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. (Youâre assuming the separate components can be added directly (or with fixed weights, say).)
I guess the cases where you canât add directly (or with fixed weights) involve genuine normative uncertainty or incommensurability. Or, maybe some cases of two envelopes problems where itâs 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 youâre risk neutral, probably. Maybe not if youâre 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 doesnât save us from cluelessness, because we donât have access to an overall-better-than-the-default intervention to begin with.
For my illustration, thatâs 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-âthe cost-effectiveness of Aâ. Having things cancel out so exactly and ending up with a range thatâs a single value is unrealistic, but I wonder if we could at least get a positive range this way.
(Although a complication I havenât thought about is that you should compare interventions with one another too, unless you think the default has a privileged status.)
Ya, the default doesnât seem privileged if youâre a consequentialist. See this post.
Hi Nicolas, thanks for commenting!
Ah, good point. (Youâre assuming the separate components can be added directly (or with fixed weights, say).)
I guess the cases where you canât add directly (or with fixed weights) involve genuine normative uncertainty or incommensurability. Or, maybe some cases of two envelopes problems where itâs 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 youâre risk neutral, probably. Maybe not if youâre 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, thatâs 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-âthe cost-effectiveness of Aâ. Having things cancel out so exactly and ending up with a range thatâs a single value is unrealistic, but I wonder if we could at least get a positive range this way.
Ya, the default doesnât seem privileged if youâre a consequentialist. See this post.