I think the reason summing counterfactual impact of multiple people leads to weird results is not a problem with counterfactual impact but with how you are summing it. Adding together each individual’s counterfactual impact by summing is adding the difference between world A where they both act and world B and C where each of them act alone. In your calculus, you then assume this is the same as the difference between world A and D where nobody acts.
The true issue in maximising counterfactual impact seems to arise when actors act cooperatively but think of their actions as an individual. When acting cooperatively you should compare your counterfactuals to world D, when acting individually world B or C.
The Shapley value is not immune to error either I can see three ways it could lead to poor decision making:
For the Vaccine Reminder example, It seems more strange to me to attribute impact to people who would otherwise have no impact. We then get the same double-counting problem or in this case infinite dividing which is worse as It can dissuade you of high impact options. If I am not mistaken, then in this case the Shapley value is divided between the NGO, the government, the doctor, the nurse, the people driving logistics, the person who built the roads, the person who trained the doctor, the person who made the phones, the person who set up the phone network and the person who invented electricity. In which case, everyone is attributed a tiny fraction of the impact when only the vaccine reminder intentionally caused it. Depending on the scope of other actors we consider this could massively reduce the impact of the action.
Example 6 reveals another flaw as attributing impact this way can lead you to make poor decisions. If you use the Shapley value then when examining whether to leak information as the 10th person you see that the action costs −1million utilities. If I was offered 500,000 utils to share then under Shapley I should not do so as 500,00 −1M is negative. However, this thinking will just prevent me from increasing overall utilis by 500,000.
In example 7 the counterfactual impact of the applicant who gets the job is not 0 but the impact of the job the lowest impact person gets. Imagine each applicant could earn to give 2 utility and only has time for one job application. When considering counterfactual impact the first applicant chooses to apply to the EA org and gets attributed 100 utility (as does the EA org). The other applicants now enter the space and decide to earn to give as this has a higher counterfactual impact. They decrease the first applicant’s counterfactual utility to 2 but increase overall utility. If we use Shapely instead then all applicants would apply for the EA org and as this gives them a value of 2.38 instead of 2.
I may have misunderstood Shapely here so feel free to correct me. Overall I enjoyed the post and think it is well worth reading. Criticism of the underlying assumptions of many EAs decision-making methods is very valuable.
At Animal Ask we did later hear some of that feedback ourselves and one of our early projects failed for similar reasons. Our programs are very group-led, as in we select our research priorities based on groups looking to pursue new campaigns. This means the majority of our projects tend to focus on policy rather than corporate work, given more groups consider new country-specific campaigns and want research to inform this decision.
In the original report from CE, they do account for the consolidation of corporate work behind a few asks. They expected the research on corporate work to be ‘ongoing’ deeper’ and ‘more focused research’. So strategically would look more like research throughout the previous corporate campaign to inform the next with a low probability of updating any specific ask. The expectation is that it could be many years between the formation of corporate asks.
So in fact this consolidation was highlighted in the incubation program as a reason success could have so much impact. As with the large amount of resources the movement devotes to these consolidated corporate asks ensuring these are optimised is essential.
As Ren outlined we have a couple of recent, more detailed evaluations and we have found that the main limitations on our impact are factors only a minority of advisors in the animal space highlighted. These are constraints from other organisation stakeholders either upper management (when the campaigns team had updated on our findings but there was momentum behind another campaign) or funders (particular individual or smaller donors who are typicaly less research motivated than OPP, EAAWF, ACE etc.)
You can see this was the main concern for CE researchers in the original report. “Organizations in the animal space are increasingly aware of the importance of research, but often there are many factors to consider, including logistical ease, momentum, and donor interest. It is possible that this research would not be the determining factor in many cases”.