Generally, I think that modeling is most useful in situations where we know enough about an issue to construct a solid framework for what effects to include, how to analyze it, and how to provide some evidence-based justification for key parameter values.
Given the enormous, many-layered uncertainties that surround second-order effects (like those on soil animals), and given that we don’t have yet a framework for analyzing such second-order effects comprehensively and equally across all interventions, I think it wouldn’t be responsible for me to speculate on either how various kinds of risk aversion can or should apply to them, or what the impact on our recommendations would be.
Because we have limited capacity, this is going to be my last comment about soil animals in particular. However, if you have questions about other aspects of the Cross Cause Fund, we would be happy to engage with them.
Because we have limited capacity, this is going to be my last comment about soil animals in particular.
I am replying in case anyone is interested, or you want to comeback to it later. Feel free not to reply, and thanks for the thoughts you have shared.
Generally, I think that modeling is most useful in situations where we know enough about an issue to construct a solid framework for what effects to include, how to analyze it, and how to provide some evidence-based justification for key parameter values.
Results are more certain in the situations you describe, but very uncertain results could still be informative. They may help identify the most important uncertainties. The results would not be useful even for this if they were sufficiently arbitrary. However, I do understand why you would believe this.
Based on the “More options” drop-down in question 1 of the Donor Compass, you are comparing the welfare of humans with that of chickens, shrimps, and “non-shrimp invertebrates”, which I assume includes BSFs (as these were covered in Bob’s book). So I would have expected you to be open to comparing the welfare of chickens with that of soil ants and termites, which are macroarthropods like shrimps and BSFs.
In addition, based on the “More options” drop-down in question 2 of the Donor Comass, you are modelling effects after 500 years, which I think are very uncertain. So I would have expected you to be open to modelling how changes in feed consumption resulting from improving the conditions of farmed animals impact the population of soil invertebrates.
Given the enormous, many-layered uncertainties that surround second-order effects (like those on soil animals), and given that we don’t have yet a framework for analyzing such second-order effects comprehensively and equally across all interventions, I think it wouldn’t be responsible for me to speculate on either how various kinds of risk aversion can or should apply to them, or what the impact on our recommendations would be.
Makes sense. I just meant to illustrate accounting for effects on soil invertebrates could change recommendations even if there is large uncertainty about their magnitude and direction.
“In addition, based on the “More options” drop-down in question 2 of the Donor Comass, you are modelling effects after 500 years, which I think are very uncertain.”
That’s not a bad point. I would be kind of on the opposite end of the scale though. We are so clueless about both 500 years in the future and soil arthropods I wouldn’t be modeling either of those....
On the future weights specifically, I think you raise a really interesting point. I tentatively think there are some differences between the GCR modeling and soil animal effects modeling, at least at this stage, but I understand if one wants to zero out their effects (and I suggest below ways to do so).
As a way of background, in the model for GCRs, we largely adopt the Time of Perils perspective, wherein we’re living in an unusually risky period of time, but if we survive past it, existential risk drops to some lower level. The expected value of the future is partially governed by the probability that we survive into the far future. Then, interventions that reduce/increase risk now–even if they only have temporary effects–raise/lower the probability of civilization surviving until a specific year and realizing the value of civilization in that year.
Based on this structure, I do think that the case of modeling the effects of GCR interventions in the far future is meaningfully different enough from modeling the effects of interventions on soil animals to justify including the former in the model but not the latter, at least at this point. For one, we do have some existing frameworks from within the GCR and cause-prioritization academic communities for modeling them (which we drew upon in doing the modeling, see here and here for example). Consistent with working on the terms of the fields themselves, it’s my understanding that at least people think it would be bad if human civilization ended, so that reducing x-risk is good (if we can figure out interventions that actually succeed at this). By contrast, it’s my understanding that how to incorporate effects on soil animals into our models is pretty uncertain, as we’re highly uncertain about all of the mechanisms that affect animal welfare that are involved. And, as you’ve pointed out, we’re also highly uncertain about the direction of the impact on soil animals.
There are many possible improvements that we could make to the modeling of GCR interventions’ long-term effects (see below). Yet, being able to draw upon existing frameworks within the time that we built the first version and having some consensus about the directional value of reducing x-risks made us more confident about including their effects on the longer-term than we feel about soil animals.
