I list a couple of possible sources.
Caspar Oesterheld makes this point in Complications in evaluating neglectedness:
I think many interventions initially face increasing returns from learning/research, creating economies of scale, specialization within the cause area, etc. For example, in most cause areas, the first $10,000 are probably invested into prioritization, organizing, or (potentially symbolic) interventions that later turn out to be suboptimal.
(I strongly recommend this neglected (!) article.)
Ben Todd makes a related point about charities (rather than causes) in Stop assuming ‘declining returns’ in small charities:
Economies of scale are a force for increasing returns, and they win out while still at a small scale, so the impact of the 5th staff member can easily be greater than the 4th.
Economies of scale are caused by:
1. Gains from specialisation. In a one person organisation, that person has to do everything – marketing, making the product, operations and so on. In a larger organisation, however, you can hire a specialist to do each function, which is more efficient.
2. Fixed costs. Often you have to pay the same amount of money for a service no matter what scale you have e.g. legally registering as an organisation costs about the same amount of time no matter how large you are; an aircraft with 100 passengers requires the same number of pilots as one with 200 passengers. As you become larger, fixed costs become a smaller and smaller fraction of the total.
3. Physical effects. Running an office that’s 2x as large doesn’t cost 2x as much to heat, because the volume increases by the cube of the length, while the surface area only increases by the square of the length. A rule of thumb is that capital costs only increase 50% in order to double capacity.
[posted in wrong thread]
See also bmg’s LW post, Realism and rationality. Relevant excerpt:
A third point of tension is the community’s engagement with normative decision theory research. Different normative decision theories pick out different necessary conditions for an action to be the one that a given person should take, with a focus on how one should respond to uncertainty (rather than on what ends one should pursue).
A typical version of CDT says that the action you should take at a particular point in time is the one that would cause the largest expected increase in value (under some particular framework for evaluating causation). A typical version of EDT says that the action you should take at a particular point in time is the one that would, once you take it, allow you to rationally expect the most value. There are also alternative versions of these theories—for instance, versions using risk-weighted expected value maximization or the criterion of stochastic dominance—that break from the use of pure expected value.
I’ve pretty frequently seen it argued within the community (e.g. in the papers “Cheating Death in Damascus” and “Functional Decision Theory”) that CDT and EDT are not “correct” and that some other new theory such as functional decision theory is. But if anti-realism is true, then no decision theory is correct.
Eliezier Yudkowsky’s influential early writing on decision theory seems to me to take an anti-realist stance. It suggests that we can only ask meaningful questions about the effects and correlates of decisions. For example, in the context of the Newcomb thought experiment, we can ask whether one-boxing is correlated with winning more money. But, it suggests, we cannot take a step further and ask what these effects and correlations imply about what it is “reasonable” for an agent to do (i.e. what they should do). This question—the one that normative decision theory research, as I understand it, is generally about—is seemingly dismissed as vacuous.
If this apparently anti-realist stance is widely held, then I don’t understand why the community engages so heavily with normative decision theory research or why it takes part in discussions about which decision theory is “correct.” It strikes me a bit like an atheist enthustiastically following theological debates about which god is the true god. But I’m mostly just confused here.
Two more newsletters:
It’s nice to know the origin of that phrase.
I haven’t read Lem’s novel, but I very much enjoyed Andrei Tarkovsky’s film adaptation. (I agree with Tyler Cowen that “all Takovsky movies are visually and conceptually brilliant”.)
Source for the screenshot: Samuel Karlin & Howard E. Taylor, A First Course in Stochastic Processes, 2nd ed., New York: Academic Press, 1975.
I’m also interested.
Anders Sandberg discusses the issue a bit in one of his conversations with Rob Wiblin for the 80k Podcast.
I once read a comment on the effective altruism subreddit that tried to explain why aging didn’t get much attention in EA despite being so important, and I thought it was quite enlightening.
For background, here’s the comment I wrote:
Longevity research occupies an unstable position in the space of possible EA cause areas: it is very “hardcore” and “weird” on some dimensions, but not at all on others. The EAs in principle most receptive to the case for longevity research tend also to be those most willing to question the “common-sense” views that only humans, and present humans, matter morally. But, as you note, one needs to exclude animals and take a person-affecting view to derive the “obvious corollary that curing aging is our number one priority”. As a consequence, such potential supporters of longevity research end up deprioritizing this cause area relative to less human-centric or more long-termist alternatives.
Crossposted from Hourglass Magazine
The entire “magazine” seems to have gone offline. SAD!
Thanks to your comment, I can now endorse what you said as a more accurate and nuanced version of the position my previous comment expressed. Agreed 100%.
Yeah, see my reply to Tobias.
I suspect that these results are very sensitive to model assumptions, such as tactical voting behaviour. But it would be interesting to see more work on VSE.
I agree with this. An approach I find promising is that of Nicolaus Tideman & Florenz Plassmann. In one study, the authors consider several different statistical models, use them to simulate actual elections, and rank the models by how best they approximate actual results. Then, in a subsequent study, the authors use the top-ranking model from their previous study to evaluate a dozen or so alternative voting rules, finding that plurality, anti-plurality, and Bucklin perform worst. As far as I’m aware, this is the only example of an attempt to assess voting rules by conducting simulations with a model that has been pre-fitted to actual election data. I believe that extending this approach may be among the most impactful research within this cause area.
