Vasco Grilo🔸

• Vasco Grilo🔸 13 Mar 2026 17:47 UTC

  2 points

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  in reply to: Vasco Grilo🔸’s comment on: Cost-effec­tive­ness of An­ima In­ter­na­tional Poland

  I would estimate the number of layer-years improved in expectation in year Y from “expected population of layers in year Y”*(“expected population of layers in cages in year Y without the intervention as a fraction of all of them in year Y”—“expected population of layers in cages in year Y with the intervention as a fraction of all of them in year Y”) = P(Y)*(f_control(Y) - f_intervention(Y)), which is correct by definition.

  Here is a post illustrating this.

  Summary

  • Cost-effectiveness analyses (CEAs) of interventions accelerating animal welfare reforms usually estimate the increase in the welfare of the target animals (for example, hens in cages) based on the acceleration in years of the full implementation of the reform. This makes sense if each level of implementation of the reform is accelerated as much as its full implementation.

  • However, there are many cases where the acceleration of the full implementation of the reform is not enough to determine the number of animals helped, or animal-years improved. I discuss some below.

Es­ti­ma­tion of the benefits of ac­cel­er­at­ing welfare reforms

• Vasco Grilo🔸 12 Mar 2026 19:58 UTC

  2 points

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  on: Hard-to-re­verse de­ci­sions de­stroy op­tion value

  Thanks for the great post, Stefan.

  Risk aversion. Risk aversion is normally a reason not to make a hard-to-reverse decision. Since reversibility gives you the option to switch course if your strategy underperforms, it normally reduces the risk of a truly bad outcome. Note, though, that the standard view within the effective altruism movement seems to be that altruists should not be risk-averse.

  It could similarly be argued that reversibility gives one the option to switch course despite their strategy performing well, this increasing the risk of missing a truly great outcome? The takeaway is that one should make certainly bad options less available, and certainly good options more unavoidable?

• Vasco Grilo🔸 12 Mar 2026 18:02 UTC

  2 points

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  on: I un­der­es­ti­mated AI ca­pa­bil­ities (again)

  Hi Ajeya.

  But for the first time, I don’t see any solid trend we can extrapolate to say it won’t happen soon.^[11] AI R&D really could be automated this year.

  What are your predictions for the unemployment rate of software engineers? What do you think about these reasons for potentially overestimating the pace of automation based on AI benchmarks?

  But there’s a big problem here – if AIs are actually able to perform most tasks on 1-hour task horizons, why don’t we see more real-world task automation? For example, most emails take less than an hour to write, but crafting emails remains an important part of the lives of billions of people every day.

  Some of this could be due to people underusing AI systems,^[2] but in this post I want to focus on reasons that are more fundamental to the capabilities of AI systems. In particular, I think there are three such reasons that are the most important:

  1. Time-horizon estimates are very domain-specific

  2. Task reliability strongly influences task horizons

  3. Tasks are very bundled together and hard to separate out.

• Vasco Grilo🔸 12 Mar 2026 15:20 UTC

  5 points

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  in reply to: Max Klijnstra 🔶’s comment on: GWWC’s 2026 strat­egy: strength­en­ing foun­da­tions & ac­cel­er­at­ing growth

  Welcome to the EA Forum, Max. Thanks for the clarification, and additional context. I am rooting for your (GWWC’s) success.

• Vasco Grilo🔸 12 Mar 2026 11:48 UTC

  11 points

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  on: Which An­i­mal Char­i­ties Should ACE Be Eval­u­at­ing?

  Thanks for asking, Vince. Here are some suggestions listed alphabetically which are not in your sheet, and have not yet been mentioned in other answers to your post:

  • Arthropoda Foundation.

  • Innovate Animal Ag.

  • Rethink Priorities’ (RP’s) animal welfare department.

  • Welfare Footprint Institute (WFI).

• Vasco Grilo🔸 9 Mar 2026 9:24 UTC

  2 points

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  on: Gra­da­tions of moral weight

  Thanks for the post, Michael.

  However, any specific function or set of coefficients would (to me) require justification, and it’s unclear that there can be any good justification.

