Hi Vasco, thanks for this follow up. I believe we cannot rule out the possibilities you mentioned. That is, the answer could be yes to both questions. Even with humans, there is high variability in preference for intense but short aversiveness, as compared to moderate but longer aversion.
One solution we have been adopting lately is to simply ‘add up’ times in the different categories (with no weighting or conversion). Since the ‘boundaries between intensity categories’ are acknowledged to be uncertain (i.e., we assign probabilities to each intensity category), adding up the time spent in different intensities follows naturally from this uncertainty. For instance, hours in Hurtful, Disabling, and Excruciating pain can be added together to report total time in ‘moderate to intense pain,’ or hours in Disabling, and Excruciating pain can be added together to report total time in ‘intense pain’.
cynthiaschuck
Hi Vasco, that’s a good question, but I do not think we can say with certainty that the ratio is preserved across species. The ratio may differ across species for a number of reasons, such as species-specific differences in the subjective perception of time. In fact, the relationship between the aversiveness of pain and its intensity may itself change dynamically for a ‘same’ species, depending on the duration of the experience. If on the one hand it’s true that the ‘criteria’ defining each intensity category is universal (the same for all species), we cannot know for sure whether Excruciating pain in a shrimp is the same as Excruciating pain in a human being.
Thank you for this thought-provoking article, Vasco! In situations where we have clear, certain benefits in one area (here, knowing that use of slower-growing chicken breeds clearly reduces suffering for those chickens, even if their lives are longer) and a high degree of uncertainty on multiple dimensions in another (here the welfare consequences for arthropods), my inclination is that it’s often more effective to focus on the suffering we are confident we can alleviate, and avoid the non-negligible risk of relevant unintended negative consequences in the uncertain case (while simultaneously promoting further research on the latter effects).
Hi CB, thanks a lot for your comment, I think it represents a main concern of many people. I’ll break my thoughts in two parts
(1) AI use in shrimp farming and similar situations.
In this case, I understand what AI-monitoring did was to enable farmers to optimize feed use enormously (shrimp grew larger, mortality was reduced, and feed was not wasted), as well as water quality monitoring. This could be seen as negative for welfare, as it facilitates farming in high stocking densities, makes shrimp farming more profitable and could reduce prices, though this price effect is complex since the same AI technologies will likely make alternative proteins cheaper too, making the net effect on consumption less certain.However, consider the actual conditions shrimp face. Without AI, feed distribution was uneven, leading to competition, stress, malnutrition and starvation for a large fraction of animals (mortality without AI was higher), as well as longer exposure times to poor water quality, and higher incidence of toxicities (hence respiratory distress, gill damage, skin damage) that come associated with it. This leads to suffering and higher mortality rates. So it’s possible (though this should be measured) that even in higher-density environments, AI use can maintain better welfare than lower-density farms with poor feed and water quality management. Importantly , if shrimp feed relies on fishmeal and fish oil, optimizing feed reduces the number of wild fish needed, so each pound of shrimp has a smaller welfare footprint in terms of wild fish captures.
The industry trajectory also matters. Aquaculture is already moving toward higher-density and intensified farming with or without AI. So I believe the relevant comparison isn’t between AI farming and a low-density or extensive scenario, but between AI-farming and conventional (intensive) high-density farming without AI.
(2) On AI leading to greater income/prosperity, potentially increasing consumption of animal foods.
I see greater incomes and prosperity as extremely positive to reduce human suffering, but animal suffering as well. While rising incomes historically increased meat consumption, the relationship is not linear, in that as societies become more prosperous (on top of being an extraordinary thing in itself), they often can afford being more concerned with environmental and ethical issues. It’s particularly in wealthier nations that we see a trend towards reduced meat consumption, stronger welfare legislation, increased interest in plant-based alternatives, and the means needed for the development of innovations like cultivated meat and other substitutes of animal protein. And again, the same technologies making animal farming more efficient are simultaneously making alternatives more competitive and affordable. I believe that the key isn’t if AI increases income (something to be celebrated), but how to channel greater incomes toward ethical food systems.
