A blog about biology, self-improvement, and the future

Tag archive: Effective altruism

Also posted on the EA Forum; see there for further critique and discussion.

The Weapon of Openness is an essay published by Arthur Kantrowitz and the Foresight Institute in 1989. In it, Kantrowitz argues that the long-term costs of secrecy in adversarial technology development outweigh the benefits, and that openness (defined as “public access to the information needed for the making of public decisions”) will therefore lead to better technology relative to adversaries and hence greater national security. As a result, more open societies will tend to outperform more secretive societies, and policymakers should tend strongly towards openness even in cases where secrecy is tempting in the short-term.

The Weapon of Openness presents itself as a narrow attack on secrecy in technological development. In the process, however, it makes many arguments which seem to generalise to other domains of societal decision-making, and can hence be viewed as a more general attack on certain kinds of secretiveness1. As such, it seems worth reviewing and reflecting on the arguments in the essay and how they might be integrated with a broader concern for information hazards and the long-term future.

The essay itself is fairly short and worth reading in its entirety, so I’ve tried to keep this fairly brief. Any unattributed blockquotes in the footnotes are from the original text.

Secrecy in technological development

The benefits of secrecy in adversarial technological development are obvious, at least in theory. Barring leaks, infiltration, or outright capture in war, the details of your technology remain opaque to outsiders. With these details obscured, it is much more difficult for adversaries to either copy your technology or design countermeasures against it. If you do really well at secrecy, even the relative power level of your technology remains obscured, which can be useful for game-theoretic reasons2.

The costs of secrecy are more subtle, and easier to miss, but potentially even greater than the benefits. This should sound alarm bells for anyone familiar with the failure modes of naïve consequentialist reasoning.

One major cost is cutting yourself off from the broader scientific and technological discourse, greatly restricting the ability of experts outside the project to either propose new suggestions or point out flaws in your current approach. This is bad enough by itself, but it also makes it much more difficult for project insiders to enlist outside expertise during internal disputes over the direction of the project. The result, says Kantrowitz, is that disputes within secret projects have a much greater tendency to be resolved politically, rather than on the technical merits. That means making decisions that flatter the decision-makers, those they favour and those they want to impress, and avoiding changes of approach that might embarrass those people. This might suffice for relatively simple projects that involve making only incremental improvements on existing technology, but when the project aims for an ambitious leap in capabilities (and hence is likely to involve several false starts and course corrections) it can be crippling3.

This claimed tendency of secret projects to make technical decisions on political grounds hints at Kantrowitz’s second major argument5: that secrecy greatly facilitates corruption. By screening not only the decisions but the decision-making progress from outside scrutiny, secrecy greatly reduces the incentive for decision-makers to make decisions that could be justified to outside scrutinisers. Given the well-known general tendency of humans to respond to selfish incentives, the result is unsurprising: greatly increased toleration of waste, delay and other inefficiencies, up to and including outright corruption in the narrow sense, when these inefficiencies make the lives of decision-makers or those they favour easier, or increase their status (e.g. by increasing their budget)4.

This incentive to corruption is progressive and corrosive, gradually but severely impairing general organisational effectiveness in ways that will obviously impair the effectiveness of the secret project. If the same organisation performs other secret projects in the future, the corrosion will be passed to these successor projects in the form of normalised deviance and generalised institutional decay. Since the corrupted institutions are the very ones responsible for identifying this corruption, and are screened from most or all external accountability, this problem can be very difficult to reverse.

Hence, says Kantrowitz, states that succumb to the temptations of secret technological development may reap some initial gains, but will gradually see these gains eaten away by impaired scientific/technological exchange and accumulating corruption until they are on net far less effective than if they’d stayed open the whole time. The implication of this seems to be that the US and its allies should be tend much more towards openness and less towards secrecy, at least in the technological domain in peacetime6.

Secrecy as a short-term weapon

Finally, Kantrowitz makes the interesting argument that secrecy can be a highly effective short-term weapon, even if it isn’t a viable long-term strategy.

When a normally-open society rapidly increases secrecy as a result of some emergency pressure (typically war) they initially retain the strong epistemic institutions and norms fostered by a culture of openness, and can thus continue to function effectively while reaping the adversarial advantages provided by secrecy. In addition, the pressures of the emergency can provide an initial incentive for good behaviour: “the behavior norms of the group recruited may not tolerate the abuse of secrecy for personal advancement or interagency rivalry.”

As such, groups that previously functioned well in the open can continue to function well (or even better) in secret, at least for some short time. If the emergency persists for a long time, however, or if the secret institutions persist past the emergency that created them, the corroding effects of secrecy – on efficacy and corruption – will begin to take root and grow, eventually and increasingly compromising the functionality of the organisation.

Secrecy may therefore be good tactics, but bad strategy. If true, this would explain how some organisations (most notably the Manhatten Project) produce such impressive achievements while remaining highly secretive, while also explaining why these are exceptions to the general rule.

Speculating about this myself, this seems like an ominous possibility: the gains from secrecy are clearly legible and acquired rapidly, while the costs accrue gradually and in a way difficult for an internal actor to spot. The initial successes justify the continuation of secrecy past the period where it provided the biggest gains, after which the accruing costs of declining institutional health make it increasingly difficult to undo. Those initial successes, if later made public, also serve to provide the organisation with a good reputation and public support, while the organisations declining performance in current events are kept secret. As a result, the organisation’s secrecy could retain both public and private support well past the time at which it begins to be a net impediment to efficacy7.

