Hayley Clatterbuck

Karma: 975

Hayley Clatterbuck 8 Oct 2024 20:07 UTC
8 points
3 ∶ 0
in reply to: Vasco Grilo🔸’s comment on: What do RP’s tools tell us about giving $100m to AW or GHD?
Depending on the allocation method you use, you can still have high credence in expected total hedonistic utilitarianism and get allocations that give some funding to GHD projects. For example, in this parliament, I assigned 50% to total utilitarianism, 37% to total welfarist consequentialism, and 12% to common sense (these were picked semi-randomly for illustration). I set diminishing returns to 0 to make things even less likely to diversify. Some allocation methods (e.g. maximin) give everything to GHD, some diversify (e.g. bargaining, approval), and some (e.g. MEC) give everything to animals.
With respect to your second question, it wouldn’t follow that we should give money to causes that benefit the already well-off. Lots of worldviews that favor GHD will also favor projects to benefit the worst off (for various reasons). What’s your reason for thinking that they mustn’t? For what it’s worth, this comes out in our parliament tool as well. It’s really hard to get any parliament to favor projects that don’t target suffering (like Artists Without Borders).

Hayley Clatterbuck 8 Oct 2024 18:25 UTC
11 points
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in reply to: titotal’s comment on: What do RP’s tools tell us about giving $100m to AW or GHD?
Our estimate uses Saulius’s years/$ estimates. To convert to DALYs/$, we weighted by the amount of pain experienced by chickens per year. The details can be found in Laura Dufffy’s report here. The key bit:
I estimated the DALY equivalent of a year spent in each type of pain assessed by the Welfare Footprint Project by looking at the descriptions of and disability weights assigned to various conditions assessed by the Global Burden of Disease Study in 2019 and comparing these to the descriptions of each type of pain tracked by the Welfare Footprint Project.
These intensity-to-DALY conversion factors are:
- 1 year of annoying pain = 0.01 to 0.02 DALYs
- 1 year of hurtful pain = 0.1 to 0.25 DALYs
- 1 year of disabling pain = 2 to 10 DALYs
- 1 year of excruciating pain = 60 to 150 DALYs

Hayley Clatterbuck 7 Aug 2024 21:35 UTC
6 points
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in reply to: geoffrey’s comment on: AMA: Rethink Priorities’ Worldview Investigation Team
Here’s one method that we’ve found helpful when presenting our work. To get a feel for how the tools work, we set challenges for the group: find a set of assumptions that gives all resources to animal welfare; find how risk averse you’d have to be to favor GHD over x-risk; what moral views best favor longtermist causes? Then, have the group discuss whether and why these assumptions would support those conclusions. Our accompanying reports are often designed to address these very questions, so that might be a way to find the posts that really matter to you.

Hayley Clatterbuck 7 Aug 2024 20:39 UTC
10 points
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in reply to: Ian Howe’s comment on: AMA: Rethink Priorities’ Worldview Investigation Team
I think that I’ve become more accepting of cause areas that I was not initially inclined toward (particularly various longtermist ones) and also more suspicious of dogmatism of all kinds. In developing and using the tools, it became clear that there were compelling moral reasons in favor of almost any course of action, and slight shifts in my beliefs about risk aversion, moral weights, aggregation methods etc. could lead me to very different conclusions. This inclines me more toward very significant diversification across cause areas.

Hayley Clatterbuck 7 Aug 2024 20:38 UTC
11 points
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in reply to: Toby Tremlett🔹’s comment on: AMA: Rethink Priorities’ Worldview Investigation Team
A few things come to mind. First, I’ve been really struck by how robust animal welfare work is across lots of kinds of uncertainties. It has some of the virtues of both GHD (a high probability of actually making a difference) and x-risk work (huge scales). Second, when working with the Moral Parliament tool, it is really striking how much of a difference different aggregation methods make. If we use approval voting to navigate moral uncertainty, we get really different recommendations than if we give every worldview control over a share of the pie or if we maximize expected choiceworthiness. For me, figuring out which method we should use turns on what kind of community we want to be and which (or whether!) democratic ideals should govern our decision-making. This seems like an issue we can make headway on, even if there are empirical or moral uncertainties that prove less tractable.

Hayley Clatterbuck 15 Jul 2024 18:23 UTC
3 points
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in reply to: MichaelStJules’s comment on: Difference-Making Risk Aversion: An Exploration
All super interesting suggestions, Michael!

