What are selfish lifestyle reasons to work on the WIT team?
Is it fair to say the work WIT does is unusual outside of academia? What are closely related organizations that tackle similar problems?
How does your team define “good enough” for a sequence? What adjustments do you make when you fall behind schedule? Cutting individual posts? Shortening posts? Spending more time?
How much does the direction of a sequence change as you’re writing it? It seems like you have a vision in mind when starting out, but you also mention being surprised by some results.
Can you tell us more about the structure of research meetings? How frequently do individual authors chat with each other and for what reason? In particular, the CURVE sequence feels very intentionally like a celebration of different “EA methodologies”. Most of the posts feel individual before converging on a big cost-effectiveness analysis.
Much of your work is numerical simulation over discrete choices. Have there been attempts to define more “closed-form” analytical equations? What are pros and cons here?
What are selfish lifestyle reasons to work on the WIT team?
It’s fun to talk to smart people! Remote work is great. It’s a privilege to be able to think about big problems that are both philosophically complicated and practically important.
Is it fair to say the work WIT does is unusual outside of academia? What are closely related organizations that tackle similar problems?
Yes, what we do is very unusual outside of academia—and inside it too. Re: other groups that do global priorities research, the most prominent ones are GPI, PWI, and the cause prio teams at OP.
How does your team define “good enough” for a sequence? What adjustments do you make when you fall behind schedule? Cutting individual posts? Shortening posts? Spending more time?
That’s a hard one and we’re still trying to figure it out. There are a lot of variables here, many of which are linked to whether we have the funding to linger on a particular project. In general, however, our job isn’t to produce academic research: it’s to inform decisions. So, if we think we’ve done enough to help people who need to make decisions, then that’s a good sign that we should wrap up the project soon.
How much does the direction of a sequence change as you’re writing it? It seems like you have a vision in mind when starting out, but you also mention being surprised by some results.
The general structure tends not to change much—we plan out posts together and have a general sense of the research we want to do—but the narrative certainly evolves as we learn more about the topic we’re investigating. The conclusions definitely aren’t set from the beginning!
Can you tell us more about the structure of research meetings? How frequently do individual authors chat with each other and for what reason? In particular, the CURVE sequence feels very intentionally like a celebration of different “EA methodologies”. Most of the posts feel individual before converging on a big cost-effectiveness analysis.
We’re in touch all the time, brainstorming new ideas, reviewing drafts, and figuring out solutions to problems. The whole team meets once or twice a week and then we individually hop on 1-1 calls more frequently to discuss specific aspects of our projects. Most of the research still has a lead who’s driving it forward, but everyone’s fingerprints tend to be on everything.
Much of your work feels numerical simulation over discrete choices. Have there been attempts to define “closed-form” analytical equations for your work? What are reasons to allocate resources to this versus not?
This ties to your previous question “How does your team define “good enough” for a sequence?”. We think analytical equations can be valuable (they are often tidier, speed up computational work, and can provide clearer insights into sensitivity analysis). For example, it’s a natural next step in our human extinction post, which we flagged in the conclusion. And indeed we’ve done some work towards this already but not polished it enough for it to be in a shareable state. Back to your question “when is a piece of research good enough to wrap up?” We don’t know for sure, but we’ve found that running computational simulations that we’re sufficiently confident in gives us approximations that are perfectly suitable to learn about the models we’re interested in. We hear you, closed-form solutions are mathematically satisfying. But, once we’ve learned the main headlines, it’s hard to justify spending the extra time working through closed-form solutions for everything, especially for some of the more complex models with several moving parts.
What are the main constraints the WIT team faces?
The standard ones: we’re funding- and capacity-constrained. We could do a lot more with additional resources!
What are selfish lifestyle reasons to work on the WIT team?
Is it fair to say the work WIT does is unusual outside of academia? What are closely related organizations that tackle similar problems?
How does your team define “good enough” for a sequence? What adjustments do you make when you fall behind schedule? Cutting individual posts? Shortening posts? Spending more time?
How much does the direction of a sequence change as you’re writing it? It seems like you have a vision in mind when starting out, but you also mention being surprised by some results.
Can you tell us more about the structure of research meetings? How frequently do individual authors chat with each other and for what reason? In particular, the CURVE sequence feels very intentionally like a celebration of different “EA methodologies”. Most of the posts feel individual before converging on a big cost-effectiveness analysis.
Much of your work is numerical simulation over discrete choices. Have there been attempts to define more “closed-form” analytical equations? What are pros and cons here?
What are the main constraints the WIT team faces?
What are selfish lifestyle reasons to work on the WIT team?
It’s fun to talk to smart people! Remote work is great. It’s a privilege to be able to think about big problems that are both philosophically complicated and practically important.
Is it fair to say the work WIT does is unusual outside of academia? What are closely related organizations that tackle similar problems?
Yes, what we do is very unusual outside of academia—and inside it too. Re: other groups that do global priorities research, the most prominent ones are GPI, PWI, and the cause prio teams at OP.
How does your team define “good enough” for a sequence? What adjustments do you make when you fall behind schedule? Cutting individual posts? Shortening posts? Spending more time?
That’s a hard one and we’re still trying to figure it out. There are a lot of variables here, many of which are linked to whether we have the funding to linger on a particular project. In general, however, our job isn’t to produce academic research: it’s to inform decisions. So, if we think we’ve done enough to help people who need to make decisions, then that’s a good sign that we should wrap up the project soon.
How much does the direction of a sequence change as you’re writing it? It seems like you have a vision in mind when starting out, but you also mention being surprised by some results.
The general structure tends not to change much—we plan out posts together and have a general sense of the research we want to do—but the narrative certainly evolves as we learn more about the topic we’re investigating. The conclusions definitely aren’t set from the beginning!
Can you tell us more about the structure of research meetings? How frequently do individual authors chat with each other and for what reason? In particular, the CURVE sequence feels very intentionally like a celebration of different “EA methodologies”. Most of the posts feel individual before converging on a big cost-effectiveness analysis.
We’re in touch all the time, brainstorming new ideas, reviewing drafts, and figuring out solutions to problems. The whole team meets once or twice a week and then we individually hop on 1-1 calls more frequently to discuss specific aspects of our projects. Most of the research still has a lead who’s driving it forward, but everyone’s fingerprints tend to be on everything.
Much of your work feels numerical simulation over discrete choices. Have there been attempts to define “closed-form” analytical equations for your work? What are reasons to allocate resources to this versus not?
This ties to your previous question “How does your team define “good enough” for a sequence?”. We think analytical equations can be valuable (they are often tidier, speed up computational work, and can provide clearer insights into sensitivity analysis). For example, it’s a natural next step in our human extinction post, which we flagged in the conclusion. And indeed we’ve done some work towards this already but not polished it enough for it to be in a shareable state. Back to your question “when is a piece of research good enough to wrap up?” We don’t know for sure, but we’ve found that running computational simulations that we’re sufficiently confident in gives us approximations that are perfectly suitable to learn about the models we’re interested in. We hear you, closed-form solutions are mathematically satisfying. But, once we’ve learned the main headlines, it’s hard to justify spending the extra time working through closed-form solutions for everything, especially for some of the more complex models with several moving parts.
What are the main constraints the WIT team faces?
The standard ones: we’re funding- and capacity-constrained. We could do a lot more with additional resources!