If this argument is true, it suggests that secrecy should be kept as a rare, short-term weapon in the policy toolbox. Rather than an indispensible tool of state policy, secrecy might then be regarded analogously to a powerful but addictive stimulant: to be used sparingly in emergencies and otherwise avoided as much as possible.

Final thoughts

The Weapon of Openness presents an important-seeming point in a convincing-seeming way. Its arguments jibe with my general understanding of human nature, incentives, and economics. If true, they seem to present an important counterpoint to concerns about info hazards and information security. At the same time, the piece is an essay, not a paper, and goes to relatively little effort to make itself convincing beyond laying out its central vision: Kantrowitz provides few concrete examples and cites even fewer sources. I am, in general, highly suspicious of compelling-seeming arguments presented without evidentiary accompaniment, and I think I should be even more so when those arguments are in support of my own (pro-academic, pro-openness) leanings. So I remain somewhat uncertain as to whether the key thesis of the article is true.

(One point against that thesis that immediately comes to mind is that a great deal of successful technological development in an open society is in fact conducted in secret. Monetised open-source software aside, private companies don’t seem to be in the habit of publicly sharing their product before or during product development. A fuller account of the weapon of openness would need to account for why private companies don’t fail in the way secret government projects are alleged to8.)

If the arguments given in the Weapon of Openness are true, how should those of us primarily concerned with value of the long-term future respond? Long-termists are often sceptical of the value of generalised scientific and technological progress, and in favour of slower, more judicious, differential technological development. The Weapon of Openness suggests this may be a much more difficult needle to thread than it initially seems. We may be sanguine about the slower pace of technological development9, but the corrosive effects of secrecy on norms and institutions would seem to bode poorly for the long-term preservation of good values required for the future to go well.

Insofar as this corrosion is inevitable, we may simply need to accept serious information hazards as part of our narrow path towards a flourishing future, mitigating them as best we can without resorting to secrecy. Insofar as it is not, exploring new ways10 to be secretive about certain things while preserving good institutions and norms might be a very important part of getting us to a good future.

  1. It was, for example, cited in Bostrom’s original information-hazards paper in discussion of reasons one might take a robust anti-secrecy stance. 

  2. Though uncertainty about your power can also be very harmful, if your adversaries conclude you are less powerful than you really are. 

  3. Impediments to the elimination of errors will determine the pace of progress in science as they do in many other matters. It is important here to distinguish between two types of error which I will call ordinary and cherished errors. Ordinary errors can be corrected without embarrassment to powerful people. The elimination of errors which are cherished by powerful people for prestige, political, or financial reasons is an adversary process. In open science this adversary process is conducted in open meetings or in scientific journals. In a secret project it almost inevitably becomes a political battle and the outcome depends on political strength, although the rhetoric will usually employ much scientific jargon.

  4. The other side of the coin is the weakness which secrecy fosters as an instrument of corruption. This is well illustrated in Reagan’s 1982 Executive Order #12356 on National Security (alarmingly tightening secrecy) which states {Sec. 1.6(a)}:

    “In no case shall information be classified in order to conceal violations of law, inefficiency, or administrative error; to prevent embarrassment to a person, organization or agency; to restrain competition; or to prevent or delay the release of information that does not require protection in the interest of national security.”

    This section orders criminals not to conceal their crimes and the inefficient not to conceal their inefficiency. But beyond that it provides an abbreviated guide to the crucial roles of secrecy in the processes whereby power corrupts and absolute power corrupts absolutely. Corruption by secrecy is an important clue to the strength of openness.

  5. As a third argument, Kantrowitz also claims that greater openness can reduce “divisiveness” and hence increase societal unity, further strengthening open societies relative to closed ones. I didn’t find this as well-explained or convincing as his other points so I haven’t discussed it in the main text here. 

  6. We can learn something about the efficiency of secret vs. open programs in peacetime from the objections raised by Adm. Bobby R. Inman, former director of the National Security Agency, to open programs in cryptography. NSA, which is a very large and very secret agency, claimed that open programs conducted by a handful of matheticians around the world, who had no access to NSA secrets, would reveal to other countries that their codes were insecure and that such research might lead to codes that even NSA could not break. These objections exhibit NSA’s assessment that the best secret efforts, that other countries could mount, would miss techniques which would be revealed by even a small open uncoupled program. If this is true for other countries is it not possible that it also applies to us?

  7. Kantrowitz expresses similar thoughts:

    The general belief that there is strength in secrecy rests partially on its short-term successes. If we had entered WWII with a well-developed secrecy system and the corruption which would have developed with time, I am convinced that the results would have been quite different.

