How is that in conflict with my point? As superforecasters spend more time talking and sharing information with one another, maybe they have already incorporated extremising into their own forecasts.
Doesn’t this clearly demonstrate that the superforecasters are not using modest epistemology? At best, this shows that you can improve upon a “non-modest” epistemology by aggregating them together, but does not argue against the original post.
Imagine two epistemic peers estimating the weighting of a coin. They start with their probabilities bunched around 50% because they have been told the coin will probably be close to fair. They both see the same number of flips, and then reveal their estimates of the weighting. Both give an estimate of p=0.7. A modest person, who correctly weights the other person’s estimates as equally as informative as their own, will now offer a number quite a bit higher than 0.7, which takes into account the equal information both of them has to pull them away from their prior.
This is what I’m talking about when I say “jut so stories” about the data from the GJP. One explanation is that superforecasters are going through this thought process, another would be that they discard non-superforecasters’ knowledge, and therefore end up as more extreme without explicitly running the extremizing algorithm on their own forecasts.
Similarly, the existence of super-forecasters themselves argues for a non-modest epistemology, while the fact that the extremized aggregation beats the superforecasters may argue for somewhat of a more modest epistemology. Saying that the data here points one way or the other to my mind is cherrypicking.
One option we were looking to use at Verity is the ‘contest’ model—In which an interested party can subsidize a particular question, and then split the pool between forcasters based on their reputation/score after the outcome has come to pass. This helps to subsidize specific predictions, rather than subsidizing more general predictions when paying people for their overall score. It has similarities to the subsidized prediction market model as well.
I worked on this problem for a few years and agree that it’s a bottleneck just in EA, but globally. I do think that the work on prediction is one potential “solution”, but there are additional problems with getting people to actually adopt solutions. The incentives for the people in power to change to a solution that gives them less power is low, and there are lots of evolutionary pressures that lead to the current vetting procedures. I’d love to talk more to you about this as I’m working on similar things, although have moved away from this exact problem.
I actually looked at standard categories but AFAICT there is no standard. Knightian uncertainty and statistical uncertainty are one standard that are almost synonymous with epistemic and aleatory, which are fairly synonymous with model uncertainty and base uncertainty, hence my use of knightian. However, those definitions don’t include the difference between transparent and opaque risk mentioned above.
The definitions of risk and uncertainty seemed to be defined many different places, and frequently the definitions are swapped in different sources. Ignorance and uncertainty are sometimes seen as synonymous, sometimes not.
Basically, I created my own terms because I thought the current terms were muddied enough that using them would create more confusion than clarity. I used existing terms for solutions to different types of risk because the opposite was true.
One place where I didn’t look as hard to find existing categories is the “Types of knightian risk” part. I couldn’t find any existing breakdowns of this and as far as I know it’s original, but there may be an existing list and I was simply using the wrong search terms.
This is great, thanks for sharing!
Interesting, thanks!
Edit: I’ve now updated the post.
Fair – an implicit assumption of my post is that markets are efficient. If you don’t think so, then what I had to say is probably not very relevant.
I assume you’re not arguing for the strong EMH here (markets are maximally efficient), so the difference to me seems to be a difference of degree than kind (you think hiring markets are more efficient than Peter does, Peter thinks hiring markets are less efficient than you do.)
If you are arguing for the strong version of EMH here I’d be curious as to your reasoning, as I can’t think of any credible economists who think that real world markets don’t have any inefficiencies.
If you’re arguing for a weaker version, I think it’s worth digging in to cruxes… Why do you think that the hiring market is more efficient than Peter does?
Blogging/research counts as a project as well, and one that could ultimately do a lot of good. Trying to understand insect sentience is definitely a project, even it’s not billed as one. As is writing a thesis on the philisophical underpinnings of effective altruism. There’s a wider span of “projects that can do good” than “startups that can make money” so the former might not always look the latter.
Studying I would not consider a project, but I still consider the EA hotel a great place for the few who are doing it outside of a project. This sentence explains why:
For EAs who are still looking for projects, it provides a bridge to focus on gaining skills and knowledge while getting chances to join new projects as they circulate through the hotel.
ETA: As the EA hotel gets more applicants, I suspect the distribution will shift towards a few more things that look more like traditional projects, but I still think foundational research and other “weird” projects should be a large consideration.
I’m wary of making this particular post about my project, but happy to talk through private message, on a call, or as comments on the linked document. I’ll probably make a post about project metis on the EA forum at some point to solicit feedback, but not quite yet.
Because buying the hotel was a fixed cost, whereas rent is an ongoing one.
Good point. Either route seems viable. The EA hotel route might be slightly higher EV as the way you’re gaining skills and status has the potential to do a lot of good, but I think probably many ways to cross/fill the chasm are needed.
I’ve seen people need breaks and time off. I think the “Culture of care” goes a long way towards making sure this isn’t the norm though.
Right… or even worse, you’re simply paying someone’s rent and not working towards ownership at all.
If my values say “I should help lots of people”, and I work to maximize my values (which makes my life meaningful) which category does that fit into? Does it matter if I’m doing it “because” it makes my life meaningful, or because it helps other people?
To me that last distinction doesn’t even make a lot of sense—I try to maximize my values BECAUSE they’re my values. Sometimes I think the egoists are just saying “maximize your values” and the altruists are just saying “my values are helping others” and the whole thing is just a framing argument.
Sure, but if one has the value of actually helping other people, that distinction disappears, yes?
As an example of a famous egoist, I think someone like Ayn Rand would say that fooling yourself about your values is doing it wrong.
I think my crux is something like “this is a question to be dissolved, rather than answered”
To me, trying to figure out whether a goal is egoistic or altruistic is like trying to figure out whether a whale is a mammal or a fish—it depends heavily on my framing and why I’m asking the question, and points to two different useful maps that are both correct in different situations, rather than something in the territory.
Another useful map might be something like “is this eudomonic or hedonic egoism” which I think can get less squirrely answers than the “egoic or altruistic” frame. Another useful one might be the “Rational Compassion” frame of “Am I working to rationally optimize the intutions that my feelings give me?”
It’s an interesting just so story about what IARPA has to say about epistemology, but the actual story is much more complicated. For instance, the fact that “Extremizing” works to better calibrate general forecasts, but that extremizing of superforecaster’s predictions makes them worse.
Furthermore, that contrary to what you seem to be claiming about people not being able to outperform others, there are in fact “superforecasters” who out perform the average participant year after year, even if they can’t outperform the aggregate when their forecasts are factored in.