Key Takeaways

  • We offer welfare range estimates for 11 farmed species: pigs, chickens, carp, salmon, octopuses, shrimp, crayfish, crabs, bees, black soldier flies, and silkworms.
  • These estimates are, essentially, estimates of the differences in the possible intensities of these animals' pleasures and pains relative to humans' pleasures and pains. Then, we add a number of controversial (albeit plausible) philosophical assumptions (including hedonism, valence symmetry, and others discussed here) to reach conclusions about animals' welfare ranges relative to human's welfare range.
  • Given hedonism and conditional on sentience, we think (credence: 0.7) that none of the vertebrate nonhuman animals of interest have a welfare range that’s more than double the size of any of the others. While carp and salmon have lower scores than pigs and chickens, we suspect that’s largely due to a lack of research.
  • Given hedonism and conditional on sentience, we think (credence: 0.65) that the welfare ranges of humans and the vertebrate animals of interest are within an order of magnitude of one another. 
  • Given hedonism and conditional on sentience, we think (credence 0.6) that all the invertebrates of interest have welfare ranges within two orders of magnitude of the vertebrate nonhuman animals of interest. Invertebrates are so diverse and we know so little about them; hence, our caution.
  • Our view is that the estimates we’ve provided should be seen as placeholders—albeit, we submit, the best such placeholders available. We’re providing a starting point for more rigorous, empirically-driven research into animals’ welfare ranges. At the same time, we’re offering guidance for decisions that have to be made long before that research is finished

Introduction

This is the eighth post in the Moral Weight Project Sequence. The aim of the sequence is to provide an overview of the research that Rethink Priorities conducted between May 2021 and October 2022 on interspecific cause prioritization—i.e., making resource allocation decisions across species. The aim of this post is to share our welfare range estimates.

This post builds on all the others in the Moral Weight Project Sequence. In the first, we explained how we understand welfare ranges and how they might be used to make cross-species cost-effectiveness estimates. In the second, we introduced the Welfare Range Table, which reported the results of a literature review covering over 90 empirical traits across 11 farmed species. In the third, we suggested a way to quantify the impact of assuming hedonism on our welfare range estimates. In the fourth, we explained why we’re skeptical of using neuron counts as our sole proxy for animals’ moral weights. In the fifth and sixth, we explained why we aren’t convinced by some revisionary ways that people try to alter humans’ and animals’ moral weights by proposing that there are more subjects per organism than we might initially assume. In the seventh, we argued that “animal-friendly” results shouldn’t be that surprising given the Moral Weight Project’s assumptions—nor are they a good reason to think that the Project’s assumptions are mistaken.

In what follows, we’ll briefly recap our understanding of welfare ranges and our proposed way of using them. Then, we’ll summarize our methodology and respond to some questions and objections.

How can we compare benefits to the members of different species?

Many EA organizations use DALYs-averted as a unit of goodness. So, the Moral Weight Project tries to express animals’ welfare level changes in terms of DALYs-averted. This lets people conduct standard cost-effectiveness analyses across human and animal interventions. (What follows is a compressed overview of our strategy. For more detail, please see our Introduction to the Moral Weight Project.)

In the context of a cost-effectiveness analysis, a “moral weight discount” is a function that takes some amount of some species’ welfare as an input and has some number of DALYs as an output. So, the Moral Weight Project tries to provide “moral weight discounts” for 11 commercially-significant species. The interpretation of this function depends on the moral assumptions in play. The Moral Weight Project assumes hedonism (welfare is determined wholly by positively and negatively valenced experiences) and unitarianism (equal amounts of welfare count equally, regardless of whose welfare it is). Given hedonism and unitarianism, a species's moral weight is how much welfare its members can realize—i.e., its members’ capacity for welfare. That is, everyone’s welfare counts the same, but some may be able to realize more welfare than others.

Capacity for welfare = welfare range × lifespan. An individual’s welfare range is the difference between the best and worst welfare states the individual can realize. In other words, assume we can assign a positive number to the best welfare state the individual can realize and a negative number to the worst welfare state the individual can realize. The difference between them is the individual’s welfare range.

We’re ultimately trying to convert changes in welfare levels into DALYs. So, the relevant “best” human welfare state is the average welfare level of the average human in full health. The relevant “best” animal welfare states will be analogous.

For simplicity’s sake, we assume that humans’ welfare range is symmetrical around the neutral point. So, if the “best” welfare state for a human is represented by some arbitrary positive number, then the “worst” welfare state is represented by the negation of that number. (For reasons we sketch below, this assumption matters less than you might think. For some preliminary thoughts on the symmetry assumption, see this report.)

Welfare ranges allow us to convert species-relative welfare assessments, understood as percentage changes in the portions of animals’ welfare ranges, into a common unit. To illustrate, let’s make the following assumptions:

  1. Chickens’ welfare range is 10% of humans’ welfare range.
  2. Over the course of a year, the average chicken is about half as badly off as they could be in conventional cages (they’re at the ~50% mark in the negative portion of their welfare range).
  3. Over the course of a year, the average chicken is about a quarter as badly off as they could be in a cage-free system (they’re at the ~25% mark in the negative portion of their welfare range). 

Given these assumptions, we can calculate the welfare gain of a cage-free campaign in DALY-equivalents averted: 

  1. Assuming symmetry around the neutral point, the negative portion of chickens’ welfare range is 10% of humans’ positive welfare range. (For instance, if humans’ welfare range is 100 and chickens’ welfare range is 10, humans range from -50 to 50 and chickens range from -5 to 5. So, the negative portion of chickens’ welfare range is still 10% of humans’ welfare range.)
  2. Given our assumptions about the welfare impacts of the two production systems, the move from conventional cages to aviary systems averts an amount of welfare equivalent to 25% of the average chicken’s negative welfare range. (Continuing with the numbers mentioned in the previous step, it moves chickens from -2.5 to -1.25).
  3. So, assuming symmetry around the neutral point, 25% of chickens’ negative welfare range is equivalent to 2.25% (10% × 25%) of humans’ positive welfare range. 
  4. By definition, averting a DALY averts the loss of an amount of welfare equivalent to the positive portion of humans’ welfare range for a year.
  5. So, assuming symmetry around the neutral point, the move from conventional cages to aviary systems averts the equivalent of 0.025 DALYs per chicken per year on average.

The symmetry assumption doesn’t matter for our welfare range estimates. Instead, it matters for estimates of the total number of DALY-equivalents averted. Suppose, for instance, that humans’ welfare range is 0 to 100 (on net, their welfare is always neutral or positive) whereas chickens’ welfare range is -9 to 1 (their welfare can be 9x worse than it can be good). Our estimate of chickens’ relative welfare range would be the same: 10%. However, such an asymmetry would obviously alter the amount of welfare represented by “25% of chickens’ negative welfare range” (0.225 DALYs per chicken per year on average vs. 0.025 DALYs per chicken per year on average). To make the implications clear, we’ve developed a farmed animal welfare cost-effectiveness BOTEC that allows users to input their own assumptions about the skews of animals’ welfare ranges to convert welfare changes into DALY-equivalents averted.