Thanks for writing this—I think electoral reform is an interesting and important cause area.
[Approval voting] fails the later-no-harm criterion
All voting systems violate intuitively desirable conditions, so noting that some system violates some condition is in itself no reason to favor other systems. One needs to look at the full picture, see what conditions are violated by what systems, and pick the system that minimizes weight-adjusted violations. (There is a clear parallel here between voting theory and population ethics: impossibility theorems have demonstrated in both fields that there exists no voting rule or population axiology that satisfies all intuitively plausible desiderata, so violation of a condition can’t be adduced as a reason for rejecting the rule or axiology that violates it.)
But there is a much better approach, namely, to assess different systems by their “voter satisfaction efficiency” (VSE). Instead of relying on adequacy conditions, this approach considers the preferences that the electorate has for rival candidates and deals with them using the apparatus of expected utility theory. Each candidate is scored by the degree to which they satisfy the preferences of each voter, and then rival voting systems are scored by their probability of electing different candidates. Monte Carlo simulations independently performed by Warren Smith, Jameson Quinn and others generally find that approval voting has higher VSE than instant-runoff voting, and that both approval voting and instant-runoff voting have much higher VSE than plurality voting.
Given these results, I think the priority for EAs is to support whichever alternatives to plurality voting are most viable in a particular jurisdiction, rather than obsess over which of these alternatives to plurality is the absolute best. Of course, I also think it makes sense to continue to research the field, and especially refine the models used to compute VSE. What EAs definitely shouldn’t do, in my opinion, is to spend considerable resources discrediting those alternatives to one’s own preferred system, as FairVote has repeatedly done with respect to approval voting. Much more is gained by displacing plurality than is lost by replacing it with a suboptimal alternative (for all reasonable alternatives to plurality).
(In case it isn’t obvious, I’m definitely not saying that you have done this in your essay; I’m rather highlighting a serious failure mode I see in the “voting reform” community that I believe we should strive to avoid.)
a quick look would suggest ~75% moved from 50% to 10%
So, to confirm, are you saying that maybe 5 out of the 7 people who moved out of the 50% category moved in the 10% category? I think it’s important to get clarity on this, since until encountering this comment I was interpreting your post (perhaps unreasonably) as saying that those 7 people had left the EA community entirely. If in fact only a couple of people in that class left the community, out of a total of 16, that’s a much lower rate of drift than I was assuming, and more in line with anonymous’s analysis of value drift in the original CEA team.
Its interesting to note that I got downvoted for giving excellent sources. While you got upvoted for reading the articles and commenting. Basically I am outgroup/outcaste in EA.
I’m not sure I’m the right person to comment on this, given that I’m one of the parties involved, but I’ll provide my perspective here anyway in case it is of any help or interest.
I don’t think you are characterizing this exchange or the reasons behind the pattern of votes accurately. Bruno asked you to provide a source in support of the following claim, which you made four comments above:
One child policy had no effect on China’s population size. It was their widespread education pre-1979 than reduced fertility.
In response to that request, you provided two sources. I looked at them and found that both failed to support the assertion that “It was [China’s] widespread education pre-1979 than reduced fertility”, and that one directly contradicted it.
I didn’t downvote your comment, but I don’t think it’s unreasonable to expect some people to downvote it in light of this revelation. In fact, on reflection I’m inclined to favor a norm of downvoting comments that incorrectly claim that a scholarly source supports some proposition, since such a norm would incentivize epistemic hygiene and reduce the incidence of information cascades. I do agree with you that ingroup/outgroup dynamics sometimes explain observed behavior in the EA community, but I don’t think this is one of those cases. As one datapoint confirming this, consider that a month or two ago, when I pointed out that someone had mischaracterized the main theses of a paper, that person’s comment was heavily downvoted, despite this user being a regular commenter and not someone (I think) generally perceived to be an “outsider”.
Moving to the object-level, in your recent comment you appear to have modified your original contention. Whereas before your stated that “widespread education” was the factor explaining China’s reduced fertility, now you state that education was one factor among many. Although this difference may seem minor, in the present context it is crucial, because both in comments to this post and elsewhere in the Forum you have argued that EAs should prioritize education over growth. Yet if both of these factors account for the fertility reduction in China, your position cannot derive any support from this Chinese experience.
I actually took the time to look at those two sources, and as far as I can tell they provide no support whatsoever for your claim that “It was [China’s] widespread education pre-1979 that reduced fertility.” The word ‘education’ occurs exactly once in the first article, and in a sentence that doesn’t make any claims about education reducing fertility. As for the second article, to the extent that it attributes the fertility decline to anything, it attributes it not to “education”, but to economic development (pp. 158-159):
The third fatal problem with the “400 million births prevented” claim is that it totally ignores the most significant source of fertility decline worldwide: economic development… China’s rapid economic development since 1980 deserves the lion’s share of the credit for the [fertility decline].
I just thought it would be valuable to recalculate the estimated rates of attrition with this new data, though I think it’s totally fine for you to deprioritize this.