I agree. So the worst case is that the campaigns cause lots of suffering (relative to inaction)?
I donât think this is right. I think youâre still treating âAvoiding the worstâ like a difference-making view. You shouldnât be thinking in terms of ârelative to inactionâ, which itself has a highly uncertain distribution of outcomes. Just evaluate the distribution of outcomes for each option, without fixing any as a comparison option.
The question is only whether worst-case outcomes are more or less likely with action or inaction.
FWIW, the actual worst cases are s-risks, and Iâd expect âAvoiding the worstâ views to prioritize their mitigation, as long as weâre not clueless about that.
You shouldnât be thinking in terms of ârelative to inactionâ, which itself has a highly uncertain distribution of outcomes. Just evaluate the distribution of outcomes for each option, without fixing any as a comparison option.
I had understood this. As I said, âI understand I should look into the distributions of global welfare with and without the campaigns, and then assess their negative tailsâ. My phrasing âcause lots of suffering (relative to inaction)â was confusing. However, I meant âincrease the probability of outcomes with lots of suffering (the worst) relative to the probability under inactionâ.
Ah, sorry, I was too quick and should have read more carefully.
However, I believe the distribution with the campaigns has longer positive and negative tails
Why do you believe this?
As chicken and egg prices increase from these welfare reforms, I would expect:
some shifts between crops and nature (including through substitution), but Iâm clueless about which involves more suffering, so this doesnât clearly favour one or the other.
substitution towards beef and other pasture products, which reduces invertebrate populations substantially, and probably without making lives much worse. This would mean less suffering and so do better in the worst case.
Here is how I am thinking. Imagine the world has welfare 0.
With inaction, the final welfare will be 0 with probability 100 %.
With campaigns, there is lots of uncertainty, but here is a simplified set of outcomes:
Agricultural land will increase with probability 75 %, and decrease with probability 25 %.
If agricultural land increases, the final welfare will be â1 with probability 25 %, and 1 with probability 75 %. So the final welfare will be â1 with probability of 18.75 % (= 0.75*0.25), and 1 with probability 56.25 % (= 0.75*0.75) considering outcomes where agricultural land increases.
If agricultural land decreases, the final welfare will be â1 with probability 75 %, and 1 with probability 25 %. So the final welfare will be â1 with probability 18.75 % (= 0.25*0.75), and 1 with probability 6.25 % (= 0.25*0.25) considering outcomes where agricultural land decreases.
As a result, final welfare will be â1 with probability 37.5 % (= 0.1875*2), 1 with probability 62.5 % (= 0.5625 + 0.0625), and 0.25 (= 0.375*(-1) + 0.625*1) in expectation.
The worst possible outcome across the 2 interventions is a final welfare of â1. With inaction, it has a probability of 0. With campaigns, it has a probability of 37.5 %. So the campaigns make the worst possible outcome more likely.
Unless you believe the expected amount of wild animal suffering is higher all-things-considered than with inaction, you shouldnât really expect it to do worse according to âAvoiding the worstâ risk aversion (as a heuristic; there could be exceptions).
The intervention which can decrease welfare the most is the one leading to the lowest possible final welfare.
With inaction, the final welfare will be 0 with probability 100 %.
This is exactly a procedure you could follow for difference-making risk aversion; itâs equivalent to taking the statewise difference with inaction. The welfare of the world with inaction isnât 0 with probability 100%.
RP has a model/âprocedure for avoiding the worst risk aversion here.
The welfare of the world with inaction isnât 0 with probability 100%.
Why? I was assuming a world with initial welfare 0 with probability 100 %. However, I think my point stands for any distribution describing the initial welfare.
RP has a model/âprocedure for avoiding the worst risk aversion here.
If you instead set the campaign option to 0 welfare and defined the welfare of the world with inaction relative to the campaign option, youâd end up with the opposite conclusion, that only inaction reaches â1.
Avoiding the worst is meant to treat each option symmetrically. It doesnât depend (in theory) on which option you single out to define things relative to.
(RPâs practical procedure does start with inaction, but if you end up with the same probability distributions for each option in the end, the results will be the same as if you started with a different option to define all distributions relative to. I think their procedure helps ensure consistent probability assignments and is less work, compared to directly estimating each distribution independently.)
What exactly do you mean by this? The campaign has many potential effects. So it cannot result in a final welfare of X with probability 100 %, where X can be 0 or any other number.
Suppose the initial welfare is 0 with probability 100 %. Inaction would lead to a final welfare of 0 with probability 100 %. Imagine an intervention which decreases welfare by 1 with probability 50 %, and increases welfare by 1 with probability 50 %. The intervention leads to a final welfare of 0 in expectation. However, it leads to a final welfare of â1 with probability 50 %, and 1 with probability 50 %. The the lowest possible welfare of â1 is more likely with the intervention?
Inaction also does not in fact lead to welfare of 0 with probability 100%. There will be lots of animals suffering and many possible outcomes if we do nothing. So itâs not correct to assume total welfare of 0.