Some welfare range estimates

What follows are some probability-of-sentience- and rate-of-subjective-experience-adjusted welfare range estimates. These numbers are based on:

  • estimates of the probability of sentience for the following taxa
  • welfare range estimates conditional on sentience for the following taxa, and
  • credence-adjusted rates of subjective experience estimates (based on Jason Schukraft’s prior work on the rate of subjective experience, about which more below).
Species5th-percentile50th-percentile95th-percentile
Pigs

0.005

0.515

1.031

Chickens

0.002

0.332

0.869

Octopuses

0.004

0.213

1.471

Carp

0

0.089

0.568

Bees

0

0.071

0.461

Salmon

0

0.056

0.513

Crayfish

0

0.038

0.491

Shrimp

0

0.031

1.149

Crabs

0

0.023

0.414

Black Soldier Flies

0

0.013

0.196

Silkworms

0

0.002

0.073

We provide the technical details in this document. We now turn to the more general methodology behind these numbers.

How did we estimate relative welfare ranges?

Given hedonism, an individual’s welfare range is the difference between the welfare level associated with the most intense positively valenced experience the individual can realize and the welfare level associated with the most intense negatively valenced experience that the individual can realize. So, we looked for evidence of variation in the capacities that generate positively and negatively valenced experiences.

Since there are no agreed-upon objective measures of the intensity of valenced states, we pursued a four-step strategy:

  1. Make some plausible assumptions about the evolutionary function of valenced experiences
  2. Given those functions, identify a lot of empirical traits that could serve as proxies for variation with respect to those functions
  3. Survey the literature for evidence about those traits
  4. Aggregate the results

There are many theories of valence, not all of which are mutually exclusive. For instance, some think that valenced experiences represent information in a motivationally-salient way (“That’s good” / “That’s bad” / “That’s really good” / etc.; Cutter & Tye 2011), others that valenced experiences provide a common currency for decision-making (“A feels better than B” / “C feels worse than D”; Ginsburg & Jablonka 2019), and others still that they facilitate learning (“If I do X, I feel good” / “If I do Y, I feel bad”; Damasio & Carvalho 2013). In all three cases, there are potential links between valence and conceptual or representational complexity, decision-making complexity, and affective (emotional) richness.

We conducted a large literature review for traits that could serve as indicators of conceptual or representational complexity, decision-making complexity, and affective richness, involving over 100 qualitative and quantitative proxies across 11 species. The literature review is available here. Descriptions of the proxies are available here (and for the “quantitative proxies” model, here).

We aggregated the results. However, aggregation raises lots of thorny methodological issues. So, we opted to build several models. For a variety of reasons, though, we ultimately opted not to include them all in our estimates: some could be accused of stacking the deck in favor of animals (the Equality Model), some were missing too much data (the Quantitative Model), and some involved assumptions that went beyond the key assumptions of the Moral Weight Project (the Grouped Proxy Model and the JND Model). We then took the remaining models and used Monte Carlo simulations to estimate the distribution of welfare ranges, as detailed here.

Jason Schukraft estimated that there’s a ~70% chance that there exist morally relevant differences in the rate of subjective experience and a ~40% chance that CFF values roughly track the rate of subjective experience under ideal conditions. So, we applied a credence-discounted adjustment to our welfare range estimates by the CFF for a given species. Since this proxy suggests that some animals have a faster rate of subjective experience than humans, it supports greater-than-human welfare range estimates on some models. 

Finally, we adjusted our estimates based on our best guess estimates of the probability of sentience. We generated those estimates by extending and updating Rethink Priorities’ Invertebrate Sentience Table and then aggregating the results as detailed here.

Questions about and objections to the Moral Weight Project’s methodology 

“I don't share this project’s assumptions. Can't I just ignore the results?”

We don’t think so. First, if unitarianism is false, then it would be reasonable to discount our estimates by some factor or other. However, the alternative—hierarchicalism, according to which some kinds of welfare matter more than others or some individuals’ welfare matters more than others’ welfare—is very hard to defend. (To see this, consider the many reviews of the most systematic defense of hierarchicalism, which identify deep problems with the proposal.)

Second, and as we’ve arguedrejecting hedonism might lead you to reduce our non-human animal estimates by ~⅔, but not by much more than that. This is because positively and negatively valenced experiences are very important even on most non-hedonist theories of welfare.

Relatedly, even if you reject both unitarianism and hedonism, our estimates would still serve as a baseline. A version of the Moral Weight Project with different philosophical assumptions would build on the methodology developed and implemented here—not start from scratch.

“So you’re saying that one person = ~three chickens?”

No. We’re estimating the relative peak intensities of different animals’ valenced states at a given time. So, if a given animal has a welfare range of 0.5 (and we assume that welfare ranges are symmetrical around the neutral point), that means something like, “The best and worst experiences that this animal can have are half as intense as the best and worst experiences that a human can have”—remembering that, in this context, the welfare level associated with “best experiences that a human can have” is the average welfare level of the average human in full health, which, presumably, is lower than the most intense pleasure humans are physically capable of experiencing. 

Because we’re estimating the relative intensities of valenced states at a time, not over time, you have to factor in lifespan to make individual-to-individual comparisons. Suppose, then, that the animal just mentioned—the one with a welfare range of 0.5—has a lifespan of 10 years, whereas the average human has a lifespan of 80. Then, humans have, on average, 16x this animal’s capacity for welfare; equivalently, its capacity for welfare is 0.0625x a human’s capacity for welfare.

However, while there are decision-making contexts where total capacity for welfare matters, they aren’t the most pressing ones. In practice, we rarely compare the value of creating animal lives with the value of creating human lives. Instead, we’re usually comparing either improving animal welfare (welfare reforms) or preventing animals from coming into existence (diet change → reduction in production levels) with improving human welfare or saving human lives. Whatever combination we consider, total capacity for welfare isn’t relevant. Instead, we want to know things like how much suffering we can avert via some welfare reform vs. how many years of human life will this intervention save. Welfare ranges can be helpful in answering the former question.

“I can’t believe that bees beat salmon!”

We also find it implausible that bees have larger welfare ranges than salmon. But (a) we’re also worried about pro-vertebrate bias; (b) bees are really impressive; (c) there's a great deal of overlap in the plausible welfare ranges for these two types of animals, so we aren't claiming that their welfare ranges are significantly different; and (d) we don’t know how to adjust the scores in a non-arbitrary way. So, we’ve let the result stand. (We’d make similar points in response to: “I can’t believe that octopuses beat carp!”)

“Even granting the project’s assumptions, it seems obvious that [insert species] have much smaller welfare ranges than you’re suggesting. If the empirical evidence doesn’t demonstrate that, isn’t it a problem with the empirical evidence?”

No. First, the empirical evidence is our only objective guide to animals’ abilities—avoiding the twin mistakes of anthropomorphism (attributing human characteristics to nonhumans) and what Franz de Waal calls “anthropodenial”—i.e., “the a priori rejection of shared characteristics between humans and animals.” So, we’re inclined to defer to it.