Hi Vasco! Yes, it would be very interesting to collaborate on this. Right now we do not have the resources (in terms of people, and time) to do it ourselves, but we would gladly collaborate with anyone leading this effort. One possibility would be running WTP tests with people, from various demographics, to determine the extent to which they would pay to trade Disabling Pain by Hurtful Pain, or Excruciating Pain by Disabling Pain and so on (having the understanding of these intensities well explained and calibrated with examples, past experiences, etc, within a clear set of criteria). This would help understand some level of equivalence (though from a “human” perspective) between the categories, but also generate rational WTP numbers for any estimates of Cumulative Pain (e.g., if cage-free campaigns avert X hours of Disabling Pain per hen, and people are willing to pay on average 1-10 dollars to avert one hour of this pain, you can in theory calculate the extent to which they would pay more for improved welfare, run CBA analysis, apply these to funding decisions, etc)
By not tolerated we mean that Excruciating pain can’t be sustained for long (e.g., hours, as opposed to minutes) without neurological shutdown. It will tend to overrides an organism’s ability to function or respond coherently, with even powerful opioids providing minimal relief, or desperate attempts to escape pain even at the risk of death . Examples would include severe burning in large areas of the body, dismemberment, or extreme torture.
Examiing empirically trade-offs with milder pain could be interesting, but may be challenging given the nature of truly excruciating pain . Perhaps comparing to severe but more sustained pain (like bone fractures)?
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This is indeed a legitimate concern. We do not have accurate information on the distribution of BCC-approved breeds used in the committments made so far, but I believe that organizations working on and monitoring the committments (possibly the Humane League and CIWF, which publishes the Chicken Track), are likely to have this information. From statements of company’s representatives, it seems that the Hubbard breeds are prevailing in Europe, see e.g. this statement: “In Europe, where the issue of breed is more advanced than in the U.S., the Hubbard JA757, JA787 and Redbro are the main breeds used for the BCC market”.
It may be interesting to know though that an experiment published recently, which used Avian Ranger Classic (the fastest growing of the Aviagen breeds), consistently showed various welfare benefits associated with this slower-growing breed, even when it was raised in higher stocking densities than the conventional fast-growing breeds (showing that it is better to be this slower-growing breed in high densities than a fast-growing breed in low densities).If also useful: our estimates are very conservative, and represent what we estimate to be the minimum time in pain averted with the reform. In this chapter (table 1), there is a list of welfare harms excluded from analysis, which if considered should increase further the estimated benefits of the transition to slower growing breeds.
Thanks! I will revise the post to make sure it is clearer!
This is an interesting question, relating to the evolutionary role of pain going beyond protection of the individual survival and immediate reproduction (or that of kin), but affecting the group as a whole. As you said, debilitating pain (i.e., pain that prevents individuals to function normally) for a solitary species may also have different moral implications than for a social species even if the unpleasantness of the pain experience is the same.
My impression is that it would be difficult to determine differences in pain aversiveness among species with different degrees of sociality given the confounders present. For example, there might be differences in hedonic and cognitive capacity between social and non-social species (see eg here), hence differences in aversiveness to pain for reasons other than the context of the painful experience.
Thank you for the detailed comments, it is really nice to see you were so thorough with the text and the studies we cite, these are good points!
If we understand you well, with a few exceptions (as in the case of the time trade-off study, [21]), what you mention is that the superlinearity observed could be a by-product of participants of the studies interpreting the designed scales of intensity (even the numerical ones) as not equidistant, hence a non-equidistant plot of aversiveness could, for example, be observed if the relationship between intensity and aversiveness was in fact linear.