  8. There are various possible answers to this I could imagine being true. The first is that private companies are in fact just as vulnerable to the corrosive effects of secrecy as governments are, and that technological progress is much lower than it would be if companies were more open. Assuming arguendo that this is not the case, there are several factors I could imagine being at play:

    • Competition (i.e. the standard answer). Private companies are engaged in much more ferocious competition over much shorter timescales than states are. This provides much stronger incentives for good behaviour even when a project is secret.
    • Selection. Even if private companies are just as vulnerable to the corrosive effects of secrecy as state agencies, the intense short-term competition private firms are exposed to means that those companies with better epistemics at any given time will outcompete those that do not and gain market share. Hence the market as a whole can continue to produce effective technology projects in secret, even as secrecy continuously corrodes individual actors within the market.
    • Short-termism. It’s plausible to me that, with rare exceptions, secret projects in firms are of much shorter duration than in state agencies. If this is the case, it might allow at least some private companies to continuously exploit the short-term benefits of secrecy while avoiding some or all of the long-term costs.
    • Differences in degrees of secrecy. If a government project is secret, it will tend to remain so even once completed, for national security reasons. Conversely, private companies may be less attached to total, indefinite secrecy, particulary given the pro-openness incentives provided by patents. It might also be easier to bring external experts into secret private projects, through NDAs and the like, than it is to get them clearance to consult on secret state ones.
    I don’t yet know enough economics or business studies to be confident in my guesses here, and hopefully someone who knows more can tell me which of these are plausible and which are wrong. 

  9. How true this is depends on how much importance you place on certain kinds of adversarialism: how important you think it is that particular countries (or, more probably, particular kinds of ideologies) retain their competitive advantage over others. If you believe that the kinds of norms that tend to go with an open society (free, democratic, egalitarian, truth-seeking, etc) are important to the good quality of the long-term future you may be loath to surrender one of those societies’ most important competitive advantages. If you doubt the long-term importance of those norms, or their association with openness, this will presumably bother you less. 

  10. I suspect they really will need to be new ways, and not simply old ways with better people. But I as yet know very little about this, and am open to the possibility that solutions already exists about which I know nothing. 

This article is cross-posted from the Wild Animal Initiative website. The original is here. It is also available on the EA Forum here.


In order to determine which conditions provide the best overall quality of life for nonhuman animals, it is important to be able to measure their cumulative welfare experience. The ideal measure of cumulative welfare would be comprehensive, objectively measurable, and easy to transfer across species; however, existing approaches fall far short of this ideal. Recent academic work has suggested that measures of biological ageing could provide a highly promising alternative measure of cumulative welfare, which comes much closer to meeting these ideal goals.

Here, I review the existing empirical support for the use of biomarkers of ageing as a measure of cumulative welfare, discuss the prerequisites of applying the method, and explore a number of important caveats that may limit its applicability. Many of these caveats are particularly applicable to the study of wild-animal welfare, though some may also be important in domesticated contexts.

Overall, despite some important potential weaknesses, biomarkers of ageing are likely to represent an important step forward in the assessment of cumulative animal welfare, which could potentially help resolve some important long-running uncertainties and disputes in the animal-welfare movement. Wild Animal Initiative recommends that both researchers and funders take note of these new techniques, and consider how best they can develop them further or apply them in their own domains of expertise.

We need a good way of measuring cumulative animal welfare

While single experiences can be acutely positive or negative, what matters more from a welfare perspective is the lasting cumulative impact of these experiences.

— Bateson & Pourier 2019 1

Over the course of their life, an animal will undergo various positive and negative experiences. Some of these experiences will primarily affect welfare in an acute, short-lived manner, while others will have significant long-lasting welfare effects. The overall welfare state of an animal will be determined by the cumulative effect of all the experiences they have faced in the course of their life to date.

In order to improve animal welfare, we need to know what affects it. In order to improve animal welfare effectively, we need to know which factors have the greatest total effect on animals’ cumulative wellbeing. Actually measuring cumulative wellbeing, however, is highly challenging, and our existing methods for doing so frequently rely on crude proxies or error-prone anthropomorphic judgements. To make matters worse, the welfare effect of a given experience is frequently complex, species-specific, and non-obvious to humans:

  • Species of fish which are naturally solitary exhibit stress indicators upon overcrowding, while schooling fish become stressed if raised at unnaturally low densities 7.
  • Exposure to environmental ultrasound frequencies that are undetectable to humans causes depression-like symptoms in laboratory rats and mice 21.
  • The flickering of some fluorescent lights at frequencies above the human flicker-fusion rate raises stress-hormone levels in starlings 22.
  • The stress-hormone levels induced in captive-bred lizards by different experimental procedures can differ dramatically from how stressful human observers think the procedures are, with one controversial procedure found to generate much lower levels of stress hormone than the “less-stressful” procedure that replaced it 13.

Which specific stimuli are important for an animal’s welfare is therefore very difficult to predict a priori, and the use of anthropomorphism to make these predictions is fraught with danger.

Even if it is possible to determine that a given experience is acutely positive or negative for an animal, it is not obvious how to convert these acute measurements into measurements of lasting impacts on welfare. Experiences with similar acute effects may have dramatically differences on long-run wellbeing: one stressor may involve temporary pain or result in a temporary spike in stress-hormone levels but have no long-term effects, while another might significantly contribute to long-term stress levels. In some cases, the short- and long-term welfare impacts of an exposure may even be of opposite sign: some acute stresses can be beneficial in the long-term, while the short-term pleasure of sugary food might be outweighed by the long-term pain caused by tooth decay and weight gain 1. Worse, the relationship between short-term and long-term welfare impacts is likely to vary significantly based on species, subspecies, chronological age, past experiences, and individual genetic variation, making the long-term welfare effect of an acute experience even more difficult to predict.

