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).
The user inputs a target taxon. The tool then determines, where necessary, two related but distinct scopes: one for the sentience gate and one for the affective-capacity analysis.
You may be interested in the article "Ethics Without Sentience: Facing Up to the Probable Insignificance of Phenomenal Consciousness" by François Kammerer. Here is a summary. Relatedly, you may like this and this very short posts by Keith Frankish. I agree with Keith that phenomenal consciousness does not exist. In any case, even if it exists, I agree with François it is not significant. Phenomenal consciousness as traditionally defined is not falsifiable (or, to be precise, it is traditionally defined such that one can only know about their own consciousness at the moment). In practice, one always has to come up with empirical proxies for it, but I feel these are largely arbitrary if it is not falsifiable. So I believe it would be better to focus just on what animals can do (as in your affective-capacity analysis), and not on assessing phenomenal sentience.
When I click on a given section of a post on the left pane, the URL shown at the top is still that of the post. It used to be the URL of the section. However, I can still link to a section by opening it in a new tab.
I wonder if the power required by brains is proportional to the number of neurons across very different species. It seems to be so among mammals. From Herculano-Houzel (2011):
Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans).
We tested demand in multiple ways. Apart from running user interviews, we ran a survey on the board asking what people were looking for (now with 800+ responses), and added full-time roles to the role-type filter (before we had any full-time listings), to see whether people would look for them organically. Full-time was the most mentioned role type in the survey and the most used filter. It also turned out 48% of the visitors to the board weren't using other popular boards to find EA roles (such as 80,000 Hours or Probably Good).
Have you considered letting visitors to the EA Opportunities Board know about the boards from 80,000 Hours and Probably Good? To which extent did people who knew about these boards still wanted full time roles in the EA Opportunities Board? If to a significant extent, why?
"Welfare per year" = "population (animal-years per year)"*"welfare per animal-year" = "deaths per year"*"welfare per death". It is unclear to me whether "welfare per animal-year" varies more or less than "welfare per death". So it is also unclear to me whether population is a better or worse proxy for welfare than deaths per year as long as both proxies cover the same life stages.
Using more appropriate units (such as total annual deaths or days of experience) reveals that highly r-selected animals might dominate moral calculus to a greater degree than a naive estimate might suggest.
On the other hand, more r-selected animals will tend to be more abundant, and have a lower moral weight? I agree with the above because I think differences in moral weight may be very small, but I also believe they may be very large.
Standing population size is usually not a reliable proxy for comparing the likely scale of harm between different highly numerous species, due to significant differences in population throughput and life-history.
"adult population size"?
For example, for identical stable populations of ants and aphids, we might expect there to be over 200x as many aphid deaths, and over 7x as many aphid days of experience.
One could argue that 1 and 2 remain incomparable and that I have no reason to favor 2 over 1.
If the absolute value of the expected cost-effectiveness of 1 was astronomically larger than that of intervention 2, I think comparing the interventions would be similar to comparing intervention 1 with one with cost-effectiveness of 0 (burning money). It is very unclear whether the expected cost-effectiveness of 1 is positive or negative. So it would be close to arbitrary which intervention has the highest expected cost-effectiveness.
Another thing, assuming there is no 2-like intervention, is that the criterion to pick could be something other than "act straightforwardly as if you were endorsing SHARP". It could instead be some (other) form of bracketing.
I think cost-effectiveness accounting for effects on all organisms spans many orders of magnitude (OOMs) due to large uncertainty about how to compare welfare across species. So I expect something like a loguniform or lognormal distributions would be more appropriate. Ideally, one would model the inputs as distributions instead of assuming a distribution for the cost-effectiveness.
In the context of assessing interventions with very uncertain cost-effectiveness (in my view, practically any context), in which sense would it matter a lot whether one uses sharp or unsharp probabilities? With sharp probabilities, it would be close to arbitrary which interventions should be supported. With unsharp probabilities, it would be indeterminate which interventions should be supported, but one would still end up supporting something based on some criteria. From my perspective, it is unclear which one would lead to greater impact. Given the large uncertainty, it is not even clear to me whether any of the approaches would outperform picking interventions randomly.
So I believe the priority would be decreasing uncertainty. I expect this can be most cost-effectively achieved via research (on comparing welfare across species). However, supporting the interventions under comparison also indirectly decreases uncertainty to some extent. Funders who do not want to fund research directly decreasing the uncertainty might be open to funding research aiming to figure out how to decrease uncertainty via supporting existing interventions. They could then update to some extent towards funding interventions which look better in terms of decreasing uncertainty. I guess ones contributing to moral circle expansion help attracts resources to target more neglected animals, including to study how their welfare compares with that of other less neglected animals.
Right. I think using unsharp probabilities, and expected values is fine to highlight it is unclear which of the interventions being compared has the highest expected cost-effectiveness. However, I do not see what is the advantage of this relative to just getting wide distributions for the cost-effectiveness, and showing these overlap a lot, which would be a sign that decreasing their uncertaity may have a higher expected cost-effectiveness than picking the intervention with the highest expected cost-effectiveness. One can analyse value of information (VOI) using perfectly sharp credences.
Hi Jim. You meant "the author's non-endorsement of Uniqueness"? You said "the other's".
Adam (the author) says "It is compatible with sharp that for certain batches of evidence, there is more than one probability function it is rationally permissible to have on the basis of that evidence". However, Adam concedes in footnote 11 it may be difficult to accept sharpness, and deny uniqueness.
There may well be difficulties with accepting sharp while denying Uniqueness. But I will not press any such difficulties here. Thanks to Susanna Rinard and John Collins for pressing me on this point.
I endorse sharpness and uniqueness. As far as I can tell, the issues of unsharp probabilities would apply in the same way to non-unique probabilities. Why would this not be the case?
