I am a generalist quantitative researcher. I am open to volunteering and paid work. I welcome suggestions for posts. You can give me feedback here (anonymously or not).
Vasco Grilođ¸
Karma: 11,794
Hi Rafael. You are welcome. Thanks for the great post.
Beyond the ExÂpandÂing CirÂcle: Towards a PluÂralÂist AcÂcount of MoÂral Progress
Hello Jen. Nice post. Where could one look for opportunities to live with impact- or rationality-focussed people in San Francisco? I know there is the Facebook group Bay Area EA/âRationality Housing Board.
Hi Jim. Great post. I share your concerns.
Granularization was impossible at first, since there was no sentience, so expansion was of course necessary.
Unless any operationalisation of sentience is arbitrary?
Hi Kieran. Welcome to the EA Forum. That matches my read.
Hi Laura. That finding also jumped out to me. I suspect orgs are less willing to improve MEL than suggested by that quote. â77% [of orgs] say funders rarely or never fund MEL (excl. n/âa results)â, but I assume this refer to funding restricted to MEL, whereas what I think matters to assess orgsâ preferences is whether there are funds which can be used to improve MEL. I would be surprised if funders restricted funding to non-MEL activities. So I believe orgs could spend more on MEL if they wanted to.
â75% of organizations say improving MEL is important or urgentâ, but I find it difficult to interpret this without knowing what orgs would say about other activities. Maybe the vast majority of orgs considers more important or urgent improving other activities, which could explain why âonly 18% would definitely pay for itâ.
Also, in regards to Bobâs book, it seems like it would make sense, rather than adding the modelâs estimates together to instead act on each model separately, i.e. assigning 5% of resources to help animals according to the equality model, 25% according to the neurophysiological model, and 70% according to the proxies model. Then, within the 70% for the proxies model, we could assign 33% of resources to applying a high exponent to the welfare ranges, 33% to applying no exponent to the welfare ranges, and 33% to applying a low exponent to the welfare ranges.
Something like this is proposed in the article âMoral Uncertainty, Proportionality and Bargainingâ.
Only physical consciousness is falsifiable? I think so. So I believe it is better to focus on physicalist theories of consciousness. Relatedly, you may be interested in this discussion between me and Wladimir Alonso from the Welfare Footprint Institute (WFI) about whether sentience is binary.
Hi Gregory. Great post.
I mentioned before that IRT is basically an Elo for the game of responding correctly to benchmark questions. For Elo, hereâs the win probability of player A vs. player B, given respective strengths Sa and Sb:
P(A wins) = 1 /â [1 + 10^((SbâSa)/â400)]
Nitpick. I think the above is the probability of winning plus half of the probability of drawing.
I broadly agree with the points you made in that comment.
You may be interested in my estimates of the total welfare of various organism populations assuming welfare per fully-happy-animal-year is a power law of the individual number of neurons, or of the basal metabolic rate (BMR) at 25 ÂşC. Here is some context about why a power lay may be appropriate.
Hi Aidan and Aaron. Great news. The program sounds very promising.
Meanwhile, the animal movement needs more new interventions and organizations in the pipeline so that efforts to fix factory farming can scale in the coming years.
I like that you say âfixâ instead of âendâ factory farming. Fixing it is more aligned with increasing animal welfare, as there is large uncertainty about which animals have positive or negative lives (worth living or not from their own perspective).
Thanks, Anthony.
I think it would help a lot if you spelled out the premises more, because theyâre quite opaque to me as written. E.g. I donât know what âthe norm reveals a choice functionâ means. (I think this kind of use of jargon without giving context is a common failure mode of current LLM summaries.)
I asked Claude to update the post to address your comment. There is now a section with the setup of the theorems, and clearer premises. Are they sufficiently understandable now?
Also, if I understand correctly, âbehaviour maximizes a family of preference orderings...â means that the result only shows that we can represent an agentâs behavior as satisfying completeness.
