I’m suspicious you can do a good job of predicting ex ante outcomes. After all, that’s what VCs would want to do and they have enormous resources. Their strategy is basically to pick as many plausible winners as they can fund.
I agree that looking at e.g. VC practices is relevant evidence. However, it seems to me that if VCs thought they couldn’t predict anything, they would allocate their capital by a uniform lottery among all applicants, or something like that. I’m not aware of a VC adopting such a strategy (though possible I just haven’t heard of it); to the extent that they can distinguish “plausible” from “implausible” winners, this does suggest some amount of ex-ante predictability. Similarly, my vague impression is that VCs and other investors often specialize by domain/sector, which suggests they think they can utilize their knowledge and network when making decisions ex ante.
Sure, predictability may be “low” in some sense, but I’m not sure we’re saying anything that would commit us to denying this.
Yeah, I’d be interested to know if VC were better than chance. Not quite sure how you would assess this, but probably someone’s tried.
But here’s where it seems relevant. If you want to pick the top 1% of people, as they provide so much of the value, but you can only pick the top 10%, then your efforts to pick are much less cost-effective and you would likely want to rethink how you did it.
I think it’s plausible that VCs aren’t better than chance when choosing between a suitably restricted “population”, i.e. investment opportunities that have passed some bar of “plausibility”.
I don’t think it’s plausible that they are no better than chance simpliciter. In that case I would expect to see a lot of VCs who cut costs by investing literally zero time into assessing investment opportunities and literally fund on a first-come first-serve or lottery basis.
And yes, I totally agree that how well we can predict (rather than just the question whether predictability is zero or nonzero) is relevant in practice.
If the ex-post distribution is heavy-tailed, there are a bunch of subtle considerations here I’d love someone to tease out. For example, if you have a prediction method that is very good for the bottom 90% but biased toward ‘typical’ outcomes, i.e. the median, then you might be better off in expectation to allocate by a lottery over the full population (b/c this gets you the mean, which for heavy-tailed distributions will be much higher than the median).
Data from the IAP indicates that they can identify the top few percent of successful inventions with pretty good accuracy. (Where “success” is a binary variable – not sure how they perform if you measure financial returns.)
I agree that looking at e.g. VC practices is relevant evidence. However, it seems to me that if VCs thought they couldn’t predict anything, they would allocate their capital by a uniform lottery among all applicants, or something like that. I’m not aware of a VC adopting such a strategy (though possible I just haven’t heard of it); to the extent that they can distinguish “plausible” from “implausible” winners, this does suggest some amount of ex-ante predictability. Similarly, my vague impression is that VCs and other investors often specialize by domain/sector, which suggests they think they can utilize their knowledge and network when making decisions ex ante.
Sure, predictability may be “low” in some sense, but I’m not sure we’re saying anything that would commit us to denying this.
Yeah, I’d be interested to know if VC were better than chance. Not quite sure how you would assess this, but probably someone’s tried.
But here’s where it seems relevant. If you want to pick the top 1% of people, as they provide so much of the value, but you can only pick the top 10%, then your efforts to pick are much less cost-effective and you would likely want to rethink how you did it.
I think it’s plausible that VCs aren’t better than chance when choosing between a suitably restricted “population”, i.e. investment opportunities that have passed some bar of “plausibility”.
I don’t think it’s plausible that they are no better than chance simpliciter. In that case I would expect to see a lot of VCs who cut costs by investing literally zero time into assessing investment opportunities and literally fund on a first-come first-serve or lottery basis.
And yes, I totally agree that how well we can predict (rather than just the question whether predictability is zero or nonzero) is relevant in practice.
If the ex-post distribution is heavy-tailed, there are a bunch of subtle considerations here I’d love someone to tease out. For example, if you have a prediction method that is very good for the bottom 90% but biased toward ‘typical’ outcomes, i.e. the median, then you might be better off in expectation to allocate by a lottery over the full population (b/c this gets you the mean, which for heavy-tailed distributions will be much higher than the median).
Data from the IAP indicates that they can identify the top few percent of successful inventions with pretty good accuracy. (Where “success” is a binary variable – not sure how they perform if you measure financial returns.)