Great start, I’m looking forward to seeing how this software develops!
I noticed that the model estimates of cost-effectiveness for GHD/animal welfare and x-risk interventions are not directly comparable. Whereas the x-risk interventions are modeled as a stream of benefits that could be realized over the next 1,000 years (barring extinction), the distribution of cost-effectiveness for a GHD or animal welfare is taken as given. Indeed:
For interventions in global health and development we don’t model impact internally, but instead stipulate the range of possible values. This intervention is assumed to cost between<lower bound>and<upper bound> per DALY averted.
So I’d be keen to see more granular modeling of the benefits of these interventions, especially over longer time scales. For example, cash transfers have not only immediate benefits to their recipients, but also a multiplier effect on the economy: according to this 80k episode, $1 in cash transfers produces $2.50 in additional economic output for the community. This could ultimately put the economy on a higher growth path. What effect would a $1000 donation to GiveDirectly or AMF have over the next 20-50 years?
Similarly, for an animal welfare intervention such as a corporate cage-free campaign, the long-term effects would depend on how long the cage-free policy is expected to last, how well it’s enforced, etc. This would be undoubtedly complicated to model, but would help make these interventions easier to compare with traditionally “longtermist” interventions.
Thanks for this insightful comment. We’ve focused on capturing the sorts of value traditionally ascribed to each kind of intervention. For existential risk mitigation, this is additional life years lived. For animal welfare interventions, this is suffering averted. You’re right that there are surely other effects of these interventions. Existential risk mitigation and ghd interventions will have an effect on animals, for instance. Animal welfare interventions might contribute to moral circle expansion. Including these side effects is not just difficult, it adds a significant amount of uncertainty. The side effects we choose to model may determine the ultimate value we get out. The way we choose to model these side effects will add a lot of noise that makes the upshots of the model much more sensitive to our particular choices. This doesn’t mean that we think it’s okay to ignore these possible effects. Instead, we conceive of the model as a starting point for further thought, not a conclusive verdict on relative value assessments.
Similarly, for an animal welfare intervention such as a corporate cage-free campaign, the long-term effects would depend on how long the cage-free policy is expected to last, how well it’s enforced, etc. This would be undoubtedly complicated to model, but would help make these interventions easier to compare with traditionally “longtermist” interventions.
To some extent, these sorts of considerations can be included via existing parameters. There is a parameter to determine how long the intervention’s effects will last. I’ve been thinking of this as the length of time before the same policies would have been adopted, but you might think of this as the time at which companies renege on their commitments. We can also set a range of percentages of the population affected that represents the failure to follow through.
Great start, I’m looking forward to seeing how this software develops!
I noticed that the model estimates of cost-effectiveness for GHD/animal welfare and x-risk interventions are not directly comparable. Whereas the x-risk interventions are modeled as a stream of benefits that could be realized over the next 1,000 years (barring extinction), the distribution of cost-effectiveness for a GHD or animal welfare is taken as given. Indeed:
So I’d be keen to see more granular modeling of the benefits of these interventions, especially over longer time scales. For example, cash transfers have not only immediate benefits to their recipients, but also a multiplier effect on the economy: according to this 80k episode, $1 in cash transfers produces $2.50 in additional economic output for the community. This could ultimately put the economy on a higher growth path. What effect would a $1000 donation to GiveDirectly or AMF have over the next 20-50 years?
Similarly, for an animal welfare intervention such as a corporate cage-free campaign, the long-term effects would depend on how long the cage-free policy is expected to last, how well it’s enforced, etc. This would be undoubtedly complicated to model, but would help make these interventions easier to compare with traditionally “longtermist” interventions.
Thanks for this insightful comment. We’ve focused on capturing the sorts of value traditionally ascribed to each kind of intervention. For existential risk mitigation, this is additional life years lived. For animal welfare interventions, this is suffering averted. You’re right that there are surely other effects of these interventions. Existential risk mitigation and ghd interventions will have an effect on animals, for instance. Animal welfare interventions might contribute to moral circle expansion. Including these side effects is not just difficult, it adds a significant amount of uncertainty. The side effects we choose to model may determine the ultimate value we get out. The way we choose to model these side effects will add a lot of noise that makes the upshots of the model much more sensitive to our particular choices. This doesn’t mean that we think it’s okay to ignore these possible effects. Instead, we conceive of the model as a starting point for further thought, not a conclusive verdict on relative value assessments.
To some extent, these sorts of considerations can be included via existing parameters. There is a parameter to determine how long the intervention’s effects will last. I’ve been thinking of this as the length of time before the same policies would have been adopted, but you might think of this as the time at which companies renege on their commitments. We can also set a range of percentages of the population affected that represents the failure to follow through.