most cost-effective solutions have already been tried or implemented. But these resources are often deployed ineffectively, such that it's often easier to just directly assess the impact of resources at the margin than to do what the formal ITN framework suggests, which is to break this hard question into two hard ones: you have to assess something like the abstract overall solvability of a cause (namely, "percent of the problem solved for each percent increase in resources," as if this is likely to be a constant!) and the neglectedness of the cause.

That brings me to another problem: assessing neglectedness might sound easier than abstract tractability, but how do you weigh up the resources in question, especially if many of them are going to inefficient solutions? I think EAs have indeed found lots of surprisingly neglected (and important, and tractable) sub-areas within extremely crowded overall fields when they've gone looking. Open Phil has an entire program area for scientific research, on which the world spends >$2 trillion, and that program has supported Nobel Prize-winning work on computational design of proteins. US politics is a frequently cited example of a non-neglected cause area, and yet EAs have been able to start or fund work in polling and message-testing that has outcompeted incumbent orgs by looking for the highest-value work that wasn't already being done within that cause. And so on.

What I mean by "disaster for EA" (despite the wins/exceptions in the previous paragraph) is that I often encounter "but that's not neglected" as a reason not to do something, whether at a personal or organizational or movement-strategy level, and it seems again like a decent initial heuristic but easily overridden by taking a closer look. Sure, maybe other people are doing that thing, and fewer or zero people are doing your alternative. But can't you just look at the existing projects and ask whether you might be able to improve on their work, or whether there still seems to be low-hanging fruit that they're not taking, or whether you could be a force multiplier rather than just an input with diminishing returns? (Plus, the fact that a bunch of other people/orgs/etc are working on that thing is also some evidence, albeit noisy evidence, that the thing is tractable/important.) It seems like the neglectedness heuristic often leads to more confusion than clarity on decisions like these, and people should basically just use importance * tractability (call it "the IT framework") instead.

Biggest disagreement between the average worldview of people I met with at EAG and my own is something like "cluster thinking vs sequence thinking," where people at EAG are like "but even if we get this specific policy/technical win, doesn't it not matter unless you also have this other, harder thing?" and I'm more like, "Well, very possibly we won't get that other, harder thing, but still seems really useful to get that specific policy/technical win, here's a story where we totally fail on that first thing and the second thing turns out to matter a ton!"

A technique I've found useful in making complex decisions where you gather lots of evidence over time -- for example, deciding what to do after your graduation, or whether to change jobs, etc., where you talk to lots of different people and weigh lots of considerations -- is to make a spreadsheet of all the arguments you hear, each with a score for how much it supports each decision.

For example, this summer, I was considering the options of "take the Open Phil job," "go to law school," and "finish the master's." I put each of these options in columns. Then, I'd hear an argument like "being in school delays your ability to take a full-time job, which is where most of your impact will happen"; I'd add a row for this argument. I thought this was a very strong consideration, so I gave the Open Phil job 10 points, law school 0, and the master's 3 (since it was one more year of school instead of 3 years). Later, I'd hear an argument like "legal knowledge is actually pretty useful for policy work," which I thought was a medium-strength consideration, and I'd give these options 0, 5, and 0.

I wouldn't take the sum of these as a final answer, but it was useful for a few reasons:

• In complicated decisions, it's hard to hold all of the arguments in your head at a time. This might be part of why I noticed a strong recency bias, where the most recent handful of considerations raised to me seemed the most important. By putting them all in one place, I could feel like I was properly accounting for all the things I was aware of.
• Relatedly, it helped me avoid double-counting arguments. When I'd talk to a new person, and they'd give me an opinion, I could just check whether their argument was basically already in the spreadsheet; sometimes I'd bump a number from 4 to 5, or something, based on them being persuasive, but sometimes I'd just say, "Oh, right, I guess I already knew this and shouldn't really update from it."
• I also notice a temptation to simplify the decision down to a single crux or knockdown argument, but usually cluster thinking is a better way to make these decisions, and the spreadsheet helps aggregate things such that an overall balance of evidence can carry the day.