As a different phrasing of Michael’s question on forecasting, do EAIF grantmakers have implicit distributions of possible outcomes in their minds when making a grant, either a) in general, or b) for specific grants?
If so, what shape does those distributions (usually) look like? (an example of what I mean is “~log-normal minus a constant” or “90% of the time, ~0, 10% of the time, ~power law”)
If not, are your approaches usually more quantitative (eg explicit cost-effectiveness models) or more qualitative/intuitive (eg more heuristic-based and verbal-argument driven)?
I think I often have an implicit intuition about something like “how heavy-tailed is this grant?”. But I also think most grants I’m excited about are either at least somewhat heavy-tailed or aimed at generating information for a decision about a (potentially heavy-tailed) future grant, so this selection effect will reduce differences between grants along that dimension.
But I think for less than 1⁄10 of the grants I think about I will have any explicit quantitative specification of the distribution in mind. (And if I have it will be rougher than a full distribution, e.g. a single “x% of no impact” intuition.)
Generally I think our approaches are more often qualitative/intuitive than quantitative. There are rare exceptions, e.g. for the children’s book grant I made a crappy cost-effectiveness back-of-the-envelope calculation just to check if the grant seemed like a non-starter based on this. As far as I remember, that was the only such case this round.
Sometimes we will discuss specific quantitative figures, e.g., the amount of donations a fundraising org might raise within a year. But our approach for determining these figures will then in turn usually be qualitative/intuitive rather than based on a full-blown quantitative model.
As a different phrasing of Michael’s question on forecasting, do EAIF grantmakers have implicit distributions of possible outcomes in their minds when making a grant, either a) in general, or b) for specific grants?
If so, what shape does those distributions (usually) look like? (an example of what I mean is “~log-normal minus a constant” or “90% of the time, ~0, 10% of the time, ~power law”)
If not, are your approaches usually more quantitative (eg explicit cost-effectiveness models) or more qualitative/intuitive (eg more heuristic-based and verbal-argument driven)?
I think I often have an implicit intuition about something like “how heavy-tailed is this grant?”. But I also think most grants I’m excited about are either at least somewhat heavy-tailed or aimed at generating information for a decision about a (potentially heavy-tailed) future grant, so this selection effect will reduce differences between grants along that dimension.
But I think for less than 1⁄10 of the grants I think about I will have any explicit quantitative specification of the distribution in mind. (And if I have it will be rougher than a full distribution, e.g. a single “x% of no impact” intuition.)
Generally I think our approaches are more often qualitative/intuitive than quantitative. There are rare exceptions, e.g. for the children’s book grant I made a crappy cost-effectiveness back-of-the-envelope calculation just to check if the grant seemed like a non-starter based on this. As far as I remember, that was the only such case this round.
Sometimes we will discuss specific quantitative figures, e.g., the amount of donations a fundraising org might raise within a year. But our approach for determining these figures will then in turn usually be qualitative/intuitive rather than based on a full-blown quantitative model.