I feel less confident in this specific approach — I think downstream effects are just often very contingent. It’s easy to imagine scenarios like: eliminate very bad disease 1, population goes up, now very bad disease 2 emerges or is transmitted from some other population because contact occurs, etc.
I suppose this could be less of an issue for the very worst diseases/issues, etc., but I suspect those by their nature are less common (e.g. could be self-limiting, etc), and it seems like the method would only work insofar as changing population levels, etc. doesn’t increase the risk of novel worse things, which seems only possible for the very worst things.
That being said I am very sympathetic to just trying more things, at least for some animals, especially vaccinations.
My argument is that while such outcomes are possible, the outcomes of the opposite effect are also possible, and often there just doesn’t seem reason to assume one to be more likely than the other (this is in simplest terms what people mean when they talk about cluelessness). Given that, I think it makes sense to assume the effects which we cannot predict are on expectation neutral (most likely, they will be a mix of good and bad) and then pursue it if the effects we can predict are clearly good.
So for example, treating chronic wasting disease in deer will surely affect populations of very small mammals, reptiles, and insects in subtle but nonetheless large ways, due to the numbers of these species. But it’s going to be a mix of benefits and harms, which are incredibly hard to predict. The benefit for the deer, however, is really certain, so if it can be done without great environmental disruption, then I’d say the expected value is pretty good.
I’ve been thinking about this approach since last year, and haven’t had time to prioritize it to do detailed work on a framework, but I have some initial thoughts. I think you’re right that, if you’re comfortable with that sort of cluelessness, this kind of thing is relatively safe to do (although as @Michael St Jules 🔸 notes, I’d also want to do some proper population modeling in a highly studied ecosystem to get some grounding for the idea).
But I think you can actually do better than focusing on only the very worst diseases depending on population parameters. For example, in populations that are top-down regulated (i.e., the population size is held under the carrying capacity of the resource by an external factor), you would not expect increases in starvation as a result of removing a disease (caveat: if that disease *is* the top down regulator, than you would have a problem—which unfortunately is the case in many CWD contexts). So then the disease doesn’t need to be worse than both starvation and predation, say, but rather just worse than predation. The population size would equilibriate somewhere a bit higher, but the top-down regulation creates a buffer between population size and resource carrying capacity, and at high enough predation pressures you might reasonably expect almost no population increase.
So I think in an ideal case, you’d identify (1) a high suffering disease that (2) affects a population primarily controlled by intense predation pressure in (3) a predator that mainly eats the target population (so the increases in predator population sizes don’t affect other animals, who aren’t having a disease treated and for whom this would just represent an increase in suffering).
Of course, if you have a population with high predation pressure, the target population probably dies very quickly after getting the disease, so the suffering caused by the disease might not be very long in duration. But if its a really awful disease that could still be a lot of suffering.
I don’t think anyone’s done a scan of the literature for diseases with these properties, and I doubt you’d easily find a perfect case—most populations are a mixture of top-down and bottom-up regulation. But I also think that probably my few hours of playing around with these ideas on the side of my other work are not likely to be the final word on the question :) so I’m optimistic someone spending a lot more time with this could identify other “ecological profiles” of diseases that make them “safer” in indirect effects terms to work on than others (I think there’s some things to say about bottom-up regulated populations as well, for example—probably there you would want a disease that is a lot worse than starvation).
Hey Mal, this is a great point, I completely agree. The disease doesn’t have to be worse than all possible ways of dying if you know that the counterfactual is likely to be a particular mid-intensity harm. Although the welfare gains should still be significant in order to justify the ecological risks.
Thank you all for the very interesting discussion.
I think addressing the greatest sources of suffering is a promising approach to robustly increase welfare. However, I believe the focus should be on the greatest sources of suffering in the ecosystem, not in any given population, such that effects on non-target organisms can be neglected. Electrically stunning farmed shrimps arguably addresses one of the greatest sources of suffering of farmed shrimps, and the ratio between its effects on target and non-target organisms is much larger than for the vast majority of interventions, but I still do not know whether it increases or decreases welfare (even in expectation) due to potentially dominant effects on soil animals and microorganisms.
I expect the greatest sources of suffering in the ecosystem to be found in the organisms accounting for the most suffering in the ecosystem. However, I would say much more research on comparing welfare across species is needed to identify such organisms. I can see them being vertebrates, invertebrates, trees, or microorganisms.
I worry very specific unrealistic conditions will be needed to ensure the effects on non-target organisms can be neglected if it is not known which organisms account for the most suffering in the ecosystem. So I would prioritise research on comparing welfare across species over mapping sources of suffering in ecosystems.