My impression, however, is that we are referring to different things when we say pain intensity (and this is our fault for not being clearer on the text, we will make the distinction clearer). My understanding is that ‘pain intensity’ is often used simply as a synonym for aversiveness or unpleasantness, as opposed to physical intensity of stimuli/pain signals.
As I see it, assessments of pain intensity (unpleasantness) are typically made on what can only be understood as an ordinal scale. Whether evidence on pain unpleasantness is collected with verbal descriptors or numerical descriptors, these unpleasantness scales (i.e. the intensity scales used) can only be interpreted as ordinal exactly because we do not know what type of understanding of the scale study participants have. Even though some authors use the data collected (e.g., pain intensity/unpleasantness ratings in scales from 1-10 or 1-100) and calculate things such as areas under the curve for plots of ‘intensity’ (meant as unpleasantness) ratings x duration, the data is still ordinal for practical purposes and unsuitable for these operations.
So the effort in this work was to try and see if it would be possible to make such a conversion of pain unpleasantness (from ordinal to ratio scale), and determine the distance among the categories of unpleasantness on a ratio scale. Thus, in the case of the psychophysics studies, I do not see three parameters: intensity of physical stimuli, pain intensity and pain aversiveness, but only two: intensity of physical stimuli and aversiveness (we will correct the text where we mention that the relationship is exponential).
On the availability of studies, unfortunately, all of them were riddled with limitations. Although designs such as the time trade-off approach (used in study [21]) are much better, to our surprise the literature is extremely scarce. So no good studies, and no recent studies either, as far as we are aware. This is why we currently prefer a disaggregated approach, as we do not see how, with the evidence at hand, it is possible to estimate the equivalence weights with any precision .
Yes, I believe the salience of more intense experiences is disproportionally greater, hence the possibly disproportionally stronger memory trace.
On chronic x acute pains, this is our general impression too. With chronic pain there is the possibility of sensitization over time and multiple additional longer-term consequences.
On memory differences with other species, that is a very interesting question, one for which there is not much research available as far as I am aware. For example, observations of the Peak-End rule mentioned in the text are widespread in humans, but so far there has been only one experiment testing it in animals (dairy cows). That said, at least in vertebrates intense pain has been observed to lead to greater levels of fearfulness, reduced resilience, sensitization, among others (so if it happens early in life, the effects of intense pain are likely much more pervasive). So at least in vertebrates, either memory is present, or intense pain leads to a reconfiguration of pain coping mechanisms.
Sure, we would be glad to review a project like this. Coincidentally, today I came across an EA post where four people from RP articulated their reasons for choosing to present, or not to present, sentience weights for invertebrates. Our rationale for not providing intensity equivalence weights are very much aligned with the views of Jason K, particularly the notion that any weights we use would be overemphasized and reduce our credibility with potential collaborators. That said, we are not advocating for giving up on this area, it is just that we think we are not there yet.
Thanks for your nice words about our work :) . Yes, I see it can be frustrating to have estimates disaggregated (it is very much for us too), and that it can reduce the use and impact of the work. At this moment though we feel it is important to have a solid evidence-based model to quantify animal suffering. That is, a model that is very robust to scrutiny by academics (so they are more likely to adopt it) and by the industry, one in which all estimates can be justified thoroughly.Traction in the academic community is important because as a small team, we would be unable to analyse all situations of animal suffering (in farming context, research, etc) by ourselves, so ideally academics should adopt it too to enable increasing the coverage of the analyses substantially. Robustness against criticisms by the industry is also important to ensure the credibility of this new type of evidence, as used by advocates, in this early stage. So while we can justify well time in four intensities of suffering, knowledge is not yet available for us to do the same regarding equivalence weights. That said, we have been using summaries of the estimates like “there is a decrease of about 60% of the time in pain for every hen raised in an aviary instead of a cage”. Will try to add summaries like this in the forthcoming work, thanks!
Thanks! I like the idea of a ‘minimum transparency rating’ policy in the supply chain.
