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.