With charity we’re looking for global utility. That would be analogous to total market expansion in the for-profit world. When someone arbitrages a pricing failure and gains some, someone else loses the same amount. That’s a zero-sum game around the market expansion line. We don’t have to take that into account here, there’s likely no speculation around charity utility.
But say charity A has a track record of $20 cost per DALY, charity B has $30, charity C has $40. As we don’t know the future, hand picking charity A and giving all our donations to them would be a mistake—maybe they will not be that efficient next year, maybe they will operate on $50 cost per DALY (the circumstances might change, etc). We can reduce that risk by distributing our donations based on, for example, 1⁄2 and 1⁄3 weights for charity A and B (so based on cost efficiency). That bet is more safe. But we can get even better with donating to all three in a 1⁄2 : 1⁄3 : 1⁄4 ratio.
Given we hand pick a few hundred charities based on cost efficiency, distributing donations among them based on such (or probably more complex) criteria is closer to optimum utility.
I agree that we don’t need to (and usually don’t) play those zero-sum games. The problem is that those zero-sum games are the mechanism for price discovery, and we don’t have market price signals in the charity world.
I agree with your point about diversification reducing risk. This is true for empirical uncertainty and for value uncertainty sometimes. If you have a convex utility function, reducing risk has positive expected value, if not, then no.
With charity we’re looking for global utility. That would be analogous to total market expansion in the for-profit world. When someone arbitrages a pricing failure and gains some, someone else loses the same amount. That’s a zero-sum game around the market expansion line. We don’t have to take that into account here, there’s likely no speculation around charity utility.
But say charity A has a track record of $20 cost per DALY, charity B has $30, charity C has $40. As we don’t know the future, hand picking charity A and giving all our donations to them would be a mistake—maybe they will not be that efficient next year, maybe they will operate on $50 cost per DALY (the circumstances might change, etc). We can reduce that risk by distributing our donations based on, for example, 1⁄2 and 1⁄3 weights for charity A and B (so based on cost efficiency). That bet is more safe. But we can get even better with donating to all three in a 1⁄2 : 1⁄3 : 1⁄4 ratio.
Given we hand pick a few hundred charities based on cost efficiency, distributing donations among them based on such (or probably more complex) criteria is closer to optimum utility.
I agree that we don’t need to (and usually don’t) play those zero-sum games. The problem is that those zero-sum games are the mechanism for price discovery, and we don’t have market price signals in the charity world.
I agree with your point about diversification reducing risk. This is true for empirical uncertainty and for value uncertainty sometimes. If you have a convex utility function, reducing risk has positive expected value, if not, then no.