This deference, plus the assumption of hedonism, do a lot of work in explaining our estimates. Given our deference to the empirical literature, we aren’t positing differences if we can’t cite justifications for them. Given hedonism, lots of apparent differences between humans and animals don’t matter, as they’re irrelevant to the intensities of the valenced states. So, if our results seem counterintuitive, it may be that implicit disagreements about these assumptions explain that reaction.

Second, recall that we’re treating missing data as evidence against sentience and for larger welfare range differences. So, while the empirical evidence is limited, we aren’t using that fact to stack the deck in animals’ favor—quite the opposite.

Third, even if the results are counterintuitive, that is not necessarily a reason to reject the estimates (as we argue here). After all, it’s an open question whether we should trust any of our intuitions about animals’ ability to generate welfare, especially if those intuitions are driven by thinking about the practical implications of these estimates. There are many, many other assumptions that need to be in place before these estimates have any practical implications at all. So, if the practical implications are counterintuitive, those other assumptions are just as much to blame.

“I’m skeptical that [insert proxy] has much to do with welfare ranges.” 

In some cases, we share that skepticism; we readily grant that the proxy list could be refined. However, there is either a version of hedonism or a theory about valenced states on which each of the proxies bears on differences in welfare ranges. We couldn’t resolve all those theoretical issues in the time available. Moreover, we could reject certain proxies if we had independent ways to check whether our welfare range estimates are accurate. Plainly, though, we don’t. So, it’s best to err on the side of inclusiveness. Indeed, the proxy list could be expandedWe opted for a fairly inclusive approach to the proxies, which made the project enormous. Still, there are many other traits that could have been included—and, in some cases, perhaps ought to have been included in a list of this length. 

If we can make progress on the relevant theoretical issues, we can refine our proxy list. Until then, we’re navigating uncertainty by incorporating as many reasonable approaches as possible.

“How could there be as many ‘unknowns’ as you’re suggesting? After all, in this context, ‘not-unknown’ just means ‘above or below 50% however slightly’—and surely that’s a low bar.”

We thought it was important to have domain experts review the literature whenever possible. However, domain experts are academics. Academics are socialized into a community where it’s inappropriate to make some positive claim (“Pigs have this trait” or “pigs lack that trait”) without being able to establish that claim to the satisfaction of their peers. There are good reasons to value this socialization in the present case. For instance, it’s difficult to predict which traits an organism will have based on its other traits. Moreover, it’s difficult to predict whether one kind of organism will have a trait because a related kind of organism does. Still, even though the probability ranges we mentioned earlier establish a very low bar for “lean yes” and “lean no” (above and below 50%, respectively), we defaulted to “unknown” when we couldn’t find any relevant literature. Even if our approach is defensible, other reasonable literature reviewers may have had more “lean yes” and “lean no” assessments than we did. 

“You’re assessing the proxies as either present or absent, but many of them obviously come either in degrees or in qualitatively different forms.”

This is indeed a limitation; we readily acknowledge that many of the proxies are relatively coarse-grained. Consider a trait like reversal learning: namely, the ability to suppress a reward-related response, which involves stopping one behavior and switching to another. This trait comes in degrees: some animals can learn to suppress a reward-related response in fewer trials; and, having learned to suppress a reward-related response at all, some can suppress their response more quickly. A more sophisticated version of the project would account for this variation. 

However, it isn’t clear what to do about it, as the empirical literature doesn’t provide straightforward ways to score animals on many of these proxies. This problem might be solvable in the case of reversal learning specifically, since we can, at the very least, measure the rate at which the animal learns to suppress the reward-related response. In other cases, the problem is much harder. For instance, parental care is obviously different in humans than in chickens. But we don’t see how to quantify the difference without making many controversial assumptions that, in all likelihood, will simply smuggle in a range of pro-human biases. So, given the current state of knowledge, the present / absent approach seems best.

“It isn’t even clear to me that [insert species] are sentient. So, why should I accept your estimate of their (ostensible) welfare range?”

You shouldn’t. Instead, you should adjust our probability-of-sentience-conditioned estimate based on your credence in the hypothesis that [insert species] are sentient.

That being said, there is deep uncertainty about consciousness generally and sentience specifically. In the face of that uncertainty, we think there’s no good argument for assigning a credence below 0.3 (30%) to the hypothesis that normal adult pigs, chickens, carp, and salmon are sentient. Likewise, we think there’s no good argument for assigning a credence below 0.01 (1%) to the hypothesis that normal adult members of the invertebrate species of interest are sentient. So, skepticism about sentience might lead you to discount our estimates, but probably by fairly modest rates.

“Your literature review didn’t turn up many negative results. However, there are lots of proxies such that it’s implausible that many animals have them. So, your welfare range estimates are probably high.”

This is a good objection. However, it isn’t clear how aggressively to discount our results because of it. After all, we know so little about animals’ lives. In many cases, no one has cared enough to investigate welfare-relevant traits; in many other cases, no one knows how to investigate them. Moreover, the history of research on animals suggests that we’ll be surprised by their abilities. So, of the unknown proxies for any given species, we should expect to find at least some positive results—and perhaps many positive results. The upshot is that while it might make sense to discount our estimates by some modest rate (e.g., 25%—50%), we don’t think it would be reasonable to discount them by, say, 90%, much less 99%.

In any case, we should stress that we aren’t inflating our estimates: we’re just following what seems to us to be a reasonable methodology, premised on deferring to the state of current knowledge. As we learn more about these animals, we should—and will indeed—update.

In future work, we could make inferences about proxy possession from more distant taxa. Or, we could try using a modern missing data method to account for any potential systematic trends in why some species-model pairs have no extant evidence.

“Shouldn’t you give neuron counts more weight in your estimates?”

We discuss neuron counts in depth here. In brief, there are many reasons to be skeptical about the value of neuron counts as proxies for welfare ranges. Moreover, some ways of incorporating neuron counts would increase our welfare range estimates for invertebrates, not decrease them. So, we already regard the weight currently assigned as a kind of compromise with community credences. 

“You don’t have a model that’s based on the possibility that the number of conscious systems in a brain scales with neuron counts (i.e., 'the Conscious Subsystems Hypothesis')."

We discuss the conscious subsystems hypothesis in depth hereThe conscious subsystems hypothesis is a highly controversial philosophical thesis. So, given our methodological commitment to letting the empirical evidence drive the results, we decided not to include this hypothesis in our calculations.

How confident are we in our estimates and what would change them?

No one should be very confident in any estimate of a nonhuman animal’s welfare range. We know far too little for that. However, we’re reasonably confident about some things.

Given hedonism and conditional on sentience, we think (credence: 0.7) that none of the vertebrate nonhuman animals of interest have a welfare range that’s more than double the size of any of the others.  While carp and salmon have lower scores than pigs and chickens, we suspect that’s largely due to a lack of research.

Given hedonism and conditional on sentience, we think (credence: 0.65) that the welfare ranges of humans and the vertebrate animals of interest are within an order of magnitude of one another.

While humans have some unique and impressive abilities, those abilities have histories; they didn’t just pop into existence when humans came on the scene. Many nonhuman animals have precursors to these abilities (or variants on them, adapted to animals’ particular ecological niches). 