I think my point stands for any distribution describing the initial welfare. Imagine the minimum initial welfare W_min has probability p. With inaction, the minimum final welfare would still be W_min, and have probability p. With an intervention which decreases welfare by 1 with probability 50 %, and increases welfare by 1 with probability 50 %, the minimum final welfare would be W_min â 1 with probability 0.5*p. So the lowest possible welfare of W_min â 1 would be lower and more likely with the intervention?
No, I donât think this is the right way to model this. This looks a lot like the typical error people make for the original two envelopes problem.
Initial welfare (what does that mean?) and final welfare after inaction can differ, because the world, e.g. land use, will change even if you do nothing, and campaigns take time for their effects to materialize.
If you swapped the roles of campaign and inaction, you would flip the conclusion, too.
This looks a lot like the typical error people make for the original two envelopes problem.
The moral two envelopes problem is not problematic if there is a common scale to compare the welfare per unit time (as there is to compare temperature)?
Initial welfare (what does that mean?) and final welfare after inaction can differ, because the world, e.g. land use, will change even if you do nothing, and campaigns take time for their effects to materialize.
Suppose that inaction leads to a distribution for the future welfare (integral of the welfare per unit time across all future time) whose minimum value W_min has probability p. With an intervention that decreases future welfare by 1 with probability 50 %, and increases it by 1 with probability 50 %, the minimum future welfare would be W_min â 1 with probability 0.5*p. So I think the lowest possible future welfare of W_min â 1 would be lower and more likely with the intervention (although the intervention would not change future welfare in expectation).
If you swapped the roles of campaign and inaction
What do you mean by this? By definition, inaction does not change the distribution of the future welfare?
I see. Thanks for the patience. I could equally say that an intervention leads to a distribution for the future welfare whose minimum value W_min has probability p, and that inaction decreases it by 1 with probability 50 %, and increases it by 1 with probability 50 %, thus implying a minimum future welfare of W_min â 1 with probability 0.5*p. This is the exact opposite of what I concluded above, and suggests the lowest possible future welfare of W_min â 1 would be lower and more likely with inaction.
I agree both models are wrong. I cannot assume that the change in future welfare caused by the intervention is independent from the future welfare under inaction (as I did in my past comments), or that the change in future welfare caused by inaction is independent from the future welfare caused by the intervention (as I did just above).
Unless you believe the expected amount of wild animal suffering is higher all-things-considered than with inaction, you shouldnât really expect it to do worse according to âAvoiding the worstâ risk aversion (as a heuristic; there could be exceptions).
I agree that increasing welfare in expectation is a good heuristic for better performance under âavoiding the worstâ risk aversion. I have very little idea about whether cage-free campaigns for laying hens increase or decrease welfare in expectation. So I do not know whether they are favoured or not under âavoiding the worstâ risk aversion. They are still disfavoured under difference-making and ambiguity risk aversion, and this could make them worse than inaction. In addition, they may be worse than inaction under no risk aversion of any type.
I donât think this is right. I think youâre still treating âAvoiding the worstâ like a difference-making view. You shouldnât be thinking in terms of ârelative to inactionâ, which itself has a highly uncertain distribution of outcomes. Just evaluate the distribution of outcomes for each option, without fixing any as a comparison option.
The question is only whether worst-case outcomes are more or less likely with action or inaction.
FWIW, the actual worst cases are s-risks, and Iâd expect âAvoiding the worstâ views to prioritize their mitigation, as long as weâre not clueless about that.
I had understood this. As I said, âI understand I should look into the distributions of global welfare with and without the campaigns, and then assess their negative tailsâ. My phrasing âcause lots of suffering (relative to inaction)â was confusing. However, I meant âincrease the probability of outcomes with lots of suffering (the worst) relative to the probability under inactionâ.
Ah, sorry, I was too quick and should have read more carefully.
Why do you believe this?
As chicken and egg prices increase from these welfare reforms, I would expect:
some shifts between crops and nature (including through substitution), but Iâm clueless about which involves more suffering, so this doesnât clearly favour one or the other.
substitution towards beef and other pasture products, which reduces invertebrate populations substantially, and probably without making lives much worse. This would mean less suffering and so do better in the worst case.
Here is how I am thinking. Imagine the world has welfare 0.
With inaction, the final welfare will be 0 with probability 100 %.
With campaigns, there is lots of uncertainty, but here is a simplified set of outcomes:
Agricultural land will increase with probability 75 %, and decrease with probability 25 %.
If agricultural land increases, the final welfare will be â1 with probability 25 %, and 1 with probability 75 %. So the final welfare will be â1 with probability of 18.75 % (= 0.75*0.25), and 1 with probability 56.25 % (= 0.75*0.75) considering outcomes where agricultural land increases.
If agricultural land decreases, the final welfare will be â1 with probability 75 %, and 1 with probability 25 %. So the final welfare will be â1 with probability 18.75 % (= 0.25*0.75), and 1 with probability 6.25 % (= 0.25*0.25) considering outcomes where agricultural land decreases.