A good measure of cumulative affective experience 23 is therefore vital to the study of animal welfare. However, developing a cumulative-welfare metric which is sensitive, easy-to-measure, captures all or almost all of the relevant effects on welfare, and can be transferred with relative ease across species has proven to be highly challenging 1. Existing measures include chronic physiological markers such as resting stress-hormone levels or bodyweight, acute behavioural measures such as the presence of stereotypies, and high-level behavioural measures such as depressive symptoms or cognitive biases 1; other measures combine multiple different sources of evidence, often alongside acute welfare indicators and/or subjective welfare assessments by a trained practitioner. However, all of these methods have serious drawbacks: physiological measures often lack sensitivity and specificity (i.e. they do not always reliably correlate with affective state), stereotypies are highly species-specific and often difficult to interpret, and the behavioural tests required to measure cognitive biases and mood must be developed and validated independently for each species and often require extensive animal training. Combined measures, meanwhile, rely on difficult-to-test assumptions about how different metrics should be combined and weighted to assess overall cumulative wellbeing.

In the ideal case, the many and varied inputs into cumulative animal welfare would be captured by a single, objectively measurable metric, which could then be used as a single readout of cumulative wellbeing for many different species of animals. In the rest of this article, I will review and discuss the evidence supporting a new, surprisingly good candidate for this “objectively measurable common currency” 1 of wellbeing: biomarkers of the ageing process.

Biological ageing and cumulative animal welfare

As we get older, our bodies decay. In various ways, our cells and tissues progressively accumulate increasing levels of damage and dysregulation, leading at the whole-organism level to a decline in functionality, an increase in mortality, and a decrease in reproductive output 8. These deteriorative processes, which occur in broadly similar ways in many different animal species 9, are collectively known as ageing.

Among researchers studying the biology of ageing, it is well-known that simple chronological age is a less-than-perfect measure of the aspects of ageing we tend to care about. In humans, individuals of the same chronological age often differ substantially, both in how old they appear to others (degree of graying, wrinkles, stooping, etc.) and in their age-related health outcomes 10. It is therefore useful to separate the concepts of “chronological age” (time since conception or birth) and “biological age” (degree of age-related change/deterioration in appearance, health, or functionality) 1011. So-called “biological ageing clocks”, which incorporate a variety of different types of biological data, are an active area of research in the study of human ageing, and the best such clocks can predict health and other outcomes much more accurately than chronological age alone 11.

The biological age of an individual depends on their chronological age, genetic background, and environmental history: depending on their genes and experiences, two individuals of the same chronological age can differ substantially in their biological age. A striking example of this is smoking, which produces a variety of ageing-like symptoms and has recently been shown to substantially increase biological age in young humans 12. However, biological age is affected by a wide variety of chemical, psychological and social stimuli, many of which are also known to have important effects on an individual’s wellbeing. To take just one example, a wide variety of negative experiences (including anxiety, depression, childhood trauma, chronic pain, and various forms of stress) are associated with reduced telomere length in humans, while positive lifestyle factors are associated with longer telomeres 412526. Despite important differences in telomere biology between species, a variety of stress manipulations (including social isolation, sleep disruption, injection of stress hormones, and crowding) have also been found to accelerate telomere attrition in various nonhuman animals, including wild and laboratory mice, chickens, starlings, and various other bird species 4.

In addition to this and other empirical data, there are good theoretical reasons to expect the rate of biological ageing to correlate with the cumulative affective experience of an individual 1. Evolutionarily speaking, the affective state of an experience serves to motivate an animal to seek or avoid similar experiences; hence, affectively negative experiences tend to be those that reduce animal’s fitness, while affectively positive experiences tend to be those that increase it. An important way in which an experience can decrease fitness is by causing or contributing to some sort of damage or dysregulation in the body; hence, experiences which contribute to damage or dysregulation will typically be perceived as aversive to the organism, while those that prevent or reverse damage (or have no effect on damage but are positive for some other reason) will be perceived as attractive. In general, therefore, there is good reason to expect physiologically-damaging experiences to be affectively negative, and vice versa 3, with a similar connection between physiologically-protective experiences and positive affect. Since ageing is characterised by the progressive accumulation of various forms of physiological damage, this suggests that there is good reason to expect the affective valence (i.e. the positivity or negativity) of an experience to also generally correlate with its effect on biological age.

There are, therefore, both empirical data and theoretical arguments suggesting a relationship between cumulative affective experience and ageing. If this relationship exists, the biological age of an individual relative to their chronological age could be used to assess that individual’s cumulative welfare experience up to that point. If further testing bears this out, there are several reasons to expect biological age to be a particularly valuable tool for assessing wellbeing:

  • It is highly general, including all (or almost all) causes of stress and wellbeing experienced by the animal, including those not obvious or perceptible to humans
  • It is cumulative, giving a readout of the total affective history of an individual
  • It is objectively measurable, with well-established biomarkers already known for a number of species
  • Finally, it is plausibly relatively phylogenetically neutral: as ageing is a general phenomenon shared by very many species, whose measurement does not generally rely on anthropomorphic judgements, it can potentially be used to investigate welfare in many different animal groups.

Given these potential advantages, how might we go about actually measuring biological age?

Measuring biological age in non-human animals

In any given species, the ageing process will manifest itself in a plethora of different phenotypes, many of which can be used to try to quantify biological ageing. In humans, a wide variety of biological readouts have been used as biomarkers of ageing, including telomere length and attrition rate, DNA methylation patterns, gene expression profiles, changes in neuroanatomy, proteomic and metabolomic changes, and various composites of clinically relevant symptoms 156. While many of these biomarkers predict health outcomes better than chronological age, they often reflect different aspects of the ageing process and do not always correlate well with one another 15. Combining different measures (e.g. with machine-learning-based prediction tools) can overcome these problems and improve the ability of a biological ageing measure to predict health outcomes 612.

