Comments
Rethink Priorities’ Welfare Range Estimates
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.
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:
Given these assumptions, we can calculate the welfare gain of a cage-free campaign in DALY-equivalents averted:
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.
What follows are some probability-of-sentience- and rate-of-subjective-experience-adjusted welfare range estimates. These numbers are based on:
| Species | 5th-percentile | 50th-percentile | 95th-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.
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:
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.
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 argued, rejecting 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.
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.
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!”)
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.
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 expanded. We 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.
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.
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.
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.
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.
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.
We discuss the conscious subsystems hypothesis in depth here. The 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.
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.
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.
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.