You are absolutely right. From my personal experience in Spain, animal welfare audits are often announced to farmers weeks in advance, so even if they happen (often they only happen in the paper, without the actual visit) farmers have time to correct whatever needs to be corrected, just for the visit. Hence the idea of creating mechanisms to enable auditing by independent parties (other than the companies’ own vets or governmental auditors). There is also potential for corruption here, but if there is an organization behind certifing auditors, creating standards for how these audits should happen, or simply reporting the willingness of companies to adhere to these standards (e.g., through a transparency index or something of the sort), the risk could be reduced somehow.
It was a very nice surprise to see the Cumulative Pain method used here to guide this debate. The ‘value tag’ that is already placed on different causes by resource allocation decisions is often based on personal experiences, specialist opinion, tradition or often even empathic guesses. Yet the criteria underlying these decisions are seldom explicit or open to scrutiny. This is one of the things we wanted to help change with the method: to help inform decisions based on an atomized process, where every assumption is explicit, and can help debate in a more objective way, based on estimates and evidence about the actual hedonic experiences of the subjects/targets of the interventions (as is the case here!). The sensitivity app (https://www.pain-track.org/hens) was also created to ensure that anyone disagreeing with our assumptions/estimates could use their own.
Re. the comments:
like Michael, for an experience that someone is desperate to stop immediately, similar to “slicing into my leg with a hot, sharp live wire”, we would attribute a higher likelihood of Excruciating pain (as this does not seem as something that can be tolerated for a long time). We have not yet estimated any mathematical equivalence between the intensity categories for the reasons we discuss in the FAQ (https://welfarefootprint.org/frequently-asked-questions/), but Excruciating pain is likely much much worse (something similar to an exponential—rather than linear—function) than Disabling pain (we are working on a draft on this issue that should be released at some point this year).
You are right that Disabling pain possibly accepts a range of intensities, all fitting the Disabling definition (continuously distressing/disruptive, cannot be ignored, prevents positive welfare, reduces attention to other ongoing stimuli). We have chosen not to create more categories to ensure both resolution and tractability (given the scarcity of evidence typically available from animal studies), but subdivision of the intensity categories is certainly possible and desirable should evidence be available that enables it.
It is true that distressed behaviors can be present in the absence of suffering, but this is unlikely to be the case when these behaviors are conditional and proportional to the degree of aversiveness of the situation, and observed along with various other independent indicators of distress. For example, pre-laying behavior is very different for hens with or without access to a proper nest and excessive pacing (seen in the latter case) is associated with other aversive situations. Other indicators include the observation of inelastic demand for a nest (hens paying increasingly higher costs to get to a nest), the overcoming of aversive obstacles (e.g., narrow gaps, long walking distances, dominant/unfamiliar birds), and vocalizations typically associated with frustration in other contexts. We also need to consider the evolutionary importance of psychological distress in the absence of important resources for survival and reproduction. In the case of physical pain, it is the unpleasantness of the pain experience that protects individuals from provoking further tissue damage when the eliciting stimuli is no longer present. Negative affective states of a more diffuse nature (psychological pain) are similarly strongly selected to ensure that individuals do not give up seeking important resources, as is the case of a nest, mates and offspring.
The notion that a hen under higher levels of feed deprivation would work less hard for access to food is a thoughtful one. However, the levels of feed restriction applied where unlikely to deprive the hens from the energy needed to access food. More importantly, evidence has shown the opposite: within the time periods of food deprivation used in experiments (e.g. up to 50-60 hours) hens deprived of food for a longer period work harder than hens deprived of food for a shorter period (e.g. https://www.sciencedirect.com/science/article/abs/pii/0168159190900156)
On the extent to which it is possible to compare physical and psychological pain, or use similar criteria to define their intensity, this is an interesting discussion and one that we would also like to work more on. For example, it may be hard to identify, in the short term, reliable biological markers of psychological suffering, and the same is true for humans. Consider for example the lack of changes in adrenocortical function in prisoners held 10 days in solitary confinement (“Adrenocortical function, as measured by plasma cortisol levels, indicated that solitary confinement was not more stressful than normal institutional life”, https://psycnet.apa.org/doiLanding?doi=10.1037%2Fh0081866), despite the severity of the experience as reported by those who have undergone it.