As different biomarkers of ageing track different aspects of the ageing process, they may differ in how well they measure cumulative subjective wellbeing. A good biomarker of cumulative experience should provide a single, continuous, easy-to-measure readout that responds in opposite directions to positive and negative affective experiences in a cumulative and dose-dependent manner 41. Different biomarkers will also differ in the money, expertise and time required to obtain good measurements, and in the ease with which they can be validated in a new context. As nonhuman animals, unlike humans, cannot explicitly self-report their subjective experience, validation of a new potential biomarker’s relationship with wellbeing is much more challenging in these species; as a result, the ease and reliability with which a biomarker can be transferred between species is an additional important consideration when choosing how to measure biological age in an animal-welfare context.

Overall, I would expect more complex and multi-modal measures to provide a more accurate, precise and thorough measure of biological age and so give a better idea of an animal’s cumulative experience. On the other hand, I would expect these complex measures to be more expensive and time-consuming to obtain for each individual, and less transferable between species.

For some widely-used farmed species and experimental model organisms, it may well be worth developing sophisticated species-specific methods of measuring biological age and hence cumulative welfare; however, in contexts where resources are highly limited and/or the number of species of concern is large, cost and transferability concerns are likely to mitigate in favour of simpler, faster, cruder measures 28. Bateson & Pourier 41 suggest telomere length and hippocampal volume as two metrics that are simple, well-defined, and likely to retain validity across a wide variety of vertebrate species; further work may reveal other promising candidates. However, when using such very simple readouts of biological age, it is important to remember that they may give a significantly more partial and inaccurate reading than more sophisticated measures, and to seek to develop such improved measures where feasible.

A concrete example: the welfare effect of crowding on farmed fishes

Many farmed fish are kept at very high densities, in a manner which often appears to be detrimental to their welfare 7. The potential welfare effects of crowding are many and varied, including social stresses, reduced water quality, and increased disease transmission. However, the actual welfare effect of a given level of crowding will vary between fish populations, depending on the level of crowding and water quality they are adapted to cope with, the robustness of their immune systems, and whether they have been vaccinated, among other factors. It would be useful to measure the total cumulative welfare effect of different crowding regimes on different species, and to know the degree to which other interventions such as vaccination mitigate any crowding-induced welfare reduction. Biological age provides an ideal means of addressing these questions.

To apply this method, we would need some sort of biological ageing clock for each of the fish species of interest, as well as some way of keeping track of the chronological age of each individual. Once these two methods are in place for each species, the experiment is simple: simply raise populations of fish at different levels of crowding, sample the biological ages of individuals in each population, then compare the biological ages of chronological-age-matched individuals from different conditions. Those populations exhibiting the highest biological age relative to their chronological age would be taken as experiencing the lowest levels of cumulative welfare.

Three speculative line graphs, each with three lines, indicating the rate of biological age accumulation for fish under low-, medium- and high-density conditions in a hypothetical experiment. The leftmost plot shows hypothetical results for schooling fish and shows slowest accumulation in the mid-density condition, indicating a preference for moderate density for those fish. The middle plot shows results for solitary fish and shows the rate of accumulation increasing progressively with density, showing that these fish are negatively affected by any level of population density above the minimum. Finally, the rightmost plot shows results for solitary fish with vaccination, indicating that vaccination improves welfare at all density levels with an especially strong effect at higher densities. All results are speculative.

Plots showing speculative results of a hypothetical experiment investigating the effect of crowding on fish welfare, indicating that biological age accumulates slowest (indicating highest welfare) at low densities for solitary fish and middling densities for schooling fish, with a positive welfare effect of vaccination. The shape of the curves is arbitrary.

To make up some totally speculative results, these biological ageing data might indicate that naturally solitary fish exhibit significant welfare declines from any level of crowding, while schooling fish have a preferred crowding level and experience reduced welfare above or below this level. These welfare declines may or may not be abrogated by interventions such as vaccination, better water filtration, or changes in feeding schedule. By collapsing all (or most) of an animal’s welfare experience into a single, objective, cumulative measure, many different experiments of this kind could be performed quickly and efficiently, providing a more comprehensive picture of the welfare effects of crowding on fish wellbeing.

While the idea that crowding is bad for fish welfare may not be particularly controversial, this approach would allow researchers to empirically quantify how bad that effect is compared to other aspects of a farmed fish’s life, and assess the efficacy of different interventions (such as vaccination or water oxygenation) for mitigating that welfare impact. The same technique could be applied to help resolve active empirical controversies in the animal welfare movement, such as the relative welfare levels of caged vs cage-free chickens, the relative importance of water oxygenation in the welfare of farmed fishes, or the net welfare impact of predator reintroduction on prey species.

Limitations and caveats

While I was initially sceptical about the applicability of biological ageing markers as measures of animal welfare, I have generally been convinced that this represents a novel and important advance in the field. Nevertheless, there are a number of important limitations or difficulties I anticipate in actually applying the method, which I think it is important to be aware of and mitigate where possible. I have divided these limitations into two categories: contexts where the prerequisites of applying the method may be difficult or expensive to obtain, and contexts where the link between ageing and welfare may be weakened or broken entirely.