Moreover, and more importantly, it isn’t clear that many of these impressive abilities make much difference to the intensity of the valenced states that humans can realize. Instead, humans seem to realize a much greater variety of valenced states. If hedonism is true, though, variety probably doesn’t matter; intensity does the work.

Given hedonism and conditional on sentience, we think (credence 0.6) that all the invertebrates of interest have welfare ranges within two orders of magnitude of the vertebrate nonhuman animals of interest. Invertebrates are so diverse and we know so little about them; hence, our caution.

As for what would change our mind, the main thing is research on the proxies. In principle, research on the proxies could alter our welfare range estimates significantly. Right now, the proxies are fairly coarse-grained and we aren’t confident about their relative importance. If, for instance, we were to learn there are ten levels of reversal learning and that shrimp only reach the second, that could significantly alter our results. Likewise, if we were to learn that having a self-concept is 10x more important than parental care when it comes to estimating differences in welfare ranges, that could significantly alter our results.

Conclusion

Our view is that the estimates we’ve provided are placeholders. Our estimates will change as we learn more about all animals, human and nonhuman. They will change as we learn more about the various traits we share with nonhuman animals and the various traits we don’t share with them. They will change with advances in comparative cognition, neuroscience, philosophy, and various other fields. We’re under no illusions that we’re providing the last word on this topic. Instead, we’re providing a starting point for more rigorous, empirically-driven research into animals’ welfare ranges. At the same time, we’re offering guidance for decisions that have to be made long before that research is finished.

 

Acknowledgments

This research is a project of Rethink Priorities. It was written by Bob Fischer. For help at many different stages of this project, thanks to Meghan Barrett, Marcus Davis, Laura Duffy, Jamie Elsey, Leigh Gaffney, Michelle Lavery, Rachael Miller, Martina Schiestl, Alex Schnell, Jason Schukraft, Will McAuliffe, Adam Shriver, Michael St. Jules, Travis Timmerman, and Anna Trevarthen. If you’re interested in RP’s work, you can learn more by visiting our research database. For regular updates, please consider subscribing to our newsletter.

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48 comments, sorted by Click to highlight new comments since: Today at 3:53 AM

Hi Bob & team,

Really great work. Regardless of my specific disagreements, I do think calculating moral weights for animals is literally some of the highest value work the EA community can do, because without such weights we cant compare animal welfare causes to human-related global health/longtermism causes - and hence cannot identify and direct resources towards the most important problems. And I say this as someone who has always donated to human causes over animal ones, and who is not, in fact, vegan.

With respect to the post and the related discussion:

(1) Fundamentally, the quantitative proxy model seems conceptually sound to me.

(2) I do disagree with the idea that your results are robust to different theories of welfare. For example, I myself reject hedonism and accept a broader view of welfare (given that we care about a broad range of things beyond happiness,  e.g. life/freedom/achievement/love/whatever). If (a) such broad welfarist views are correct, (b) you place a sufficiently high weight on the other elements of welfare (e.g. life per se, even if neutral valenced), and (c) you don't believe animals can enjoy said elements of welfare (e.g. if most animals aren't cognitively sophisticated enough to have preferences over continued existence), then  an additional healthy year of human life would plausibly be worth a lot more than an equivalent animal year even after accounting for similar degrees of suffering and the relevant moral weights as calculated.

(3) I would like to say, for the record, that a lot of the criticism you're getting (and I don't exempt myself here) is probably subject to a lot of motivated reasoning. I am personally uncertain as to the degree to which I should discount my own conclusions over this reason.

(4) My main concern, as someone who does human-related cause prioritization research, is the meat eater argument and whether helping to save human lives is net negative from overall POV, given the adverse consequences for animal suffering. I am moderately optimistic that this is not so, and that saving human lives is net positive (as we want/need it to be) . Having very roughly run the numbers myself using RP's unadjusted moral weights (i.e. not taking into account point 2 above) and inputting other relevant data (e.g. on per capita consumption rate of meat), my approximate sense is that in saving lives we're basically buying 1 full week of healthy human life for around 6 days of chicken suffering or above 2 days of equivalent human suffering - which is worth it.

Thanks for the kind words about the project, Joel! Thanks too for these thoughtful and gracious comments.

1. I hear you re: the quantitative proxy model. I commissioned the research for that one specially because I thought it would be valuable. However, it was just so difficult to find information. To even begin making the calculations work, we had to semi-arbitrarily fill in a lot of information. Ultimately, we decided that there just wasn't enough to go on.

2. My question about non-hedonist theories of welfare is always the same: just how much do non-hedonic goods and bads increase humans' welfare range relative to animals' welfare ranges? As you know, I think that even if hedonic goods and bads aren't all of welfare, they're a lot of it (as we argue here). But suppose you think that non-hedonic goods and bads increase humans' welfare range 100x over all other animals. In many cost-effectiveness calculations, that would still make corporate campaigns look really good.

3. I appreciate your saying this. I should acknowledge that I'm not above motivated reasoning either, having spent a lot of the last 12 years working on animal-related issues. In my own defense, I've often been an animal-friendly critic of pro-animal arguments, so I think I'm reasonably well-placed to do this work. Still, we all need to be aware of our biases.

4. This is a very interesting result; thanks for sharing it. I've heard of others reaching the same conclusion, though I haven't seen their models. If you're willing, I'd love to see the calculations. But no pressure at all.

Would love to see the draft calculations from point 4 as well.

Will DM on slack!

At risk of jeopardizing EA's hard-won reputation of relentless internal criticism:

Even setting aside its object-level impact-relevant criteria (truth, importance, etc), this is just enormously impressive both in terms of magnitude and quality. The post itself gives us readers an anchor on which to latch critiques, questions, and comments, so it's easy to forget that each step or decision in the whole methodology had to be chosen from an enormous space of possibilities. And this looks— at least on a first red—like very many consecutive well-made steps and decisions

Thanks for the kind words, Aaron!

I'm curating the post. I should note that I think I agree with a big chunk of Joel's comment.

I notice I'm quite confused about the symmetry assumption. For example: suppose we have two animals — M and N — and they're both at the worst end of their welfare ranges (~0th percentile) and have equal lifespans (and there are no indirect effects). M has double the welfare range of N. If we assume that their welfare ranges are symmetric around the neutral point, then replacing one M with one N is similar to moving M from the 0th percentile of its welfare range to the 25th. If, however, their welfare ranges aren't symmetric — say M's is skewed very positive and N's is skewed very negative — then we could actually be making the situation worse. In the BOTEC spreadsheet you linked, you seem to resolve this by requiring people to state the specific endpoints of the welfare ranges relative to the neutral point. If that's the main solution, it seems very important to be clear about where the neutral point is for different animals, and that seems really hard — I'm curious if you have thoughts on how to approach that. (Maybe you assume that welfare ranges are generally close to symmetric, or asymmetric in similar ways? If so, I would like to understand why you think that.) It's also very possible that I misunderstood something; I was reading things fast and haven't read all the linked posts and documents.