Nevertheless, I entirely understand being very skeptical of effects 500+ years out (or even 100-500 years out), as Nick is. I’d recommend using the Advanced User mode and setting the weights for effects 100-500 and 500+ years out to zero. In our baseline inputs, we do include five (out of 14) “clueless” worldviews that assign zero weight to impacts over 100 years out, taking up 30% of the overall credence. Of course, I think reasonable people can prefer to give more weight to clueless worldviews, which they can do with the tool linked above.
I hope that this answer is clear enough – again, it’s an area of significant uncertainty and there can be many different legitimate approaches.
Addendum: Improvements we could make to our modeling of GCR impacts in the far future
Going forward, I think there are many different improvements that we might want to make to our models of the effects of GCR interventions in the far future.
Though we adopt wide uncertainty levels for all of the parameters involved, which result in very different expected values of the future, one weakness of the model is that it’s entirely possible that the future does not follow a Time of Perils trajectory at all (i.e. that risk is flat, or that we go through many periods of high-then-low-risk). Part of the reason we did so is because it makes integrating into the far future tractable, and also because we wanted to represent GCRs as a field in a way that those working inside of it would agree is fair, and that we’re in a Time of Perils is a quite common point of view. But I think a real area of improvement for the model in the future would be to figure out how to incorporate different risk trajectories.
Additionally, I think we could consider implementing some kind of Bayesian discount (like that which David Bernard discussed here in RP’s CURVE sequence) to the expected value of the impact of the GCR interventions for each year in the future. I don’t think we have a very good intuition for what order of magnitude discount to apply to the 500+ year period overall (since our uncertainty could grow very rapidly). I also think that the discount we apply to the impact in the 501st year should be different from the discount in the 10^6th year. Finally, an upside of this approach would be that it separates out cluelessness from person-affecting worldviews, which the time discounts currently don’t do very well.
By contrast, it’s my understanding that how to incorporate effects on soil animals into our models is pretty uncertain, as we’re highly uncertain about all of the mechanisms that affect animal welfare that are involved. And, as you’ve pointed out, we’re also highly uncertain about the direction of the impact on soil animals.
There is strong evidence that chickens in different conditions need a different amount of feed, and that this affects land use, and therefore soil animals? There is uncertainty about the number of soil-animal-years affected, and their welfare per animal-year, but I do not understand why it cannot simply be modelled with wide distributions, at least in the case of soil ants and termites (as there are other macroarthropods covered in Bob’s book about comparing welfare across species).
Hi Vasco,
Generally, I think that modeling is most useful in situations where we know enough about an issue to construct a solid framework for what effects to include, how to analyze it, and how to provide some evidence-based justification for key parameter values.
Given the enormous, many-layered uncertainties that surround second-order effects (like those on soil animals), and given that we don’t have yet a framework for analyzing such second-order effects comprehensively and equally across all interventions, I think it wouldn’t be responsible for me to speculate on either how various kinds of risk aversion can or should apply to them, or what the impact on our recommendations would be.
Because we have limited capacity, this is going to be my last comment about soil animals in particular. However, if you have questions about other aspects of the Cross Cause Fund, we would be happy to engage with them.
I am replying in case anyone is interested, or you want to comeback to it later. Feel free not to reply, and thanks for the thoughts you have shared.
Results are more certain in the situations you describe, but very uncertain results could still be informative. They may help identify the most important uncertainties. The results would not be useful even for this if they were sufficiently arbitrary. However, I do understand why you would believe this.
Based on the “More options” drop-down in question 1 of the Donor Compass, you are comparing the welfare of humans with that of chickens, shrimps, and “non-shrimp invertebrates”, which I assume includes BSFs (as these were covered in Bob’s book). So I would have expected you to be open to comparing the welfare of chickens with that of soil ants and termites, which are macroarthropods like shrimps and BSFs.
In addition, based on the “More options” drop-down in question 2 of the Donor Comass, you are modelling effects after 500 years, which I think are very uncertain. So I would have expected you to be open to modelling how changes in feed consumption resulting from improving the conditions of farmed animals impact the population of soil invertebrates.
Makes sense. I just meant to illustrate accounting for effects on soil invertebrates could change recommendations even if there is large uncertainty about their magnitude and direction.
