I tried estimating years of impact using graphs like this:
[...]
The yellow line accounts for the possibility that commitments will stop being relevant due to things like x-risks, global catastrophic risks, societal collapse, cultured meat taking over, animals bred not to suffer, black swans, etc. [...]
[...]
Finally, we estimate the expected proportion of hens used by companies that will be cage-free each year as follows: (blue—red) ✕ yellow. And then we add up the result for all years to calculate years of impact.
I would estimate the number of layer-years improved in expectation in year Y from “expected population of layers in year Y”*(“expected population of layers in cages in year Y without the intervention as a fraction of all of them in year Y”—“expected population of layers in cages in year Y with the intervention as a fraction of all of them in year Y”) = P(Y)*(f_control(Y) - f_intervention(Y)), which is correct by definition. I would then calculate the total number of layer-years improved adding the effects from the year in which the intervention started on. I believe the annual effects should eventually go to 0, such that there is no need to add the effects of all the years until infinity. It is enough to consider the years accounting for the vast majority of the total number of layer-years improved.
P, f_control, and f_intervention relate to your yellow, red, and blue lines, but their meaning is more intuitive. In addition, the yellow line in your formula should not be strictly seen as a probability for it to work in all cases. A probability describes effects that would make the fraction of hens in cages the same with and without the intervention, which applies to, for example, human extinction. However, there are non-binary gradual effects like the raise of alternative proteins which make the fraction of hens in cages with and without the intervention more similar in expectation, but without all the effect coming from the possibility of the fraction with and without the intervention becoming the same.
I should say though that based on my conversations at the time, it seemed unlikely that alt proteins will make a big difference.
Interesting. I think changes in diet, not global catastrophes, are the driver of reductions in the number of farmed animals. I guess alternative proteins will have a negligible effect over the next 10 years, but that their effect may well be the most important one in 100 years.
I would estimate the number of layer-years improved in expectation in year Y from “expected population of layers in year Y”*(“expected population of layers in cages in year Y without the intervention as a fraction of all of them in year Y”—“expected population of layers in cages in year Y with the intervention as a fraction of all of them in year Y”) = P(Y)*(f_control(Y) - f_intervention(Y)), which is correct by definition.
Cost-effectiveness analyses (CEAs) of interventions accelerating animal welfare reforms usually estimate the increase in the welfare of the target animals (for example, hens in cages) based on the acceleration in years of the full implementation of the reform. This makes sense if each level of implementation of the reform is accelerated as much as its full implementation.
However, there are many cases where the acceleration of the full implementation of the reform is not enough to determine the number of animals helped, or animal-years improved. I discuss some below.
Hi Saulius.
I would estimate the number of layer-years improved in expectation in year Y from “expected population of layers in year Y”*(“expected population of layers in cages in year Y without the intervention as a fraction of all of them in year Y”—“expected population of layers in cages in year Y with the intervention as a fraction of all of them in year Y”) = P(Y)*(f_control(Y) - f_intervention(Y)), which is correct by definition. I would then calculate the total number of layer-years improved adding the effects from the year in which the intervention started on. I believe the annual effects should eventually go to 0, such that there is no need to add the effects of all the years until infinity. It is enough to consider the years accounting for the vast majority of the total number of layer-years improved.
P, f_control, and f_intervention relate to your yellow, red, and blue lines, but their meaning is more intuitive. In addition, the yellow line in your formula should not be strictly seen as a probability for it to work in all cases. A probability describes effects that would make the fraction of hens in cages the same with and without the intervention, which applies to, for example, human extinction. However, there are non-binary gradual effects like the raise of alternative proteins which make the fraction of hens in cages with and without the intervention more similar in expectation, but without all the effect coming from the possibility of the fraction with and without the intervention becoming the same.
You are right that my use of the word ‘probability’ for the yellow line was a bit misleading. 1% decrease in the yellow line could mean either
1% chance that there is no more animal farming because of an x-risk or something
1% reduction in meat production due to alt proteins
I should say though that based on my conversations at the time, it seemed unlikely that alt proteins will make a big difference.
Interesting. I think changes in diet, not global catastrophes, are the driver of reductions in the number of farmed animals. I guess alternative proteins will have a negligible effect over the next 10 years, but that their effect may well be the most important one in 100 years.
Here is a post illustrating this.