I’m in favor of reducing the complexity of the framework, but I’m not sure if this is the right way to do it. In particular, estimating “importance only” or “importance and tractability only” isn’t helpful, because all three factors are necessary for calculating MU/$. A cause that scores high on I and T could be low MU/$ overall, due to being highly crowded. Or is your argument that the variance (across causes) in crowdedness is negligible, and therefore we don’t need to account for diminishing returns in practice?
My argument is about the later; the variances decrease in size from I to T to C. The unit analysis still works because the other parts are still implicitly there but treated as constants when dropped from the framework.
I guess I’m expecting diminishing returns to be an important factor in practice, so I wouldn’t place much weight on an analysis that excludes crowdedness.
Hi Justin, thanks for the comment.
I’m in favor of reducing the complexity of the framework, but I’m not sure if this is the right way to do it. In particular, estimating “importance only” or “importance and tractability only” isn’t helpful, because all three factors are necessary for calculating MU/$. A cause that scores high on I and T could be low MU/$ overall, due to being highly crowded. Or is your argument that the variance (across causes) in crowdedness is negligible, and therefore we don’t need to account for diminishing returns in practice?
My argument is about the later; the variances decrease in size from I to T to C. The unit analysis still works because the other parts are still implicitly there but treated as constants when dropped from the framework.
I guess I’m expecting diminishing returns to be an important factor in practice, so I wouldn’t place much weight on an analysis that excludes crowdedness.