  I also worry about the arbitrariness of the weights (coefficients) of the models. In Bob Fischer’s book about comparing welfare across species, there seems to be only 1 line about the weights used to aggregate the tentative estimates for the welfare range, the difference between the maximum and minimum hedonistic welfare per unit time. “We assigned 30 percent credence to the neurophysiological model, 10 percent to the equality model, and 60 percent to the simple additive model”. People usually give weights that are at least 0.1/​”number of models”, which is at least 3.33 % (= 0.1/​3) for 3 models, when it is quite hard to estimate the weights. However, giving weights which are not much smaller than the uniform weight of 1/​”number of models” could easily lead to huge mistakes. As a silly example, if I asked random people with age 7 about whether the gravitational force between 2 objects is proportional to “distance”^-2 (correct answer), “distance”^-20, or “distance”^-200, I imagine I would get a significant fraction picking the exponents of −20 and −200. Assuming 60 % picked −2, 20 % picked −20, and 20 % picked −200, one may naively conclude the mean exponent of −45.2 (= 0.6*(-2) + 0.2*(-20) + 0.2*(-200)) is reasonable. Yet, there is lots of empirical evidence against this which the respondants are not aware of. The right conclusion would be that the respondants have no idea about the right exponent because they would not be able to adequately justify their picks. I think we are in a similar situation with respect to comparing hedonistic welfare across species.

• Vasco Grilo🔸 9 Mar 2026 8:54 UTC

  2 points

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  on: In­creas­ingly vague in­ter­per­sonal welfare comparisons

  Thanks for the post, Michael.

  The more or larger such changes are necessary to get from one brain to another, the less tight the bounds on the comparisons could become, the further they may go both negative and positive overall,^[2] and the less reasonable it seems to make such comparisons at all.

  I agree comparisons become increasingly uncertain as the difference between the states of the organisms increases. However, I do not think there is a point where comparisons go from possible, but extremely difficult to not possible at all. I would say there is just a progressive widening of the distribution representing the hedonistic welfare per unit time of a given state of an organism as it moves away from typical human states. As an example, I could say my hedonistic welfare right now is 0.5 to 1.5 times that of random human who is awake, whereas that of a random nematode might be 10^-17 to 1 times that of a random human who is awake. I estimate the ratio between the individual number of neurons of nematodes and humans is 2.79*10^-9, whose square is 7.78*10^-18, roughly 10^-17.

• Vasco Grilo🔸 8 Mar 2026 13:52 UTC

  2 points

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Dig­ging In Your Heels

• Vasco Grilo🔸 8 Mar 2026 9:42 UTC

  2 points

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  in reply to: Anthony DiGiovanni 🔸’s comment on: 1. The challenge of un­aware­ness for im­par­tial al­tru­ist ac­tion guidance: Introduction

  • What makes two actions incomparable, under the imprecise EV model, is that the interval of EV differences crosses zero.

  Imagine 2 states of the world which are exactly the same, and have an imprecice expected welfare of −1 to 1. The difference between their imprecise expected welfare is −2 (= −1 − 1) to 2 (1 - (-1)), which crosses 0. So their expected welfare would be incomparable under your framework? I would say their expected welfare would be comparable, and exactly the same.

• Vasco Grilo🔸 8 Mar 2026 9:17 UTC

  2 points

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  in reply to: Mo Putera’s comment on: Are We Un­der­es­ti­mat­ing Lo­cal Giv­ing in LMICs?

  I did not have a particular structure in mind for how the people in LMICs would regrant the funds. Thanks for giving examples, Mo.

• Vasco Grilo🔸 7 Mar 2026 14:25 UTC

  2 points

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  in reply to: Evan LaForge’s comment on: Your Tools Aren’t Perfect: Method­olog­i­cal Bias Ex­plored Through Weighted Fac­tor Models

  My claim is that people should take time to understand their tools and account for their weaknesses. Accounting for weaknesses should happen not just within the tool, but outside of it when making the final decision.

  I think GiveWell is a good example of this. If CEAs made up 100% of their decision making process, their decisions would be heavily influenced by the weaknesses of CEAs as a method. However, GiveWell acknowledges these weaknesses and uses CEAs as a primary deciding factor, while also incorporating other factors as well.

  Agreed.

• Vasco Grilo🔸 7 Mar 2026 14:03 UTC

  2 points

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  on: Digi­tal Con­scious­ness Model Re­sults and Key Takeaways

  The Digital Consciousness Model (DCM) is a first attempt to assess the evidence for consciousness in AI systems in a systematic, probabilistic way.

  Is Digital Consciousness Model a bit of a misnomer considering it can be used to assess the consciousness of biological organisms? Maybe it should simply be called Consciousness Model (CM)?

