Forecasting could use more gender diversity
The new org I’m part of, the Forecasting Research Institute, recently began a hiring round, and we noticed that around 80% of research analyst applicants were male. This isn’t terribly surprising—a large percentage of forecasting tournament participants have been male historically, so we expected familiarity and interest in forecasting to be male-skewed currently. But, as a woman in forecasting myself, it does bug me.
It bugs me on several levels. One is puzzlement: many of the fields most relevant to forecasting and forecasting research—like international relations, history, social science, and, in the case of forecasting research, behavioral science—are not short of women. (And in any case, forecasting is so interdisciplinary that most backgrounds have some value to add.) There’s also nothing about the broad spectrum of potential uses of forecasting, in policy, public discourse, institutional decision-making, philanthropy, etc., that suggests its impact is limited to particularly male domains.
Which brings me to the next point. If the field of forecasting is systematically failing to attract roughly half of the population, containing roughly half of the talent, good ideas, valuable skills and knowledge, it’s made poorer and will likely face an uphill climb toward relevance and real-world impact. Which would be a shame, given its great potential.
So if you’re a woman reading this (or another sort of person who feels underrepresented in forecasting) and you think there’s even a chance you might be interested in doing forecasting research, here’s my case for why you should apply to FRI’s research analyst position:
You don’t need to be an experienced forecaster, or like doing forecasting. You don’t need to be super quantitative. You don’t need to be intimately familiar with forecasting research—for the right candidate, as long as you’re curious, motivated, and can get excited about unsolved problems in forecasting, FRI is willing to train you up.
Forecasting is an early-stage field with high impact potential, and the opportunity to be part of building such a field is kind of rare.
This also means there are tons of interesting open questions we need to answer, and lots of room to be creative in finding the answers.
In terms of subject matter, the work is extremely varied—so if you’re, say, interested in bio and AI and public policy, you might get to touch on all of them at the same time, or in quick succession. Some of our projects will also allow you to interact with leading domain experts or prominent organizations doing cool work when we partner to test forecasting tools.
If you want to use it this way, forecasting research can be a great facilitator for working on your own epistemics, and can provide a framework for interpreting and filtering the many important things one can have views about in a relatively independent way. (This is one of the primary motivations of my colleague Josh.)
Not only is everyone on our team enthusiastic about improving gender diversity in our org, and in the forecasting space generally, they’re also just extremely nice, interesting, competent and thoughtful as people.
If you’re still not sure if you could get sufficiently interested in forecasting for applying to be worth it, I’d suggest checking out our research page to see the projects we’ll be working on in the near future. But to give you a sense of how diverse the traits of people who’ve gotten into forecasting research are (all on the FRI team):
I don’t have a “formal” background of any sort: I dropped out of college, and subsequently hopped around doing stuff like writing and generalist research. I then got into “caring about AI” via compelling arguments from friends, one of whom gave me a job doing AI forecasting. From there I started getting interested in more general questions in the science of forecasting.
Some traits that feel related to my interest in forecasting: I have a strong amateur interest in history, I’m drawn to working in relatively uncharted territory, and I’m an obsessive player of strategy games (like Civilization and Europa Universalis).
As for the rest of the team, one of us has a philosophy background, and was at GiveWell for years; we also have an economist, a former lawyer, a political science grad, and, well, Phil Tetlock. (It feels hubristic to try and encapsulate Phil in a single domain.)
A few of the things that drew my teammates to forecasting include: finding the world confusing, and feeling that people generally underestimate levels of uncertainty; an interest in economic forecasting; a love of the Red Sox (and sports forecasting more generally); a desire to cut through the noise of typical communication norms; favoring mistake theory over conflict theory
I really do think increased diversity will improve the field of forecasting, but on a selfish note, I’d also just like to see more people like me (women) working in it. And I think a lot of people might be missing out on something great due simply to founder effects or [insert your preferred explanation for forecasting’s gender ratio here]. So go on: apply.
Thanks for writing this, I think it’s really important and well expressed. Diversity of thought and experiences should be valuable .
I will say as a woman who was skeptical about her personal fit regarding forecasting I found playing around Manilfold Markets and trying to make some forecasts helpful and the community there is friendly. So if anyone wants a easy way to just try for themselves what forecasting might look like head to: https://manifold.markets/
+1, thanks for writing this. The male/female ratio seems unusually bad around forecasting for some reason. It seems clearly suboptimal, I’m really curious how to best change that.
In the past, comments like Tegan’s post have been useful for getting me to apply to things.
Another thing that I liked was when a job post had a comment at the bottom saying that women and minorities are less likely to feel qualified to apply to things, mentioning something along the lines of this https://hbr.org/2014/08/why-women-dont-apply-for-jobs-unless-theyre-100-qualified and encouraging them to apply.
Just conjecturing here but I think one reason the ratio is worse than it has to be is perhaps because EA jobs are usually somewhat atypical so it is difficult to figure out if you’re actually qualified (compared to more “normal” jobs) which makes people who have a tendency to feel underqualified even less likely to apply. Plus because the community is small and a lot of people hear about opportunities / get encouraged to apply to things via people in their social networks, and people are less likely to have these social connections if they are from a currently underrepresented group.
I also think mentorship programs are helpful. One-off or series of calls with a more experienced person are helpful for promising people (regardless of gender or other demographics) who don’t have friends or social connections already in a field to figure out how to enter it.
This is just speculation, but maybe this is also why women are less likely to participate in forecasting tournaments? It does take a certain level of confidence / arrogance to see a question about e.g. NATO expansion and think “Yeah, of course I can come up with a prediction on that”.
I think it is very good that FRI is looking to create a diverse team, but I also think that forecasting has a pipeline problem where the participants in tournaments seem to be overwhelmingly male. Maybe they are also the kind of organization that could do some work on figuring out why this is the case and what we can do to solve this?
I don’t think participants in the Good Judgment tournaments that IARPA sponsored back in the day were overwhemingly male. From memory, women were about 30% of the forecaster pool, which isn’t too bad, and really quite good when you compare to other nerdy online things like editing Wikipedia.
“women were about 30% of the forecaster pool” and “80% of research analyst applicants were male” aren’t very far apart, especially since the former is from memory and the latter is a small sample.
(I did a bit of looking trying to find the gender breakdown of Good Judgement Project volunteers, without success)
I am a woman who could be very much interested in the role. But the lack of an upper bound for compensation is putting me off a bit, it might help to include that.
On average I’d expect more men to be put off by this than women though!
Could you comment a bit more about why this is? Are you concerned it might be high or low? It seems plausible to me they might not really have an upper bound.
Most of the time where an upper bound is mentioned in job ads (e.g. LinkedIn) it’s less than <1.5 times the lower bound. So I’m implicitly assuming the upper bound, though not mentioned, will be in the same ballpark.
Perhaps this is wrong and I’m supposed to interpret no upper bound as ‘very negotiable, potentially the sky is the limit’. But that possibility didn’t occur to me until you mentioned it.
I do interpret no range at all as a plausible ‘sky is the limit’ though.
FWIW, CEA solves this issue by saying:
Yes. One way to do marginally better than the community on Metaculus is to realise that while they’re pretty sensible, they also tend to have similar biases/interests to other ‘extremely online’ communities. I suspect part of that correlates with male-heavy demographics.