I only briefly skimmed the article, but I agree with your interpretation. Claude agrees too.
The completeness here is a property of a ranking reconstructed from choices, and it comes almost entirely from premise 0: because the rule always names something acceptable, every pair gets settled. [This is now clarified in the section of the linkpost with the setup.] But the rule may name both options as acceptable, which the theorem records as the two being equally good â and that is also exactly how an agent who found them incomparable, and picked arbitrarily, would behave. The result therefore cannot distinguish âequally goodâ from ânot comparableâ, and so does not show that an agent deliberating about what to do must arrive at a complete ranking. It shows their behaviour is representable as if they had one.
Hi James.
This question gets to me a lot. It seems totally unclear how much the valence of an experience correlates to behavior. Like, it seems plausible that extraordinarily little pain could motivate behavior in simple organisms, but itâs also hard to imagine, since, as a complex organism, I need pretty intense pain to be motivated to significantly change my behavior. And, it doesnât seem like thereâs any way we could reduce uncertainty about this.
Makes sense. Relatedly, you may be interested in the post âDo primitive sentient organisms feel extreme pain? disentangling intensity range and resolutionâ by Wladimir Alonso and Cynthia Schuck.
Also, in regards to Bobâs book, it seems like it would make sense, rather than adding the modelâs estimates together to instead act on each model separately, i.e. assigning 5% of resources to help animals according to the equality model, 25% according to the neurophysiological model, and 70% according to the proxies model. Then, within the 70% for the proxies model, we could assign 33% of resources to applying a high exponent to the welfare ranges, 33% to applying no exponent to the welfare ranges, and 33% to applying a low exponent to the welfare ranges.
I think the above goes against maximising expected value in principle, even if it may be a good approach in practive, and I believe maximising expected value follows from very intuitive premises.
The problem of what percentages you should use and what models to use would, of course, still remain though.
Right.
I think consistently applying your arguments suggest you should assign 0 probability in these cases, and so your models blow up in complexity and you become too credulous, contrary to Occamâs razor.
There is also Hitchensâs razor.
What can be asserted without evidence can also be dismissed without evidence.
Readers of this post may be interested in this summary of the setup, premises, and conclusions of the theorems presented in Consequentialist Foundations for Expected Utility by Peter J. Hammond.
I just linkposted a summary of the setup, premises, and conclusions of the theorems presented in Consequentialist Foundations for Expected Utility by Peter J. Hammond.
ConÂseÂquenÂtialÂist FounÂdaÂtions for ExÂpected Utility
David Spiegelhalter and Mike Pearson calculated driving a car in the UK for 250 mi increases oneâs risk of death by 1 micromort, which is 2.48*10^-7 per 100 km.
Motor vehicle road injuries (excluding motorcyclistsâ) caused 22.1 M years of life lost, and 436 k deaths in 2019, which imply 50.7 years of life lost per death from motor vehicle road injuries in 2019. This times the increase in the risk of death due to driving a car in the UK equals 6.62 person-min less of life expectancy per 100 km.
There were 2.7 road fatalities per 1 billion miles driven by cars in Great Britain in 2024 (see Table 4), 1.68*10^-7 road fatalities per 100 km (= 2.7/â(1*10^9*1.61)*100), 67.7 % (= 1.68*10^-7/â(2.48*10^-7)) as much as I estimated above. As a result, for the above 50.7 years of life lost per death, there is a reduction in life expectancy per 10 km of driving a car of 4.48 min (= 0.677*6.62).
Thanks. Relatedly, you may be interested in the comments from me and Wladimir on this post from Benthamâs Bulldog arguing for the possibility of intense agony in many species.
In the sense that talking about the 1st ever sentient being is to some extent like talking about the 1st ever planet. There is a fact of the matter about which was the 1st planet, and when it formed for a given definition of planet, but there is nothing very special about any particular definition. I think the same applies to falsifiable definitions of sentience.