Thanks for writing this, that was an interesting read!
I will continue to illustrate with separate components, since that’s more general and can capture deeper uncertainty and worse moral uncertainty
Whether or not you think you can add separate components seems pretty important for the hedging approach.
Indeed, if a portfolio dominates the default on each individual component, then some interventions in the portfolio must dominate the default overall.[1] So if you can compare interventions based on their total effects, the existence of such portfolios imply that some interventions dominate the default. Intuitively then, you would prefer investing in one of those interventions over hedging? (Although a complication I haven’t thought about is that you should compare interventions with one another too, unless you think the default has a privileged status.)
Given the above, a worry I have is that the hedging approach doesn’t save us from cluelessness, because we don’t have access to an overall-better-than-the-default intervention to begin with.
To put my two questions in more concrete terms:
In your example, how do we become confident that the wild animal intervention is worst-case net-positive compared to the default?
Given that we’re confident about (1), why prefer a portfolio to investing solely in the wild animal intervention?
Sketch of proof: Let wi be ressources allocated to intervention Ai in your portfolio, and let aji be the worst-case effect of intervention Ai on component j. Then
∑i,jwiaji=∑i(∑jaji)wi≥0
and there is an intervention for which worst-case effects are in aggregate non-negative.
Whether or not you think you can add separate components seems pretty important for the hedging approach.
Indeed, if a portfolio dominates the default on each individual component, then some interventions in the portfolio must dominate the default overall.[1] So if you can compare interventions based on their total effects, the existence of such portfolios imply that some interventions dominate the default.
Ah, good point. (You’re assuming the separate components can be added directly (or with fixed weights, say).)
I guess the cases where you can’t add directly (or with fixed weights) involve genuine normative uncertainty or incommensurability. Or, maybe some cases of two envelopes problems where it’s too difficult or unjustifiable to set a unique common scale and use the Bayesian solution.
In practice, I may have normative uncertainty about moral weights between species.
Intuitively then, you would prefer investing in one of those interventions over hedging?
If you’re risk neutral, probably. Maybe not if you’re difference-making risk averse. Perhaps helping insects is robustly positive in expectation, but highly likely to have no impact at all. Then you might like a better chance of positive impact, while maintaining 0 (or low) probability of negative impact.
Given the above, a worry I have is that the hedging approach doesn’t save us from cluelessness, because we don’t have access to an overall-better-than-the-default intervention to begin with.
For my illustration, that’s right.
However, my illustration treats the components as independent, so that you can get the worst case on each of them together. But this need not be the case in practice. You could in principle have interventions A and B, both with ranges of (expected) cost-effectiveness [-1, 2], but whose sum is exactly 1. Let the cost-effectiveness of B be 1-”the cost-effectiveness of A”. Having things cancel out so exactly and ending up with a range that’s a single value is unrealistic, but I wonder if we could at least get a positive range this way.
(Although a complication I haven’t thought about is that you should compare interventions with one another too, unless you think the default has a privileged status.)
Ya, the default doesn’t seem privileged if you’re a consequentialist. See this post.
Thanks for writing this, that was an interesting read!
Whether or not you think you can add separate components seems pretty important for the hedging approach.
Indeed, if a portfolio dominates the default on each individual component, then some interventions in the portfolio must dominate the default overall.[1] So if you can compare interventions based on their total effects, the existence of such portfolios imply that some interventions dominate the default. Intuitively then, you would prefer investing in one of those interventions over hedging? (Although a complication I haven’t thought about is that you should compare interventions with one another too, unless you think the default has a privileged status.)
Given the above, a worry I have is that the hedging approach doesn’t save us from cluelessness, because we don’t have access to an overall-better-than-the-default intervention to begin with.
To put my two questions in more concrete terms:
In your example, how do we become confident that the wild animal intervention is worst-case net-positive compared to the default?
Given that we’re confident about (1), why prefer a portfolio to investing solely in the wild animal intervention?
Sketch of proof: Let wi be ressources allocated to intervention Ai in your portfolio, and let aji be the worst-case effect of intervention Ai on component j. Then
∑i,jwiaji=∑i(∑jaji)wi≥0and there is an intervention for which worst-case effects are in aggregate non-negative.
Hi Nicolas, thanks for commenting!
Ah, good point. (You’re assuming the separate components can be added directly (or with fixed weights, say).)
I guess the cases where you can’t add directly (or with fixed weights) involve genuine normative uncertainty or incommensurability. Or, maybe some cases of two envelopes problems where it’s too difficult or unjustifiable to set a unique common scale and use the Bayesian solution.
In practice, I may have normative uncertainty about moral weights between species.
If you’re risk neutral, probably. Maybe not if you’re difference-making risk averse. Perhaps helping insects is robustly positive in expectation, but highly likely to have no impact at all. Then you might like a better chance of positive impact, while maintaining 0 (or low) probability of negative impact.
For my illustration, that’s right.
However, my illustration treats the components as independent, so that you can get the worst case on each of them together. But this need not be the case in practice. You could in principle have interventions A and B, both with ranges of (expected) cost-effectiveness [-1, 2], but whose sum is exactly 1. Let the cost-effectiveness of B be 1-”the cost-effectiveness of A”. Having things cancel out so exactly and ending up with a range that’s a single value is unrealistic, but I wonder if we could at least get a positive range this way.
Ya, the default doesn’t seem privileged if you’re a consequentialist. See this post.