At the same time, I believe there are many reasonable probabilities. Humans have a limited memory, and therefore cannot represent infinitely precise / sharp probabilities. One would need infinite resources to represent an infinitely precise probability. If I say a given event has a chance of 10 %, I mean the sharp unique probability of a rational being with the evidence I have access to is close to 10 % (how close would depend on the context). I do not mean it is exactly 10 %. So I would convey practically the same information (just in an unnecessarily precise way) if I said that same event has a chance of 10.001 %. Does this make sense?
I would prefer draft comments I deleted not to show up in my private homepage.
Hi Cynthia and Wladimir. Nice post.
You may be interested in the article "Ethics Without Sentience: Facing Up to the Probable Insignificance of Phenomenal Consciousness" by François Kammerer. Here is a summary. Relatedly, you may like this and this very short posts by Keith Frankish. I agree with Keith that phenomenal consciousness does not exist. In any case, even if it exists, I agree with François it is not significant. Phenomenal consciousness as traditionally defined is not falsifiable (or, to be precise, it is traditionally defined such that one can only know about their own consciousness at the moment). In practice, one always has to come up with empirical proxies for it, but I feel these are largely arbitrary if it is not falsifiable. So I believe it would be better to focus just on what animals can do (as in your affective-capacity analysis), and not on assessing phenomenal sentience.
When I click on a given section of a post on the left pane, the URL shown at the top is still that of the post. It used to be the URL of the section. However, I can still link to a section by opening it in a new tab.
I wonder if the power required by brains is proportional to the number of neurons across very different species. It seems to be so among mammals. From Herculano-Houzel (2011):
Hi Agnes. Thanks for the update.
Have you considered letting visitors to the EA Opportunities Board know about the boards from 80,000 Hours and Probably Good? To which extent did people who knew about these boards still wanted full time roles in the EA Opportunities Board? If to a significant extent, why?
Hi Abraham and Mal. Nice post.
"Welfare per year" = "population (animal-years per year)"*"welfare per animal-year" = "deaths per year"*"welfare per death". It is unclear to me whether "welfare per animal-year" varies more or less than "welfare per death". So it is also unclear to me whether population is a better or worse proxy for welfare than deaths per year as long as both proxies cover the same life stages.
On the other hand, more r-selected animals will tend to be more abundant, and have a lower moral weight? I agree with the above because I think differences in moral weight may be very small, but I also believe they may be very large.
"adult population size"?
"stable adult populations"?
If the absolute value of the expected cost-effectiveness of 1 was astronomically larger than that of intervention 2, I think comparing the interventions would be similar to comparing intervention 1 with one with cost-effectiveness of 0 (burning money). It is very unclear whether the expected cost-effectiveness of 1 is positive or negative. So it would be close to arbitrary which intervention has the highest expected cost-effectiveness.
Bracketing departs from impartiality, and I find this very unappealing.
I think cost-effectiveness accounting for effects on all organisms spans many orders of magnitude (OOMs) due to large uncertainty about how to compare welfare across species. So I expect something like a loguniform or lognormal distributions would be more appropriate. Ideally, one would model the inputs as distributions instead of assuming a distribution for the cost-effectiveness.
In the context of assessing interventions with very uncertain cost-effectiveness (in my view, practically any context), in which sense would it matter a lot whether one uses sharp or unsharp probabilities? With sharp probabilities, it would be close to arbitrary which interventions should be supported. With unsharp probabilities, it would be indeterminate which interventions should be supported, but one would still end up supporting something based on some criteria. From my perspective, it is unclear which one would lead to greater impact. Given the large uncertainty, it is not even clear to me whether any of the approaches would outperform picking interventions randomly.
So I believe the priority would be decreasing uncertainty. I expect this can be most cost-effectively achieved via research (on comparing welfare across species). However, supporting the interventions under comparison also indirectly decreases uncertainty to some extent. Funders who do not want to fund research directly decreasing the uncertainty might be open to funding research aiming to figure out how to decrease uncertainty via supporting existing interventions. They could then update to some extent towards funding interventions which look better in terms of decreasing uncertainty. I guess ones contributing to moral circle expansion help attracts resources to target more neglected animals, including to study how their welfare compares with that of other less neglected animals.
Right. I think using unsharp probabilities, and expected values is fine to highlight it is unclear which of the interventions being compared has the highest expected cost-effectiveness. However, I do not see what is the advantage of this relative to just getting wide distributions for the cost-effectiveness, and showing these overlap a lot, which would be a sign that decreasing their uncertaity may have a higher expected cost-effectiveness than picking the intervention with the highest expected cost-effectiveness. One can analyse value of information (VOI) using perfectly sharp credences.
Hi Jim. You meant "the author's non-endorsement of Uniqueness"? You said "the other's".
Adam (the author) says "It is compatible with sharp that for certain batches of evidence, there is more than one probability function it is rationally permissible to have on the basis of that evidence". However, Adam concedes in footnote 11 it may be difficult to accept sharpness, and deny uniqueness.
I endorse sharpness and uniqueness. As far as I can tell, the issues of unsharp probabilities would apply in the same way to non-unique probabilities. Why would this not be the case?
At the same time, I believe there are many reasonable probabilities. Humans have a limited memory, and therefore cannot represent infinitely precise / sharp probabilities. One would need infinite resources to represent an infinitely precise probability. If I say a given event has a chance of 10 %, I mean the sharp unique probability of a rational being with the evidence I have access to is close to 10 % (how close would depend on the context). I do not mean it is exactly 10 %. So I would convey practically the same information (just in an unnecessarily precise way) if I said that same event has a chance of 10.001 %. Does this make sense?