Difficulties in application

The need for biological ageing clocks

In order to use biological ageing as a cumulative welfare measure in a given species, we need some kind of biological ageing measure for that species. As discussed above, these range in complexity from simple metrics like telomere length to highly complex machine-learning-based predictors, and there is likely to be a tradeoff between the accuracy and comprehensiveness of a measure on the one hand and its affordability and transferability on the other. As funding is very limited in many animal-welfare contexts, it is likely that simpler, cheaper metrics that can be transferred between species with relatively little validation will be preferred; however, it is important to remember that these may only provide a partial measure of biological age.

The best biomarkers of ageing to use for these experiments will depend on the species being tested and its relationship to other well-validated model systems. In many vertebrate species, and probably most mammals, the markers established in humans and laboratory mice are likely to be the best option. Conversely, in species very distantly related to humans the validity of these markers may be limited: insects, for example, are largely post-mitotic in their adult form (limiting the usefulness of telomere attrition as a biomarker) and have very different neuroanatomy from vertebrates (preventing the use of hippocampal volume). If it is desirable to apply these ageing-based methods to assess welfare in these species, alternative biomarkers (such as accumulation of fluorescent advanced glycation end products in Drosophila 20) will need to be developed and validated as welfare measures, substantially increasing the upfront cost.

Experimental controls

While biological ageing is a promising measure of cumulative welfare, it is important to remember that welfare is not the only thing affecting biological age. Most obviously, chronological age has a very strong effect on biological age, and studies should always compare age-matched individuals when possible. Genetic variability is also an important factor: many species exhibit substantially different lifespans in different populations, and polymorphisms within a population can also have a substantial effect. As a result, ageing-based welfare measures will be most reliable in contexts where all individuals are genetically homogeneous, or at least where there is no systematic difference in genetic composition between different experimental groups. Finally, there should of course be as little systematic difference in environment as possible between the groups being compared, other than whatever exposure is being investigated for its welfare effects.

Of these control requirements, the need for genetic comparability between experimental groups is the most frustrating, as it appears to exclude a lot of factors that are widely thought to be important for animal welfare, particularly in domesticated contexts where animals’ genotypes have been substantially modified by humans through selective breeding. On the face of it, biological-ageing methods seem to be unable to address this, as the groups being compared are not genetically comparable. However, given the importance of genetic effects on welfare in many contexts, any way to overcome this limitation would be very useful, and further investigation on this front seems quite valuable in expectation.

Limitations to validity

Death and other acute events

In many cases, a large portion of the suffering experienced by an animal, whether domesticated or wild, is suspected to take place acutely at the moment of death. If this death is sufficiently gruesome, the suffering so engendered could potentially outweigh the entire net welfare of a life that is otherwise worth living. Despite its importance, however, it seems unlikely that the suffering entailed by dying would be adequately reflected in ageing-based measures of cumulative welfare. For one thing, there would be no opportunity for the negative experiences associated with the animal’s death to be reflected as a subsequent increase in the rate of biological ageing; for another, the extensive physiological damage resulting from death would prevent an accurate postmortem assessment of biological age in many cases. Hence, an important limitation of ageing-based methods of measuring welfare may be their inability to incorporate the affective experience of dying.

A similar concern might apply, albeit to a much lesser extent, to other highly acute exposures, i.e. those with large but short-lived effects on welfare. Many of these will be reflected in ongoing cumulative welfare to some extent (e.g. as physical or psychological trauma in the case of negative events), but it’s not clear to me that the cumulative readout of welfare given by ageing biomarkers will always incorporate them adequately. More research may be needed here.

Differences between juveniles and adults

In both wild and domesticated contexts, many of the animals of greatest concern from a welfare perspective are juveniles who have not yet reached reproductive maturation. These immature individuals are often much more numerous than adults, and have less chance to accumulate positive experiences to outweigh the pain of dying.

For animals that die shortly after birth or hatching, the cumulative welfare of their lives is likely to be dominated by the affective experience of dying, and this experience will not be adequately reflected in biological ageing markers. Ageing-based approaches therefore seem unable to effectively address the welfare of these individuals; on the other hand, it seems likely that any measure of cumulative welfare will run into the same problem.

The situation for individuals that have relatively long lives as juveniles is more complex. In many species, juvenile and adult individuals differ substantially in their biology, and the question of whether juveniles are “ageing” is somewhat fraught. There are certainly dramatic changes taking place over the course of development, some of which could be interpreted as an accumulation of damage: telomeres, for example, shorten rapidly during the period of juvenile growth 18. On the other hand, many clinical biomarkers of ageing do not begin to accumulate until adulthood 19. This means that the applicability of biological-ageing measures to juveniles depends on the specific biomarkers being used, and different markers are likely to be most appropriate for measuring juvenile vs adult wellbeing 29.

Animals very different from humans

The evidence supporting the use of biological ageing markers as measures of cumulative welfare falls into three broad categories: a theoretical, evolutionary argument linking the affective status of an exposure to its effect on ageing via its effect on somatic damage; extensive empirical research in humans associating biological ageing markers with directly reported affective mood (e.g. stress, depression or anxiety) and experiences known to impact mood (e.g. trauma, pain, exercise, and sleep); and more limited empirical research in animals linking these markers to experiences that are both plausibly affectively relevant and associated with other widely-used welfare measures.