To make sure that I understand (the broad strokes of the rest of the framework) correctly; suppose I want to use this framework and these welfare range estimates to help me decide between two (completely hypothetical, unrealistic) options — assuming that every animal's welfare range is symmetric around the neutral point: (A) getting someone to buy the equivalent of a cage-free chicken instead of a caged chicken vs (B) getting someone to buy a farmed salmon instead of a farmed carp. Is it right that I'd now need to incorporate (estimates for) the following additional information? 

  1. To understand the welfare impact on the animals in question
    1. Lifespans of the animals[1]
    2. Where exactly on their respective welfare ranges they are, on average (in the situations I'm considering)[2]
  2. The other stuff
    1. Indirect effects
      1. E.g. how (many) other animals are affected by the farming processes — feed (insects/fish), how many die in the farming process, etc.
    2. Costs of the interventions

(In particular, I worry a bit that people might not be tracking 1a and 1b — you seem to worry about this, too, given the sections on things like "so you're saying that one person =~ three chickens?" — and I'd like to make sure that I actually understand correctly (and that others do, too).)

  1. ^

    Broiler chickens live for 5-7 weeks, apparently. Farmed carp apparently live for around a year, and farmed salmon live for around 1-3 years. (These numbers are from quick Google searches  —definitely don't trust them.) 

  2. ^

    A highly technical diagram is below. Note that the diagram represents the ranges as if they're all symmetric — as if each animal can experience as much bad as good —  whereas that isn't necessarily true. The welfare impact of choice (A) and (B) is the highlighted interval (assuming completely made-up numbers), multiplying by the lifespans of the animals, and adjusting for indirect effects.

    Given the lifespans of the animals in question, switching to salmon seems harmful ((even) without accounting for indirect effects or costs). 

Fantastic questions, Lizka! And these images are great. I need to get much better at (literally) illustrating my thinking. I very much appreciate your taking the time!

Here are some replies:

Replacing an M with an N. This is a great observation. Of course, there may not be many real-life cases with the structure you’re describing. However, one possibility is in animal research. Many people think that you ought to use “simpler” animals over “more complex” animals for research purposes—e.g., you ought to experiment on fruit flies over pigs. Suppose that fruit flies have smaller welfare ranges than pigs and that both have symmetrical welfare ranges. Then, if you’re going to do awful things to one or the other, such that each would be at the bottom of their respective welfare range, then it would follow that it’s better to experiment on fruit flies. 

Assessing the neutral point. You’re right that this is important. It’s also really hard. However, we’re trying to tackle this problem now. Our strategy is multi-pronged, identifying various lines of evidence that might be relevant. For instance, we’re looking at the Welfare Footprint Data and trying to figure out what it might imply about whether layer hens have net negative lives. We’re looking at when vets recommend euthanasia for dogs and cats and applying those standards to farmed animals. We’re looking at tradeoff thought experiments and some of the survey data they’ve generated. And so on. Early days, but we hope to have something on the Forum about this over the summer.

Symmetry vs. asymmetry. This is another hard problem. In brief, though, we take symmetry to be the default simply because of our uncertainty. Ultimately, it’s a really hard empirical question that requires time we didn’t have. (Anyone want to fund more work on this!?) As we say in the post, though, it’s a relatively minor issue compared to lots of others. Some people probably think that we’re orders of magnitude off in our estimates, whereas symmetry vs. asymmetry will make, at most, a 2x difference to the amount of welfare at stake. That isn’t nothing, but it probably won’t swing the analysis.

 The "caged vs. cage-free chicken / carp vs. salmon" examples. This is a great question. We’ve done a lot on this, though none of it’s publicly available yet. Basically, though, you’re correct about the information you’d want. Of course, as your note indicates, we don’t care about natural lifespan; we care about time to slaughter. And while it’s very difficult to know where an animal is in its welfare range, we don’t think it’s in principle inestimable. Basically, if you think that caged hens are living about the worst life a chicken can live, you say that they’re at the bottom end of their welfare range. And if you think cage-free hens have net negative lives, but they’re only about half as badly off as they could be, then can infer that you’re getting a 50% gain relative to chickens’ negative welfare range in the switch from caged to cage-free. And so on. This is all imperfect, but at least it provides a coherent methodology for making these assessments. Moreover, it's a methodology that forces us to be explicit about disagreements re: the neutral point and the relative welfare levels of animals in different systems, which I regard as a good thing.

Hi Bob and RP team,

I've been working on a comparative analysis of the knock-on effects of bivalve aquaculture versus crop cultivation, to try to provide a more definitive answer to how eating oysters/mussels compares morally to eating plants. I was hoping I could describe how I'd currently apply the RP team's welfare range estimates, and would welcome your feedback and/or suggestions. Our dialogue could prove useful for others seeking to incorporate these estimates into their own projects.

For bivalve aquaculture, the knock-on moral patients include (but are not limited to) zooplankton, crustaceans, and fish. Crop cultivation affects some small mammals, birds, and amphibians, though its effect on insect suffering is likely to dominate.

RP's invertebrate sentience estimates give a <1% probability of zooplankton or plant sentience, so we can ignore them for simplicity (with apologies to Brian Tomasik). The sea hare is the organism most similar to the bivalve for which  sentience estimates are given, and it is estimated that a sea hare is less likely to be sentient than an individual insect. Although the sign of crop cultivation's impact on insect suffering is unclear, the magnitude  seems likely to dominate the effect of bivalve aquaculture on the bivalves themselves, so we can ignore them too for simplicity.

The next steps might be:

  1. Calculate welfare ranges:
    1. For bivalve aquaculture, use carp, salmon, crayfish, shrimp, and crabs to calculate a welfare range for the effect of bivalve aquaculture on marine populations.
    2. Use chickens as a model species to calculate a welfare range for the effect of crop cultivation on vertebrate populations.
    3. For the effect of crop cultivation on insect suffering, I might just toss this problem on to future researchers. I'm only doing this as a side project, and given the sheer complexity of the considerations at play, I'm worried I might publish something which inadvertently increases insect suffering instead of decreasing it.
  2. For several moral views (negative utilitarianism, symmetric utilitarianism) and several perspectives of the value of a typical wild animal's life (net negative, net neutral, net positive), extract relevant conclusions. (e.g. if bivalve aquaculture is robustly shown to increase marine populations, given Brian's arguments that crop cultivation likely reduces vertebrate populations, a negative utilitarian who views wild animal lives as net negative may want to oppose bivalve consumption.)

(Of course, I'd have to mention longtermist considerations. The effect of  norms surrounding animal consumption on moral circle expansion could be crucial. So could the effect of these consumption practices on climate change or on food security.)

Thanks for your comment, Ariel, and sorry for the slow reply! What you've described sounds great as far as it goes. However, my basic view here--which I offer with sincere appreciation for the project you're describing and a genuine desire to see it completed--is that the uncertainties are so far-reaching that, while we can get clearer about the conditions under which, say, a negative utilitarian will condemn bivalve consumption, we basically have no idea which condition we're in. So, I think that the most valuable thing right now would be to write up specific empirical research questions and value-aligned ways of operationalizing the key concepts. Then, we should be hunting for graduate students and early-career researchers who might be willing to do the empirical work in exchange for relatively small amounts of funding. (Many academics are cheap dates.) From my perspective, EA has gone just about as far as it can already on these kinds of questions without more substantive collaborations with entomologists, aquatic biologists, ecologists, and so on.