Hayley Clatterbuck 15 Jul 2024 18:22 UTC
3 points
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in reply to: MichaelStJules’s comment on: Difference-Making Risk Aversion: An Exploration
I agree that the plausibility of some DMRA decision theory will depend on how we actually formalize it (something I don’t do here but which Laura Duffy did some of here). Thanks for the suggestion.

Hayley Clatterbuck 15 Jul 2024 18:18 UTC
4 points
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in reply to: Richard Y Chappell🔸’s comment on: Difference-Making Risk Aversion: An Exploration
Hi Richard,
That is indeed a very difficult objection for the “being an actual cause is always valuable” view. We could amend that principle in various ways. One is agent-neutral: it is valuable that someone makes a difference (rather than the world just turning out well), but it’s not valuable that I make a difference. One adds conditions to actual causation; you get credit only if you raise the probability of the outcome? Do not lower the probability of the outcome (in which case it’s unclear whether you’d be an actual cause at all).
Things get tricky here with the metaphysics of causation and how they interact with agency-based ethical principles. There’s stuff here I’m aware I haven’t quite grasped!

Hayley Clatterbuck 15 Jul 2024 18:12 UTC
4 points
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in reply to: MichaelStJules’s comment on: An Epistemic Defense of Rounding Down
Thank you, Michael!
To your first point, that we have replaced arbitrariness over the threshold of probabilities with arbitrariness about how uncertain we must be before rounding down: I suppose I’m more inclined to accept that decisions about which metaprinciples to apply will be context-sensitive, vague, and unlikely to be capturable by any simple, idealized decision theory. A non-ideal agent deciding when to round down has to juggle lots of different factors: their epistemic limitations, asymmetries in evidence, costs of being right or wrong, past track records, etc. I doubt that there’s any decision theory that is both stateable and clear on this point. Even if there is a non-arbitrary threshold, I have trouble saying what that is. That is probably not a very satisfying response! I did enjoy Weatherson’s latest that touches on this point.
You suggest that the defenses of rounding down would also bolster decision-theoretic defenses of rounding down. It’s worth thinking what a defense of ambiguity aversion would look like. Indeed, it might turn out to be the same as the epistemic defense given here. I don’t have a favorite formal model of ambiguity aversion, so I’m all ears if you do!

Hayley Clatterbuck 15 Jul 2024 17:53 UTC
3 points
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in reply to: David Mathers🔸’s comment on: An Epistemic Defense of Rounding Down
Hi David,
Thanks for the comment. I agree that Wilkinson makes a lot of other (really persuasive) points against drawing some threshold of probability. As you point out, one reason is that the normative principle (Minimal Tradeoffs) seems to be independently justified, regardless of the probabilities involved. If you agree with that, then the arbitrariness point seems secondary. I’m suggesting that the uncertainty that accompanies very low probabilities might mean that applying Minimal Tradeoffs to very low probabilities is a bad idea, and there’s some non-arbitrary way to say when that will be. I should also note that one doesn’t need to reject Minimal Tradeoffs. You might think that if we did have precise knowledge of the low probabilities (say, in Pascal’s wager), then we should trade them off for greater payoffs.

Hayley Clatterbuck 31 Oct 2023 17:06 UTC
2 points
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in reply to: Angelina Li’s comment on: The Risks and Rewards of Prioritizing Animals of Uncertain Sentience
It’s possible that invertebrate sentience is harder to investigate given that their behaviors and nervous systems differ from ours more than those of cows and pigs do. Fortunately, there’s been a lot more work on sentience in invertebrates and other less-studied animals over the past few years, and I do think that this work has moved a lot of people toward taking invertebrate sentience seriously. If I’m right about that, then the lack of basic research might be responsible for quite a bit of our uncertainty.

Hayley Clatterbuck 31 Oct 2023 16:57 UTC
1 point
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in reply to: weeatquince’s comment on: The Risks and Rewards of Prioritizing Animals of Uncertain Sentience
Hi weeatquince,
This is a great question. As I see it, there are at least 3 approaches to ambiguity that are out there (which are not mutually exclusive).
a. Ambiguity aversion reduces to risk aversion about outcomes.
You might think uncertainty is bad because leaves open the possibility of bad outcomes. One approach is to consider the range of probabilities consistent with your uncertainty, and then assume the worst/ put more weight on the probabilities that would be worse for EV. For example, Pat thinks the probability of heads could be anywhere from 0 to 1. If it’s 0, then she’s guaranteed to lose $5 by taking the gamble. If it’s 1, then she’s guaranteed to win $10. If she’s risk averse, she should put more weight on the possibility that it has a Pr(heads) = 0. In the extreme, she should assume that it’s Pr(heads) = 0 and maximin.
b. Ambiguity aversion should lead you to adjust your probabilities
The Bayesian adjustment outlined above says that when your evidence leaves a lot of uncertainty, your posterior should revert to your prior. As you note, this is completely consistent with EV maximization. It’s about what you should believe given your evidence, not what you should do.
c. Ambiguity aversion means you should avoid bets with uncertain probabilities
You might think uncertainty is bad because it’s irrational to take bets when you don’t know the chances. It’s not that you’re afraid of the possible bad outcomes within the range of things you’re uncertain about. There’s something more intrinsically bad about these bets.