“In addition, based on the “More options” drop-down in question 2 of the Donor Comass, you are modelling effects after 500 years, which I think are very uncertain.”
That’s not a bad point. I would be kind of on the opposite end of the scale though. We are so clueless about both 500 years in the future and soil arthropods I wouldn’t be modeling either of those....
Hi Vasco and Nick,
On the future weights specifically, I think you raise a really interesting point. I tentatively think there are some differences between the GCR modeling and soil animal effects modeling, at least at this stage, but I understand if one wants to zero out their effects (and I suggest below ways to do so).
As a way of background, in the model for GCRs, we largely adopt the Time of Perils perspective, wherein we’re living in an unusually risky period of time, but if we survive past it, existential risk drops to some lower level. The expected value of the future is partially governed by the probability that we survive into the far future. Then, interventions that reduce/increase risk now–even if they only have temporary effects–raise/lower the probability of civilization surviving until a specific year and realizing the value of civilization in that year.
Based on this structure, I do think that the case of modeling the effects of GCR interventions in the far future is meaningfully different enough from modeling the effects of interventions on soil animals to justify including the former in the model but not the latter, at least at this point. For one, we do have some existing frameworks from within the GCR and cause-prioritization academic communities for modeling them (which we drew upon in doing the modeling, see here and here for example). Consistent with working on the terms of the fields themselves, it’s my understanding that at least people think it would be bad if human civilization ended, so that reducing x-risk is good (if we can figure out interventions that actually succeed at this). By contrast, it’s my understanding that how to incorporate effects on soil animals into our models is pretty uncertain, as we’re highly uncertain about all of the mechanisms that affect animal welfare that are involved. And, as you’ve pointed out, we’re also highly uncertain about the direction of the impact on soil animals.
There are many possible improvements that we could make to the modeling of GCR interventions’ long-term effects (see below). Yet, being able to draw upon existing frameworks within the time that we built the first version and having some consensus about the directional value of reducing x-risks made us more confident about including their effects on the longer-term than we feel about soil animals.
Nevertheless, I entirely understand being very skeptical of effects 500+ years out (or even 100-500 years out), as Nick is. I’d recommend using the Advanced User mode and setting the weights for effects 100-500 and 500+ years out to zero. In our baseline inputs, we do include five (out of 14) “clueless” worldviews that assign zero weight to impacts over 100 years out, taking up 30% of the overall credence. Of course, I think reasonable people can prefer to give more weight to clueless worldviews, which they can do with the tool linked above.
I hope that this answer is clear enough – again, it’s an area of significant uncertainty and there can be many different legitimate approaches.
Addendum: Improvements we could make to our modeling of GCR impacts in the far future
Going forward, I think there are many different improvements that we might want to make to our models of the effects of GCR interventions in the far future.
Though we adopt wide uncertainty levels for all of the parameters involved, which result in very different expected values of the future, one weakness of the model is that it’s entirely possible that the future does not follow a Time of Perils trajectory at all (i.e. that risk is flat, or that we go through many periods of high-then-low-risk). Part of the reason we did so is because it makes integrating into the far future tractable, and also because we wanted to represent GCRs as a field in a way that those working inside of it would agree is fair, and that we’re in a Time of Perils is a quite common point of view. But I think a real area of improvement for the model in the future would be to figure out how to incorporate different risk trajectories.
Additionally, I think we could consider implementing some kind of Bayesian discount (like that which David Bernard discussed here in RP’s CURVE sequence) to the expected value of the impact of the GCR interventions for each year in the future. I don’t think we have a very good intuition for what order of magnitude discount to apply to the 500+ year period overall (since our uncertainty could grow very rapidly). I also think that the discount we apply to the impact in the 501st year should be different from the discount in the 10^6th year. Finally, an upside of this approach would be that it separates out cluelessness from person-affecting worldviews, which the time discounts currently don’t do very well.
Thanks for the context, Laura.
There is strong evidence that chickens in different conditions need a different amount of feed, and that this affects land use, and therefore soil animals? There is uncertainty about the number of soil-animal-years affected, and their welfare per animal-year, but I do not understand why it cannot simply be modelled with wide distributions, at least in the case of soil ants and termites (as there are other macroarthropods covered in Bob’s book about comparing welfare across species).