As a result, final welfare will be â1 with probability 37.5 % (= 0.1875*2), 1 with probability 62.5 % (= 0.5625 + 0.0625), and 0.25 (= 0.375*(-1) + 0.625*1) in expectation.
The worst possible outcome across the 2 interventions is a final welfare of â1. With inaction, it has a probability of 0. With campaigns, it has a probability of 37.5 %. So the campaigns make the worst possible outcome more likely.
The intervention which can decrease welfare the most is the one leading to the lowest possible final welfare.
This is exactly a procedure you could follow for difference-making risk aversion; itâs equivalent to taking the statewise difference with inaction. The welfare of the world with inaction isnât 0 with probability 100%.
RP has a model/âprocedure for avoiding the worst risk aversion here.
Why? I was assuming a world with initial welfare 0 with probability 100 %. However, I think my point stands for any distribution describing the initial welfare.
I have read the section Avoiding the Worst Risk Aversion: A Model, and I do not understand why you think it undermines my point.
If you instead set the campaign option to 0 welfare and defined the welfare of the world with inaction relative to the campaign option, youâd end up with the opposite conclusion, that only inaction reaches â1.
Avoiding the worst is meant to treat each option symmetrically. It doesnât depend (in theory) on which option you single out to define things relative to.
(RPâs practical procedure does start with inaction, but if you end up with the same probability distributions for each option in the end, the results will be the same as if you started with a different option to define all distributions relative to. I think their procedure helps ensure consistent probability assignments and is less work, compared to directly estimating each distribution independently.)
What exactly do you mean by this? The campaign has many potential effects. So it cannot result in a final welfare of X with probability 100 %, where X can be 0 or any other number.
Suppose the initial welfare is 0 with probability 100 %. Inaction would lead to a final welfare of 0 with probability 100 %. Imagine an intervention which decreases welfare by 1 with probability 50 %, and increases welfare by 1 with probability 50 %. The intervention leads to a final welfare of 0 in expectation. However, it leads to a final welfare of â1 with probability 50 %, and 1 with probability 50 %. The the lowest possible welfare of â1 is more likely with the intervention?
It was illustrative.
Inaction also does not in fact lead to welfare of 0 with probability 100%. There will be lots of animals suffering and many possible outcomes if we do nothing. So itâs not correct to assume total welfare of 0.
I think my point stands for any distribution describing the initial welfare. Imagine the minimum initial welfare W_min has probability p. With inaction, the minimum final welfare would still be W_min, and have probability p. With an intervention which decreases welfare by 1 with probability 50 %, and increases welfare by 1 with probability 50 %, the minimum final welfare would be W_min â 1 with probability 0.5*p. So the lowest possible welfare of W_min â 1 would be lower and more likely with the intervention?
No, I donât think this is the right way to model this. This looks a lot like the typical error people make for the original two envelopes problem.
Initial welfare (what does that mean?) and final welfare after inaction can differ, because the world, e.g. land use, will change even if you do nothing, and campaigns take time for their effects to materialize.
If you swapped the roles of campaign and inaction, you would flip the conclusion, too.
The moral two envelopes problem is not problematic if there is a common scale to compare the welfare per unit time (as there is to compare temperature)?
Suppose that inaction leads to a distribution for the future welfare (integral of the welfare per unit time across all future time) whose minimum value W_min has probability p. With an intervention that decreases future welfare by 1 with probability 50 %, and increases it by 1 with probability 50 %, the minimum future welfare would be W_min â 1 with probability 0.5*p. So I think the lowest possible future welfare of W_min â 1 would be lower and more likely with the intervention (although the intervention would not change future welfare in expectation).
What do you mean by this? By definition, inaction does not change the distribution of the future welfare?
In your model and your answers here, just replace inaction with campaign and campaign with inaction.
I see. Thanks for the patience. I could equally say that an intervention leads to a distribution for the future welfare whose minimum value W_min has probability p, and that inaction decreases it by 1 with probability 50 %, and increases it by 1 with probability 50 %, thus implying a minimum future welfare of W_min â 1 with probability 0.5*p. This is the exact opposite of what I concluded above, and suggests the lowest possible future welfare of W_min â 1 would be lower and more likely with inaction.
I agree both models are wrong. I cannot assume that the change in future welfare caused by the intervention is independent from the future welfare under inaction (as I did in my past comments), or that the change in future welfare caused by inaction is independent from the future welfare caused by the intervention (as I did just above).
I agree that increasing welfare in expectation is a good heuristic for better performance under âavoiding the worstâ risk aversion. I have very little idea about whether cage-free campaigns for laying hens increase or decrease welfare in expectation. So I do not know whether they are favoured or not under âavoiding the worstâ risk aversion. They are still disfavoured under difference-making and ambiguity risk aversion, and this could make them worse than inaction. In addition, they may be worse than inaction under no risk aversion of any type.