Hayley Clatterbuck 30 Oct 2023 20:00 UTC
10 points
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in reply to: EdoArad’s comment on: The Risks and Rewards of Prioritizing Animals of Uncertain Sentience
Hi Edo,
There are indeed some problems that arise from adding risk weighting as a function of probabilities. Check out Bottomley and Williamson (2023) for an alternative model that introduces risk as a function of value, as you suggest. We discuss the contrast between REV and WLU a bit more here. I went with REV here in part because it’s better established, and we’re still figuring out how to work out some of the kinks when applying WLU.

Hayley Clatterbuck 18 Oct 2023 16:51 UTC
3 points
1 ∶ 0
in reply to: MichaelStJules’s comment on: Causes and Uncertainty: Rethinking Value in Expectation
Thanks for your comment, Michael. Our team started working through your super helpful recent post last week! We discuss some of these issues (including the last point you mention) in a document where we summarize some of the philosophical background issues. However, we only mention bounded utility very briefly and don’t discuss infinite cases at all. We focus instead on rounding down low probabilities, for two reasons: first, we think that’s what people are probably actually doing in practice, and second, it avoids the seeming conflict between bounded utility and theories of value. I’m sure you have answers to that problem, so let us know!

Hayley Clatterbuck 17 Oct 2023 21:51 UTC
10 points
1 ∶ 0
in reply to: Jared_Riggs’s comment on: The Risks and Rewards of Prioritizing Animals of Uncertain Sentience
Thank you so much for this comment! How to formulate hierarchicalism—and whether there’s a formulation that’s plausible—is something our team has been kicking around, and this is very helpful. Indeed, your first suggestion is something we take seriously. For example, suffering in humans feeds into a lot of higher-order cognitive processes; it can lead to despair when reflected upon, pain when remembered, hopeless when projected into the future, etc. Of course, this isn’t to say that human suffering matters more in virtue of it being human but in virtue of other properties that correlate with being human.
I agree that we presented a fairly naive hierarchicalism here: take whatever is of value, and then say that it’s more important if and because it is possessed by a human. I’ll need to think more about whether your second suggestion can be dispatched in the same way as the naive view.

Hayley Clatterbuck 16 Oct 2023 15:57 UTC
15 points
3 ∶ 0
in reply to: NickLaing’s comment on: The Risks and Rewards of Prioritizing Animals of Uncertain Sentience
Thanks for your comments, Nick.
On the first point, we tried to provide general formulae that allow people to input their own risk weightings, welfare ranges, probabilities of sentience, etc. We did use RP’s estimates as a starting point for setting these parameters. At some points (like fn 23), we note important thresholds at which a model will render different verdicts about causes. If anyone has judgments about various parameters and choices of risk models, we’re happy to hear them!
On the second point, I totally agree that welfare range matters as well (so your point isn’t nitpicky). I spoke too quickly. We incorporate this in our estimations of how much value is produced by various interventions (we assume that shrimp interventions create less value/individual than human ones).
On the third point, a few things to say. First, while there are some approaches to ambiguity aversion in the literature, we haven’t committed to or formally explored any one of them here (for various reasons). If you like a view that penalizes ambiguity—with more ambiguous probabilities penalized more strongly—then the more uncertain you are about the target species’ sentience, the more you should avoid gambles involving them. Second, we suspect that we’re very certain about the probability of human sentience, pretty certain about chickens, pretty uncertain about shrimp, and really uncertain about AIs. For example, I will entertain a pretty narrow range of probabilities about chicken sentience (say, between .75 and 1) but a much wider range for shrimp (say, between .05 and .75). To the extent that more research would resolve these ambiguities, and there is more ambiguity regarding invertebrates and AI, then we should care a lot about researching them!