• Vasco Grilo🔸 7 Mar 2026 12:12 UTC

  2 points

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  in reply to: Vasco Grilo🔸’s comment on: All pains are com­pa­rable?

  I believe any pains are quantitatively comparable if the pains of any 2 infinitesimally different states are quantitatively comparable.

  I think the weakest part of the strongest version of my argument is that it requires the pains of any 2 infinitesimally different states to be quantitatively comparable with certainty. If they are only quantitatively comparable with, for example, probability 99 %, pains which are 1 k infinitesimal steps apart would only be quantitatively comparable with probability 0.00432 % (= 0.99^(1*10^3)).

• Vasco Grilo🔸 7 Mar 2026 10:31 UTC

  2 points

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  in reply to: Evan LaForge’s comment on: Your Tools Aren’t Perfect: Method­olog­i­cal Bias Ex­plored Through Weighted Fac­tor Models

  The fundamental problem remains. Like I mentioned in my original post, any system for decision making is going to be trading away truth for practicality.

  Agreed. At the same time, I struggle to see practical cases where it makes sense to spend significant time on WFMs. I would rather improve cost-effectiveness analyses (CEA). For example, accounting better for priors, modelling more effects, and gathering more evidence to decrease uncertainty in key inputs. GiveWell uses CEAs all the time, but has never included a WFM in their public analyses as far as I know. Gemini did not find any examples either.

  You cite GiveWell as an example of an organization that takes EV estimates “close to literally”. I assume by this you mean the EV estimates they make with respect to cost-effectiveness.

  Yes. Elie Hassenfeld, GiveWell’s CEO, mentioned the following on the Clearer Thinking podcast.

  GiveWell cost- effectiveness estimates are not the only input into our decisions to fund malaria programs and deworming programs, there are some other factors, but they’re certainly 80% plus of the case.

  Isabel Arjmand from GiveWell elaborated on the above.

  The numerical cost-effectiveness estimate in the spreadsheet is nearly always the most important factor in our recommendations, but not the only factor. That is, we don’t solely rely on our spreadsheet-based analysis of cost-effectiveness when making grants.

  • We don’t have an institutional position on exactly how much of the decision comes down to the spreadsheet analysis (though Elie’s take of “80% plus” definitely seems reasonable!) and it varies by grant, but many of the factors we consider outside our models (e.g. qualitative factors about an organization) are in the service of making impact-oriented decisions. See this post for more discussion.

  • For a small number of grants, the case for the grant relies heavily on factors other than expected impact of that grant per se. For example, we sometimes make exit grants in order to be a responsible funder and treat partner organizations considerately even if we think funding could be used more cost-effectively elsewhere.

• Vasco Grilo🔸 6 Mar 2026 19:31 UTC

  2 points

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  in reply to: Evan LaForge’s comment on: Your Tools Aren’t Perfect: Method­olog­i­cal Bias Ex­plored Through Weighted Fac­tor Models

  Hi Evan.

  EV reasoning is vulnerable to Pascal’s Mugging and the Optimizer’s Curse.

  One can account for priors (information besides the new evidence) to mitigate these issues, as suggested in Holden Karnofsky’s post about not taking expected value estimates literally (when they do not incorporate priors; I think GiveWell does take expected value estimates close to literally when they incorporate the vast majority of available evidence).

• Vasco Grilo🔸 6 Mar 2026 16:31 UTC

  2 points

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  in reply to: Mo Putera’s comment on: Giv­ing Green Launches New Bio­di­ver­sity Re­search and Top Bio­di­ver­sity Nonprofits

  Thanks for the detailed clarification, Mo.

• Vasco Grilo🔸 5 Mar 2026 22:24 UTC

  5 points

  1 ∶ 0

  on: Digi­tal Con­scious­ness Model Re­sults and Key Takeaways

  Hi. Have you considered analysing not only adult humans, but also human fetuses and infants? For example, fetuses with 3 and 6 months, newborns, and infants with 3, 6, 9, and 12 months. The evidence for consciousness in animals and AI systems can be dismissed on the basis that there is stronger for adult humans. However, I think the evidence for consciousness in sufficiently early stages of human development may be weaker than for some adult animals and AI systems.

• Vasco Grilo🔸 5 Mar 2026 9:45 UTC

  2 points

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  on: The Unit of Car­ing: On “fringe” ideas

  Hi Kelsey. Thank you for the great post. It made me think about research on soil animals and microorganisms as fringe ideas worth exploring.