All that said, I'll stress that I completely agree with you about the importance of getting answers here! I just think we're at the point where we can't make much more progress toward them from the armchair.

This is really valuable work, and I look forward to seeing the discussion that it generates and to digging into it more closely myself. I did have one immediate question about the neuron count model specifically, though I recognize that it's a  a small contributor to the overall weights.  I'd be curious to understand how you arrived at 13 million neurons as your estimate for salmon. The reference in the spreadsheet is: 

The teleost brain is capable of adult neurogenesis, with neural proliferation zones in dozens of locations within the brain (e.g. Zupanc et al. 2005, Zupanc 2009). This makes a definitive count of total neurons within the brain difficult, since the number of neurons may be continuously in flux. For example, Zupanc (2009) summarizes: “the continuous production of new cells, together with the longterm persistence of a large portion of them, leads to a permanent growth of the brain and its individual structures... This growth by a net increase in the total number of brain cells is characteristic of at least some, but likely most, of the estimated 30,000 species of teleost fish.” Therefore, reports of total neuron counts for salmon and carp are rare, but Hinsch & Zupanc (2007) report that “By labeling S-phase cells with the thymidine analog 5-bromo-2-deoxyuridine (BrdU), quantitative analysis demonstrated that, on average, 6000 new cells were generated in the entire adult brain within any 30 min period. This corresponds to roughly 0.06% of the total number of brain cells” in an adult zebrafish (Danio rerio, a model cyprinid) brain. As part of their study, Hinsch & Zupanc (2007) report that, for adult zebrafish, the total number of brain cells varied between 0.8 x 107 and 1.3 x 107 (mean: 1.0 x 107 ± S.E.M. 8 x 105). They also report that “approximately 46% of the cells present at 10 days persisted in the adult zebrafish brain” meaning that “​​at least half of the cells generated in the adult zebrafish brain develop into neurons and are likely to persist for the rest of the fish’s life.” This pattern is reflected in other species of teleosts, for example in adult gymnotiform fish (Apteronotus leptorhynchus) who generate 100 000 new brain cells (corresponding to approximately 0.2% of the total population of cells in the brain) within a period of 2 hours (Zupanc & Horschke 1995). Thus the teleost brain is constantly growing and likely increasing in terms of total number of neurons, and counts are only representative of snapshots through time.

I don't easily see how that translates to 13 million neurons. When I previously looked at this issue myself, I came away thinking it was possible that salmon had substantially more neurons than you're estimating. 

Thanks, MHR. Quick reply to say: Good question, but I don't know the answer offhand, as I didn't come up with that number myself. Many different people helped with the literature reviews. I'll get in touch with the relevant person and get back to you.

Sorry for the delay, MHR! It took a bit to get to the bottom of this. In any case, the short version is that the 8-13M neuron count for both salmon and carp should be read as the lowest reasonable estimate, not our best guess. We got the number from the zebrafish literature--specifically, a study by Hinsch & Zupanc (2007) (cited in the table) who reported that the total number of brain cells for adult zebrafish varied between 8 and 13 million. In the notes associated with the Welfare Range Table, we had a caveat that neuron counts are very hard to come by in fish and, in any case, only represent a snapshot in time, because the teleost brain is constantly growing. Moreover, no one has done total neuron count estimates for salmon or carp, whereas zebrafish are often used as a model species and are well-studied; so, we simply used those values as a placeholder. Granted, then, the 8-13M number may well be an underestimate due to the size differences between zebrafish and salmon, and we do see the appeal of using Invincible Wellbeing's curve fits to come up with a higher number. However, we tried to stick as close to the empirical literature as possible. And truth be told, because neuron counts are just one of several models we include, using a higher number wouldn't make a major difference to our welfare range estimates for salmon or carp.

The upshot is that is one of many cases where our methodology is more conservative than many EAs have been when doing related projects (e.g., we were more inclined to default to "unknown," we used lower-bound placeholder values in some cases, etc.). Advantages and disadvantages!

Thanks Bob, that makes sense!

Just to see the magnitude of the change, I tried rerunning the model with a neuron count estimate of 100 million for salmon. That led to salmon's 50th-percentile estimate increasing by 0.001 and  95th-percentile estimate increasing by 0.002. So you're right that it's not really a noticeable impact. 

Hello to all,

Have you contacted the Integrated Information Theory group about this project? In my (dualistic naturalist) viewpoint their work is the most advanced in the area of consciece detection. 

https://www.amazon.com/Sizing-Up-Consciousness-Objective-Experience/dp/0198728441

Of course, conscience is absolutely noumenal and the best part of their work is focused in the case where self reported conscience experience is possible [humans], but they tried to extrapolate into mathematical models of application to any material system.

The last I read about Integrated Information Theory was Scott Aaronsson's criticism of it. Has his arguments been addressed, because I found it very compelling? 

Regarding the neurological part (the conscience detector based in brain information) that is described in "Sizin Up consciuosness" I think they are mostly rigth. The IIT mathematical model is beyond my understanding, and the Aronsson criticism also. But given my naturalistic dualist vision of conscience, unfortunately only an axiomatic and extrapolative way to consciousness measurement is possible. 

Good suggestion, Arturo. We haven't reached out, but it's certainly worth having a conversation.

Love this type of research, thank you very much for doing it!

I'm confused about the following statement:

While carp and salmon have lower scores than pigs and chickens, we suspect that’s largely due to a lack of research.

Is this a species-specific suspicion? Or does a lower amount of (high-quality) research on a species generally reduce your welfare range estimate? 
On average I'd have expected the welfare range estimate to stay the same with increasing evidence, but the level of certainty about the estimate to increase. 

If you have reason to believe that the existing research is systematically biased in a way that would lead to higher welfare range estimates with more research,  do you account for this bias in your estimates?

Great question, Tobias. Yes, less research on a species generally reduces our welfare range estimate. I agree with you that it would be better, in some sense, to have our confidence increase in a fixed estimate rather than having the estimates themselves vary. However, we couldn't see how to do that without invoking either our priors (which we don't trust) or some other arbitrary starting point (e.g., neuron counts, which we don't trust either). In any case, that's why we frame the estimates as placeholders and give our overall judgments separately: vertebrates at 0.1 or better, the vertebrates themselves within 2x of one another, and the invertebrates within 2 OOMs of the vertebrates.

I skimmed the piece on axiological asymmetries that you linked and am quite puzzled that you seem to start with the assumption of symmetry and look for evidence against it. I would expect asymmetry to be the more intuitive, therefore default, position. As the piece says

At just the first-order level, people tend to assume that (the worst) pain is worse than (the best) pleasure is pleasurable. The agonizing ends for non-human animals in factory farms and in the wild seem far worse than the best sort of life they could realize would be good. [...]  it’s hard to find any organisms that risk the worst pains for the greatest pleasures and vice versa.

I would expect that a difference in magnitude between the best pleasure and worst possible is the most obvious explanation, but the piece concludes that these judgments are "far more plausibly explained by various cognitive biases".

