This was a super fascinating read ā Iām curating. Since reading, Iāve thought a lot about the interesting discussion of the behavior in the limit of many participants.
One sense I had while reading this was, like, Iām not sure what I should be comparing this to. I feel like āreport utility functionsā is not really a workable solution. I guess I feel like Iām less compelled by analyses of optimal performance and more like, what system will work in practice. I would be interested in more follow-ups about āhere are some realistic situation where this performs more poorly than this other method.ā
Thatās not what this post though, itās describing a very rigorous analyses, and was a great read. Thanks for writing it!
I guess I feel like Iām less compelled by analyses of optimal performance and more like, what system will work in practice.
My impression is that these theoretical properties are the main reason why people are excited about QF. For example, you would prefer it over 1:1 donation matching because it is a more āprincipledā matching rule, which should lead to an allocation thatās closer to the efficient level than 1:1 donation matching. So if not for these properties, I donāt see why people should expect this mechanism to work particularly well in practice.
More generally, I agree that full on efficiency shouldnāt be thought of as a strictly necessary condition mechanisms to be useful in practice. For example, majority voting works relatively well in practice, despite not being efficient. But efficiency is nevertheless still a central concept, as it is the very motivation behind the public goods provision property (itās basically the only problem with providing public goods privately). The framing I would use here is that increasing efficiency, while satisfying other (context-dependent) considerations, should be considered a key goal of public good provision mechanisms.
Iām a big fan of increasing efficiency. But I guess I would guess that proving optimality under certain conditions is not the best way to go about designing a system to be efficient? Itās probably part of the process for sure. (Epistemic status, very unsure here.)
The correspondence between theoretical and practical efficiency is definitely not perfect. Theoretical efficiency guarantees that individuals are properly incentivized. Practical efficiency may not follow because of things like computational costs, and the extent to which this will be a problem will depend on the specific mechanism and the situation in question. For example, in the computational cost case, the actions of large companies would probably be closer to optimal behavior than individual actions.
My hunch would be that proving theoretical efficiency is generally a relatively good proxy for practical efficiency in most cases, but these other practical considerations should be considered in addition to it, as further constraints that one is trying to satisfy. But this is an empirical question, and Iām also relatively uncertain here.
This was a super fascinating read ā Iām curating. Since reading, Iāve thought a lot about the interesting discussion of the behavior in the limit of many participants.
One sense I had while reading this was, like, Iām not sure what I should be comparing this to. I feel like āreport utility functionsā is not really a workable solution. I guess I feel like Iām less compelled by analyses of optimal performance and more like, what system will work in practice. I would be interested in more follow-ups about āhere are some realistic situation where this performs more poorly than this other method.ā
Thatās not what this post though, itās describing a very rigorous analyses, and was a great read. Thanks for writing it!
Thanks, JP!
My impression is that these theoretical properties are the main reason why people are excited about QF. For example, you would prefer it over 1:1 donation matching because it is a more āprincipledā matching rule, which should lead to an allocation thatās closer to the efficient level than 1:1 donation matching. So if not for these properties, I donāt see why people should expect this mechanism to work particularly well in practice.
More generally, I agree that full on efficiency shouldnāt be thought of as a strictly necessary condition mechanisms to be useful in practice. For example, majority voting works relatively well in practice, despite not being efficient. But efficiency is nevertheless still a central concept, as it is the very motivation behind the public goods provision property (itās basically the only problem with providing public goods privately). The framing I would use here is that increasing efficiency, while satisfying other (context-dependent) considerations, should be considered a key goal of public good provision mechanisms.
Iām a big fan of increasing efficiency. But I guess I would guess that proving optimality under certain conditions is not the best way to go about designing a system to be efficient? Itās probably part of the process for sure. (Epistemic status, very unsure here.)
Great point!
The correspondence between theoretical and practical efficiency is definitely not perfect. Theoretical efficiency guarantees that individuals are properly incentivized. Practical efficiency may not follow because of things like computational costs, and the extent to which this will be a problem will depend on the specific mechanism and the situation in question. For example, in the computational cost case, the actions of large companies would probably be closer to optimal behavior than individual actions.
My hunch would be that proving theoretical efficiency is generally a relatively good proxy for practical efficiency in most cases, but these other practical considerations should be considered in addition to it, as further constraints that one is trying to satisfy. But this is an empirical question, and Iām also relatively uncertain here.