The second of these, empirical data on humans, is particularly important, as only humans are capable of directly reporting their affective state to human researchers and so directly confirming a link between biological ageing and welfare. As one moves away from humans in terms of the species under investigation, the less weight can be put on this source of evidence in support of this link, and the more one has to rely on the first and third sources of evidence outlined above. The more distant and dissimilar a species is from those species in which ageing-base techniques have been empirically studied, the more our confidence in those techniques should decrease towards the level of confidence we have in the theoretical argument alone. This poses an issue, since the vast majority of animals on the planet fall into this category.

There are two particular groups of animals for which I think the existing empirical data provides relatively little support for ageing-based welfare measures: invertebrates, and those animals (both vertebrate and invertebrate) whose pattern of lifetime ageing differs substantially from that of humans. The reasons for scepticism in the first case are clear: invertebrates are very different from vertebrates in many aspects of their biology, differ substantially from mammals in terms of their biomarkers of ageing (see above), are very diverse amongst themselves, and are almost totally unstudied as objects of welfare concern. My concerns about the second group, however, are likely to be less clear to someone outside the ageing field, and I will try to briefly explain my reasons below.

Typically, under simple assumptions that are frequently roughly met in real animals, we expect to see mortality progressively increase and fecundity progressively decrease with time after reproductive maturation 16. Many animals, including humans and nearly all common domesticated species, follow this pattern, but this does not apply universally to all animals. Some species, most famously the green hydra 14 and more recently the naked mole rat 15, do not appear to age at all, while some (including various corals, reptiles and amphibians 9) seem to “age in reverse”, exhibiting declining mortality with age until death.

These differences in life history could pose major problems for the use of biological ageing markers as a measure of cumulative welfare in these species. To begin with, it isn’t clear how to define the concept of biological age, let alone measure it, in an animal that does not age in any conventional way. Some subset of conventional biomarkers of ageing may still accumulate with time in these species, but that subset is likely to differ from taxon to taxon depending on what biological methods they have used to overcome the ageing process. Worse, even the theoretical argument in support of ageing-based welfare measures in these species may be greatly weakened: for an animal to exhibit no ageing or even reverse ageing over a prolonged time period, they must be either extremely resistant to somatic damage or have extremely good mechanisms in place to repair that damage, meaning the relationship between damaging (and therefore aversive) experiences and ageing may be largely or entirely severed.

These issues will pose little difficulty to researchers and activists concerned with the welfare of agricultural, experimental or other captive animals, most or all of which, to my knowledge, follow the conventional pattern of ageing exhibited by humans, mice and Drosophila. However, they could turn out to be significant for researchers interested in quantifying and improving the welfare of wild animals, who will inevitably have to tackle the welfare of large numbers of animals very different from humans. The extent to which these “unconventional” life histories are widespread in the natural world is unclear to me at present, and could be an important factor affecting the applicability of these methods in certain contexts.


Finding better methods with which to quantitatively measure the cumulative welfare experience of nonhuman animals would represent a major advance in the study of animal welfare. As a potential route to a better and more objective measure of cumulative welfare, biomarkers of ageing are potentially very promising.

Many of the caveats I outlined above are educated guesses and may well turn out to be circumventable with sufficient thought and care. If they are not, how serious a problem would this pose for the practical usefulness of this method? In the case of domesticated animals, an inability to compare groups differing systematically in their genetics or adequately incorporate the badness of death are all significant limitations, but would still leave us with a tool which could be gainfully applied in many important contexts. For wild animals, the issues are more serious: there are vastly more species for which we would have to develop methods of measuring biological age, it is much harder to perform well-controlled longitudinal experiments, and many more of the animals of concern fall into categories for which I am more sceptical about the theoretical applicability of the method. A further concern is that measuring chronological age accurately is often difficult for wild animals, potentially undermining one of the foundations of ageing-based welfare measures. Nevertheless, of all the methods we might think of for measuring the cumulative welfare of wild animals, biomarkers of ageing seem to be among the least hopeless, and among those most worth developing further in the hopes of overcoming some of these pervasive issues.

Overall, I am currently very optimistic about the value of applying these methods in domesticated contexts, and cautiously optimistic about applying them to wild ecosystems. I would recommend that researchers interested in the welfare of either domesticated or wild animals take note of these techniques and consider their applicability in their own domains, and that funders in this space seriously consider funding their further development and application to new contexts. On a meta level, I think the surprisingly strong applicability of techniques from the biology of ageing to animal welfare science should encourage us all to look for innovative, unexpected and interdisciplinary ways to help nonhuman animals: as is often the case in science, the crucial insights and discoveries may not be at all where we expect them to be.


This report was funded by the Wild Animal Initiative, and many WAI researchers (including Luke Hecht, Michelle Graham, Hollis Howe and Jane Capozzelli) contributed their time to reviewing and suggesting improvements to the draft. Prof. Melissa Bateson, the author of the key sources for the report, also generously read the draft and provided crucial feedback.

  1. Bateson, M., & Poirier, C. (2019). Can biomarkers of biological age be used to assess cumulative lifetime experience? Anim. Welf. 28: 41-56. doi: 10.7120/09627286.28.1.041 

  2. Medawar, P. B.(1952). An unsolved problem of biology. London: HK Lewis & Co 

  3. One major category of negative experiences which do not appear to be directly damaging (but are nevertheless important to an animal’s wellbeing) are social experiences. For many reasons, an animal’s social status and relationships are very important to their survival and reproduction, but are not typically the direct cause of bodily damage. However, negative social experiences (low status, rejection by mates, ostracism) do give rise to significant levels of stress in many species, and this stress is well known to be physiologically damaging. 

