I am wondering if assigning "moral credit" for offset purposes is too complex to do with an algorithm and instead requires context-specific application of judgment. A few possible examples:
- Let's assume that most of the individuals who voted for Prop 12 consume animal products regulated by the measure, and that Prop 12 causes an increase in the cost of those products. By voting yes, these individuals would have voted for taking money out of their own pockets to pay for the increase in animal welfare. While I'm fine adjusting "moral credit" based on the risk undertaken, I'm uneasy with a system that gives donors orders of magnitude more moral credit than others who voluntarily bear costs to achieve the objective.
- I also wouldn't collectively give the Supreme Court any "moral credit" for voting to uphold Prop 12, such that at least the Justices in the majority should feel entitled to eat meat without offsetting. This holds despite the counterfactual value and what I imagine the Shapley value for each Justice's vote would be.
- Moreover, every elections cycle, the voters could repeal Prop 12. Getting the repeal measure on the ballot shouldn't be too difficult, and there are monied interests who would happily bear those costs. If they do not, it is likely because they decided that the voters would shoot them down. So for the subsequent elections cycle, there are at least two necessary conditions for Prop 12's benefits to persist to Cycle 2: it got passed at the beginning of Cycle 1 and it didn't get repealed at the start of Cycle 2. It's true that nobody really did anything during Cycle 1 to protect Prop 12, but it's also true that the voters at the end of Cycle 1 have been judged willing to continue bearing Prop 12's costs in Cycle 2 to continue its benefits. It seems odd to attribute all of the benefits accruing in Cycle 2 to Cycle 1 activity. But how to split the moral credit here?
Motivated reasoning is always a risk, and any moral-credit granting analysis is more likely to be underinclusive (and thus over-grant available moral credit to influences that were identified) than the reverse. In some or even many cases, it may be necessary to apply an upward adjustment on even min(counterfactual value, Shapley value) to account for these factors.
Thanks for posting this!
I think we can run into problems when we attempt to transfer cost-effectiveness analyses that were sound enough to answer "where should I donate?" into the harder question of "how much do I need to give to offset"? As you point out, assigning ~100% of the counterfactual good to the donor is . . . at a minimum, generous.
When we are asking where to donate, that often isn't a major problem. For example, if my goal is to save lives, I can often assume that errors in assigning "moral credit" will be roughly equal across (at least) GiveWell-style charities like AMF. Because the error term is similar for all giving opportunities, we can usually ignore it because it shouldn't change the relative ranking of the giving opportunities unless they are fairly close.
But offset situations pose a different question -- we are looking to morally claim a certain quantum of good to counterbalance the not-good we are producing elsewhere. That means we need an absolute measure (or at least estimate) of that quantum. As a result, if we want to find the minimum amount necessary to offset, we necessarily must make judgments about distributing the moral credit available.
Some people might also want a confidence interval for their offsetting action -- e.g., "I want to be 99% confident that I am giving enough to actually offset my production of not-goods." This is likely impossible with some interventions. For instance, if I think there is a greater than 1% chance that the critics are correct that corporate campaigns are net-negative in the long run, then my 99% confidence interval will always include negative values.
Someone who wants confidence in actual offset -- rather than offset in expectancy -- would logically seek "safer" donation opportunities. These would generally have more certain impact and low spread of potential impacts. Perhaps a bundle of interventions could achieve the necessary confidence interval (such as 3 programs with an 80% chance of success and no appreciable risk of being net harmful, or a larger number at lower success probabilities).