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. 

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. 

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.

All super interesting suggestions, Michael!

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.

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!

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!

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. 

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. 

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.