  4. Bateson, M. (2016). Cumulative stress in research animals: Telomere attrition as a biomarker in a welfare context?. BioEssays 38 (2): 201-212. 

  5. Belsky, D. W., et al. (2017). Eleven telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: do they measure the same thing? American Journal of Epidemiology 187 (6): 1220-1230. 

  6. Cole, J. H. et al. (2018). Brain age predicts mortality. Molecular Psychiatry 23 (5): 1385-1392. 

  7. Ashley, P. J.(2007). Fish welfare: current issues in aquaculture. Applied Animal Behaviour Science 104 (3-4): 199-235. 

  8. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The Hallmarks of Aging. Cell 153 (6): 1194-1217. 

  9. Jones, O. R., et al. (2014). Diversity of ageing across the tree of life. Nature 505 (7482): 169. 

  10. Jia, L., Zhang, W., & Chen, X. (2017). Common methods of biological age estimation. Clinical Interventions in Aging 12: 759-772. 

  11. Horvath, S., & Raj, K. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics 19 (6): 371-384. 

  12. Mamoshina, P., et al. (2019). Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers. Scientific Reports 9: 142. 

  13. Langkilde, T., & Shine, R. (2006). How much stress do researchers inflict on their study animals? A case study using a scincid lizard, Eulamprus heatwolei. Journal of Experimental Biology 209 (6): 1035-1043. 

  14. Dańko, M. J., Kozłowski, J., & Schaible, R. (2015). Unraveling the non-senescence phenomenon in Hydra. Journal of Theoretical Biology 382: 137-149. 

  15. Ruby, J. G., Smith, M., & Buffenstein, R. (2018). Naked mole-rat mortality rates defy Gompertzian laws by not increasing with age. eLife 7:e31157. 

  16. Charlesworth, B. (2000). Fisher, Medawar, Hamilton and the evolution of aging. Genetics 156 (3): 927-931. 

  17. Williams, G. C.(1957). Pleiotropy, natural selection, and the evolution of senescence. Evolution 11(4): 398-411. 

  18. Zeichner, S. L., et al. (1999). Rapid telomere shortening in children. Blood 93 (9): 2824-2830. 

  19. Hollingsworth, J. W., Hashizume, A., & Jablon, S. (1965). Correlations between tests of aging in Hiroshima subjects—an attempt to define “physiologic age”. Yale J Biol Med 38 (1): 11-26. 

  20. Jacobson, J., et al. (2010). Biomarkers of aging in Drosophila. Aging Cell 9 (4): 466-477. 

  21. Morozova, A., et al. (2016) Ultrasound of alternating frequencies and variable emotional impact evokes depressive syndrome in mice and rats. Progress in Neuropsychopharmacology and Biological Psychiatry 68: 52-63. 

  22. Smith, E.L., et al. (2005) Effect of repetitive visual stimuli on behaviour and plasma corticosterone of European starlings. Animal Biology 55: 245-258. 

  23. Throughout this piece I am assuming that welfare is synonymous with affect, i.e. with subjectively positive and negative experiences. This is not an uncontroversial position, and there are many in the animal-welfare field who prefer a concept of welfare which incorporates both affective wellbeing and physical health, or even avoids questions of subjective experience altogether 24. While I’m sceptical about this, it’s worth noting that measures of cumulative wellbeing are also essential when using such a definition, so it doesn’t necessarily change many of the conclusions of this report. 

  24. Dawkins, M. S.(2017) Animal welfare with and without consciousness. Journal of Zoology 301: 1-10. 

  25. Pepper, G.V., et al. (2018) Telomeres as integrative markers of exposure to stress and aversity: a systematic review and meta-analysis. Royal Society Open Science 5: 180744. 

  26. It’s important to stress here that the great majority of research into the association between lifestyle and telomere length is cross-sectional and correlational: individuals with greater exposure to adversity have shorter telomeres. This finding is robust, but not causal: it doesn’t demonstrate that these adverse experiences cause shorter telomeres. Evidence from longitudinal studies, for example on smoking 27, tend to find a much smaller effect; more on this later. 

  27. Bateson, M. et al. (2019). Smoking does not accelerate leucocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts. Royal Society Open Science 6: 190420. 

  28. Melissa Bateson (pers. comm.) points out that a “complex and multi-modal” measure of biological ageing does not necessarily entail expensive and expertise-heavy multi-omics methods. An alternative approach, which captures the goal of assessing many different aspects of an animal’s biology while being much cheaper, is a “biomarker panel” approach, in which a large number of different easy-to-measure features that are associated with poor health in old age are collected from the same individuals. These could then be used as input to a machine-learning model which has been trained to use them to predict biological age. This approach has many advantages, including potentially high reliability and low cost. However, it seems to require a fairly high level of interaction with the animal (at least in humans and lab animals, many of the markers used are often behavioural), making it difficult to use in wild contexts, and is probably quite species-specific. Both of these issues could be overcome to some extent through the choice of biomarkers used. 

  29. In the case of telomere attrition, juveniles may actually be more appropriate subjects than adults (M. Bateson, pers. comm.): the base rate of telomere attrition is much higher, which makes changes in the attrition rate due to welfare factors easier to detect. It is also much easier, quicker and cheaper to do longitudinal studies on juveniles, avoiding many of the interpretation issues associated with cross-sectional studies.