As far as I can tell this would suggest that either:

  • Someone who has recently experienced or is currently experiencing intense suffering (and therefore has a better understanding of the stakes) would be more willing to take the kind of roulette gamble described in the piece. This seems unlikely.
  • People's assessments of hedonic states are deeply unreliable even if they have recent experience of the states in question. I don't like this much because it means we have to fall back on physiological evidence for human pleasure/suffering, which, as shown by the mayonnaise example, can't give us the full picture.

On a slightly separate note, I played around with the BOTEC to check the claim that assuming symmetry doesn't change the numbers much and I was convinced. The extreme suffering-focused assumption (where perfect health is merely neutral) resulted in double the welfare gain of the symmetric assumption (when the increase in welfare as a percentage of the animals' negative welfare range is held constant). 

My main question on this last point is: why use "percentage of the animals' negative welfare range" when "percentage of the animals' total welfare range"  seems more relevant and would not vary at all across different (a)symmetry assumptions?

Thanks for reading that Stan! Good question. I realize now that my report and the post together are a bit confusing because there are two types of symmetry at issue that seem to get blended together. I could have been clearer about this in the report. Sorry about that! 

First, the post mentions the concept of welfare ranges being *symmetrical around the neutral point*. Assuming this means assuming that the best realizable welfare state is exactly as good as the worst realizable welfare state. That is assumed for simplicity, though the subsequent part of the post is meant to show that that assumption matters less than one might think. 

Second, in my linked report, I focus on the concept of *axiological symmetries* which concern whether every fundamental good-making feature of a life has a corresponding fundamental bad-making feature. If we assume this and, for instance, believe that knowledge is a fundamental good-making feature, then we'd have to think that there is a corresponding fundamental bad-making feature (unjustified false belief, perhaps). 

These concepts are closely related, as the existence of axiological asymmetries may provide reason to think that welfare is not symmetrical around the neutral point and vice versa. Nevertheless, and this is the crucial point, it could work out that there is complete axiological symmetry, yet welfare ranges are still not symmetrical around the neutral point. This could be because some beings are constituted in such a way that, at any moment in time, they can realize a greater quantity of fundamental bad-making features than fundamental good-making features (or vice versa).

Axiological asymmetries seem prima facie ad hoc. Without some argument for specific axiological asymmetries and without working out their axiological implications, I do think axiological symmetry should be the default assumption. There's some nice discussion of this kind of issue in the Teresa Bruno-Niño paper cited in the report. In fact, it seems to me that both (what she calls) continuity and unity are theoretical virtues. 

https://www.pdcnet.org/msp/content/msp_2022_0999_11_25_29

Now, even granting what I just wrote about axiological symmetry, perhaps the default assumption should be that welfare is not symmetrical around the neutral point for the reasons you gave. That seems totally reasonable! I personally don't have strong views on this. Though, I do think there is a good evolutionary debunking argument to give for why animals (including humans) might be more motivated to avoid pain than accrue pleasure and why humans might be disposed to be risk-adverse in the roulette wheel example. I'm genuinely not sure how much these considerations suggest that the default is that welfare is not symmetrical around the neutral point. 

Whether welfare is symmetrical around the neutral point is largely an empirical question, though. I wouldn't be surprised if we discover that welfare is not symmetrical around the neutral point. That's a very realistic possibility. Though still a viable possibility, I would be somewhat surprised if we discover any axiological asymmetries. 

Thanks for your questions, Stan. Travis wrote the piece on axiological asymmetries and he can best respond on that front. FWIW, I'll just say that I'm not convinced that there's a difference of an order of magnitude between the best pleasure and the worst pain--or any difference at all--insofar as we're focused on intensity per se. I'm inclined to think it's just really hard to say and so I take symmetry as the default position. For all that, I'm open to the possibility that pleasures and pains of the same intensity have different impacts on  welfare, perhaps because some sort of desire satisfaction theory of welfare is true, we're risk-averse creatures, and we more strongly dislike signs of low fitness than the alternative. Point is: there may be other ways of accommodating your intuition than giving up the symmetry assumption.

To your main question, we distinguish the negative and positive portions of the welfare range because we want to sharply distinguish cases where the interventions flips the life from net negative to net positive. Imagine a case where an animal has a symmetrical welfare range and an intervention moves the animal either 60% of their negative welfare range or 60% of their total welfare range. In the former case, they're still net negative; in the latter case, they now net positive. If you're a totalist, that really matters: the "logic of the larder" argument doesn't go through even post-intervention in the former case, whereas it does go through in the latter. 

What follows are some probability-of-sentience- and rate-of-subjective-experience-adjusted welfare range estimates.

The probability of sentience is multiplied through here, right? Some of these animals are assigned <50% probability of sentience but have nonzero probability of sentience-adjusted welfare ranges at the median. Another way to present this would be to construct the random variable that's 0 if they're not sentient, and then equal to the random variable representing their moral weight conditional on sentience. This would be your actual distribution of welfare ranges for the animal, accounting for their probability of sentience. That being said, what you have now might be more useful to represent a range of expected moral weights for (approximately) risk-neutral EV-maximizing utilitarians, to represent deep uncertainty or credal fragility.

The use of expected value doesn't seem useful here. Your confidence intervals are huge (95% confidence interval for pig suffering capacity relative to humans is between 0.005 to 1.031). Because the implications are so different across that spectrum (varying from basically "make the cages even smaller, who cares" at 0.005 to "I will push my nan down the stairs to save a pig" at 1.031) it really doesn't feel like I can draw any conclusions from this.

Fair enough, Henry. We have limited faith in the models too. But as we said:

  1. The numbers are placeholders.
  2. Our actual views are summarized in the key takeaways and again toward the end (e.g., within an order of magnitude of humans for vertebrates--0.1 or above--which certainly does make a practical difference).
  3. This work builds on everything else we've done and is not, all on its own, the complete case for relatively animal-friendly welfare range estimates.

To follow up on Bob's point, the ranges presented here are from a mixture model which combines the results from several models individually. You can see the results for each model here: https://docs.google.com/spreadsheets/d/1SpbrcfmBoC50PTxlizF5HzBIq4p-17m3JduYXZCH2Og/edit?usp=sharing 

For example, the 0.005 arises because we are including the neuron count model of welfare ranges in our overall estimates. If you don't include this model (as there are good reasons not to, see https://forum.effectivealtruism.org/posts/Mfq7KxQRvkeLnJvoB/why-neuron-counts-shouldn-t-be-used-as-proxies-for-moral) then the 5th percentile welfare range for pigs of all models combined is 0.20. 

The 1.031 comes from a model called the "Undiluted Experiences" model, which suggests that animals with lower cognitive abilities have greater welfare ranges because they are not as able to rationalize their feelings (eg. pets being anxious when you're packing for a trip). A somewhat different model would be the "Higher-Lower Pleasures" model that is built on the idea that higher cognitive capacities means you can experience more welfare (akin to the JS Mill idea of higher-order pleasures). Under this model, we estimate that the range for pigs is 0.23 to 0.49--which is quite significant given how this model could be seen as having a pro-human bias! 