Thanks for the interesting discussion!
Thank you for your thoughtful analysis. This is helpful for us to understand areas that must be improved or made clearer. This is especially important as we will be soon publishing the full Welfare Footprint of the Egg (various systems), where we analyse over 120 experiences (diseases, injuries, deprivations, imbalances – nearly everything we could identify), in different housing systems, from birth to slaughter (more info here), for layers and breeders.
On your specific points on the direction of the results: there are various ways in which the existing analysis was conservative (i.e. favored caged systems). For example, in estimating the prevalence of keel fractures and other ailments in cage-free systems, we considered prevalences as they were reported, which typically was in the first few cycles of experience with cages. Evidence indicates that these prevalence rates go down as management experience increases (examples in the Prevalence Chapter), yet we preferred not to make that assumption and use the data as it was. Also, we did not take into account positive welfare (greater in cage-free settings) and more diffuse experiences, like fear, helplessness and boredom (greater in cages). Nor did we consider the flow-through effects of frustrations from behavioural deprivations beyond the period corresponding to the time budget of engagement, or practices like induced-molting (more likely in cages), or the longer cycles of caged-hens (with end disproportionally worse at the end)
Importantly, there is substantial evidence indicating that Pain is more intense (and healing delayed), even for the same injury/disease, in cages than in cage-free systems. We did not consider these modulatory effects, but they are likely present. That said, we’ll look deeper into the references you mentioned.
Unfortunately, many existing comparisons of cage and cage-free cite the CSES studies, to which the analysis from Fulton are part of. These studies were funded by the American Egg Board and facilitated by another industry-funded organization focused on building consumer confidence and maintaining the industry’s viability. While management in the caged systems was good and based on decades of experience, the cage-free systems were implemented for the first time, and did not adopt nearly any of the good management practices required. For example, during the laying phase, birds in the aviary were confined for many weeks before accessing the floor litter (something that makes injurious pecking much more likely). Also, insufficient space allowance and perch space in the aviary led to crowding, collisions, and failed landings, likely contributing to the high rate of keel injury and mortality. The authors themselves declared publicly they were still learning about what to do in the aviary systems during the research, which led to many failures. These design and management failures likely substantially inflated the negative outcomes in CF systems, including mortality (mortality data is also inconsistently reported in their publications, with some mortality—e.g. during placement—apparently excluded from caged systems).
Some info that may be useful:
Keel injuries: so far, the most consensual explanation is the earlier onset of laying, before the keel bone is ossified. This affects both cage and cage-free systems.
Nest deprivation: the justification for the ranges of durations and probabilities of distress intensities is provided in this chapter. These inferences are grounded in multiple lines of evidence (reviewed in the chapter). As such, they remain open to revision if new data or alternative interpretations of the evidence emerge. If you believe that the evidence supports a different intensity, please share your reasoning. This kind of scientific exchange is precisely what helps us approach values that are likely closer to reality.
Mortality: the lack of differences in mortality is seen in many datasets, with and without beak trimming (the control mentioned in the 2021 meta-analysis is for Week’s data, not for the meta-analysis data). Industry data also shows that the best cage free systems have lower mortality than the best caged systems.
More generally, both in the prior analysis, and in the forthcoming book, a major issue is data scarcity. Therefore, we inevitably rely on estimating uncertainty ranges for parameters like duration and prevalence. Inter-rater agreements should be made available together with future estimates, but what we have seen so far shows reasonable levels of agreement among WFI and independent academic raters/estimators.
As we build more comprehensive analyses, we’d be keen to have our estimates scrutinized as you did, so thank you!