I feel less confident in this specific approach — I think downstream effects are just often very contingent. It’s easy to imagine scenarios like: eliminate very bad disease 1, population goes up, now very bad disease 2 emerges or is transmitted from some other population because contact occurs, etc.
I suppose this could be less of an issue for the very worst diseases/issues, etc., but I suspect those by their nature are less common (e.g. could be self-limiting, etc), and it seems like the method would only work insofar as changing population levels, etc. doesn’t increase the risk of novel worse things, which seems only possible for the very worst things.
That being said I am very sympathetic to just trying more things, at least for some animals, especially vaccinations.
Thanks for this point!
My argument is that while such outcomes are possible, the outcomes of the opposite effect are also possible, and often there just doesn’t seem reason to assume one to be more likely than the other (this is in simplest terms what people mean when they talk about cluelessness). Given that, I think it makes sense to assume the effects which we cannot predict are on expectation neutral (most likely, they will be a mix of good and bad) and then pursue it if the effects we can predict are clearly good.
So for example, treating chronic wasting disease in deer will surely affect populations of very small mammals, reptiles, and insects in subtle but nonetheless large ways, due to the numbers of these species. But it’s going to be a mix of benefits and harms, which are incredibly hard to predict. The benefit for the deer, however, is really certain, so if it can be done without great environmental disruption, then I’d say the expected value is pretty good.
I’ve been thinking about this approach since last year, and haven’t had time to prioritize it to do detailed work on a framework, but I have some initial thoughts. I think you’re right that, if you’re comfortable with that sort of cluelessness, this kind of thing is relatively safe to do (although as @Michael St Jules 🔸 notes, I’d also want to do some proper population modeling in a highly studied ecosystem to get some grounding for the idea).
But I think you can actually do better than focusing on only the very worst diseases depending on population parameters. For example, in populations that are top-down regulated (i.e., the population size is held under the carrying capacity of the resource by an external factor), you would not expect increases in starvation as a result of removing a disease (caveat: if that disease *is* the top down regulator, than you would have a problem—which unfortunately is the case in many CWD contexts). So then the disease doesn’t need to be worse than both starvation and predation, say, but rather just worse than predation. The population size would equilibriate somewhere a bit higher, but the top-down regulation creates a buffer between population size and resource carrying capacity, and at high enough predation pressures you might reasonably expect almost no population increase.
So I think in an ideal case, you’d identify (1) a high suffering disease that (2) affects a population primarily controlled by intense predation pressure in (3) a predator that mainly eats the target population (so the increases in predator population sizes don’t affect other animals, who aren’t having a disease treated and for whom this would just represent an increase in suffering).
Of course, if you have a population with high predation pressure, the target population probably dies very quickly after getting the disease, so the suffering caused by the disease might not be very long in duration. But if its a really awful disease that could still be a lot of suffering.
I don’t think anyone’s done a scan of the literature for diseases with these properties, and I doubt you’d easily find a perfect case—most populations are a mixture of top-down and bottom-up regulation. But I also think that probably my few hours of playing around with these ideas on the side of my other work are not likely to be the final word on the question :) so I’m optimistic someone spending a lot more time with this could identify other “ecological profiles” of diseases that make them “safer” in indirect effects terms to work on than others (I think there’s some things to say about bottom-up regulated populations as well, for example—probably there you would want a disease that is a lot worse than starvation).
Hey Mal, this is a great point, I completely agree. The disease doesn’t have to be worse than all possible ways of dying if you know that the counterfactual is likely to be a particular mid-intensity harm. Although the welfare gains should still be significant in order to justify the ecological risks.
Thank you all for the very interesting discussion.
I think addressing the greatest sources of suffering is a promising approach to robustly increase welfare. However, I believe the focus should be on the greatest sources of suffering in the ecosystem, not in any given population, such that effects on non-target organisms can be neglected. Electrically stunning farmed shrimps arguably addresses one of the greatest sources of suffering of farmed shrimps, and the ratio between its effects on target and non-target organisms is much larger than for the vast majority of interventions, but I still do not know whether it increases or decreases welfare (even in expectation) due to potentially dominant effects on soil animals and microorganisms.
I expect the greatest sources of suffering in the ecosystem to be found in the organisms accounting for the most suffering in the ecosystem. However, I would say much more research on comparing welfare across species is needed to identify such organisms. I can see them being vertebrates, invertebrates, trees, or microorganisms.
I worry very specific unrealistic conditions will be needed to ensure the effects on non-target organisms can be neglected if it is not known which organisms account for the most suffering in the ecosystem. So I would prioritise research on comparing welfare across species over mapping sources of suffering in ecosystems.