In sum, the welfare ranges presented above reflect our high degree of uncertainty surrounding how to think about measuring welfare. As such, we invite you to take a closer look at each model (you'll find most of them converge on the overall conclusion that vertebrates are within an order of magnitude of humans in terms of their welfare ranges). 

I'm curious whether you've indicated parental care is "present" or "absent" in bees, however, I have briefly checked the documents linked and couldn't find where that lives but maybe I missed it. Can anyone link to that documentation?

(Bees provide care to young, but it's primarily done by siblings, not parents, so it's considered alloparental care, not parental care. I should think that probably counts, but wasn't sure.)

Sorry about the confusion, mvolz. The table with the models is tricky to navigate. Here's the one we shared originally, which is clearer. Short answer: yes, we said it was present.

so although I'm not worth only 3 chickens, the key takeaway is that I'm worth around 50 chickens, is that the deal?

I will respond with my interpretation of the report, so that the author might correct me to help me understand it better.

If you ask "If we have an option between preventing the birth of Sabs versus preventing the birth of an average chicken, how many chickens is Sabs worth?" then Sabs might be worth -10 chickens since chickens have net negative lives whereas you (hopefully) have a net positive life.

If you ask "Let's compare a maximally happy Sabs and maximally happy chickens, how many chickens is Sabs  worth?", I don't think these estimates respond to that either. It might be the case that chickens have a very large welfare range, but this is mostly because they have a potential for feeling excruciating pain even though their best lives are not that good.

I think you need to complement  this research with "how much the badness of  average experiences of animals compare with each other" to answer your question. This report by Rethink Priorities seems to be based on the range between the worst  and the best experiences for each species.

This is exactly right, Emre. We are not commenting on the average amount of value or disvalue that any particular kind of individual adds to the world. Instead, we're trying to estimate how much value different kinds of individuals could add to the world. You then need to go do the hard work of assessing individuals' actual welfare levels to make tradeoffs. But that's as it should be. There's already been a lot of work on welfare assessment; there's been much less work on how to interpret the significance of those welfare assessments in cross-species decision-making. We're trying to advance the latter conversation.

Thank you for the prompt reply Bob. Just to be clear, I am happy about the scope of this project and am impressed by its quality. I do not intend to criticise the report for being mindful about its scope.

Didn't take it that way at all! I appreciate your taking the time to comment and help clarify what we've done.

Thanks for your question, Sabs. Short answer: if (a) you think of your value purely in terms of the amount of welfare you can generate, (b) you think about welfare in terms of the intensities of pleasures and pains, (c) you're fine with treating pleasures and pains symmetrically and aggregating them accordingly,  and (d) you ignore indirect effects of benefitting humans vs. nonhumans, then you're right about the key takeaway.  Of course, you might not want to make those assumptions! So it's really important to separate what should, in my view, be a fairly plausible empirical hypothesis--that the intensities of many animals' pleasures and pains are pretty similar to the intensities of humans' pleasures and pains--from all the philosophical assumptions that allow us to move from that fairly plausible empirical hypothesis to a highly controversial philosophical conclusion about how much you matter.

I think you should put this in big letters on the graph and that Peter should write it in his tweet thread. Currently this is going to get misunderstood and since you can predict this, I suggest it's your responsibility to avoid it.

That graph and all tables need to be hard to share without the provisos you've given here.

Thanks, Nathan. This is a good point.

In particular Edouard of Our World In Data said that they really care about their graphs being understood well and that when they see a graph being mistaken or with a bad legend they change it. 

I think this is the right approach to ensure that graphs are shared with context.

I've redone the summary image, Nathan. Thanks again for recommending this.

Really appreciate this thread ^. I'm impressed that something misleading got pointed out by Nathan/Sabs and then was immediately improved. 

Minor comment: I'd maybe re-title the image to something like "For each species, an estimate of their welfare range" or "Estimated welfare ranges per year of life of different species" ? I find "Placeholder Welfare Range Estimates (Life Years)" somewhat hard to parse. Although having written this, I'm not sure that my suggestions are better. 

(And thanks for writing the post and working on this project!)

Good of you to say, Lizka. Thanks.

Re: the title of the image, that's a helpful suggestion. I'm genuinely unsure what's best. The most accurate title would be something like, "Welfare range estimates by species for welfare-to-DALYs-averted conversions," but that doesn't win any awards for accessibility.

It's also per period of time, and humans live much longer than chickens.

ok. Well I don't actually care about how much I think I matter (obviously the answer is "an enormous amount"), what I really care about is how much you think I matter, or how much the median EA thinks I matter. How many of these four assumptions you listed do you actually believe? If you do believe some of them, then presumably in your eyes I am worth some relatively low number of chickens, right? What happens if my neck is on the block and you have the choice between sacrificing me or wringing the necks of 100 chickens? That's the really important key question here.

Hi Sabs. We can discuss this a bit in a comment thread, but the issues here are complicated. If you'd like to have a conversation, I'm happy to chat. Please DM me for a link to my calendar.

Brief replies to your questions:

  1. I think you matter an enormous amount too. I am not saying this facetiously. It's probably the thing I believe most deeply.
  2. I don't know how much the median EA thinks you matter.
  3. I'm unsure about all four assumptions. However, I'm also unsure about their practical importance. You might not be comfortable with the results of any cross-species cost-effectiveness analysis.
  4. If it's you or a hundred chickens, I'd save you. I'd also save my children over a hundred (human) strangers. I don't think this means that my children realize more welfare than those strangers. Likewise, I don't think you realize 100x more welfare than a chicken can.

I think it's also helpful to empathise the other way around too when working on these thought-experiments. Species-membership is merely a shortcut for speaking about typical cognitive and hedonic capacities in this report, species itself is irrelevant. You might be thinking that  prioritising the torture of 1000 chickens over the life of one human being doesn't make you feel valued as much as you should be. 

But it could be the other way around in the real life as well. An illness could befall on us or our loved ones. It could very much be the case that my sister had cognitive/hedonic capacities comparable to a pig.  I wouldn't feel very much valued if my sister being tortured for a year was considered to be less of a deal compared to averting the 10 minutes long headache of a typical human being in this case.

This is extremely interesting and thought-provoking, but bees beating salmon really does undermine any attempt I can make to give this a lot of credence.

Moreso, though, I object to saying we can trade one week of human life for six days of chicken torture (in the comments). But this is more my critique of utilitarianism, as I lay out in "Biting the Philosophical Bullet" here.

Thanks, Matt. As we say, though, we don't actually think that bees beat salmon. We think that the vertebrates are 0.1 or better of humans, that the vertebrates themselves are within 2x of one another, and that the invertebrates are within 2 OOMs of the vertebrates. We fully recognize that the models are limited by the available data about specific taxa. We aren't going to fudge the numbers to get more intuitive results, but we definitely don't recommend using them uncritically.

I hear--and sometimes share--your skepticism about such human/animal tradeoffs. As we argue in a previous post, utilitarianism is indeed to blame for many of these strange results. Still, it could be the best theory around! I'm genuinely unsure what to think here.