The point about ongoing work seems fair enough!
On Gather Town: My problem with it is that to me Gather Town is too much fun, you are right it is a warmer place and more fun place, but I want to spend my time on a service like this working, not having casual chats with EAs, racing on a racetrack or just exploring the space!
Im missing two of my personal top recommendations here:
The EA Focusmate group. I have made some good connections there, somehow I feel it works better if I am matched with someone from this community.
Confido.institute . A forecasting tool for during meetings or for within organisation! Potentially makes meetings faster and it is a simple way to get some groups forecasts.
It might be helpful to have a list of these articles on a separate website somewhere, could be a good resource to link to if it is unconnected to EA / LW.
I don’t necessarily disagree but from an organizational perspective: Is this not the sort of thing we have a wiki for?
I think adversarial collaborations are very interesting, so I am curious to hear if anyone has done any work on how we can make this technique scale a bit more? Such as writing a good manual for how to do this?
Thank you for answering! It is reassuring to know that previous attendance is taken into account.
For the upcoming Pentathlon this January: Is there anything I can do to increase the odds of being teamed up with fellow-EAs if I am not working for an EA org?
I feel like there is a lot of content about getting the most out of an EAG on the forum, but not that much on who should go (again). I understand that this is going to depend a lot on personal circumstances, but I am wondering whether there are any guidelines / tips on who should apply to EAGs? And how many EAGs are too many?
I have never gone to an EAG and felt I wasted my time, and have always come back with new valuable connections, but obviously going to all EAGs is too much, and you are taking away a spot from someone else who might need it more. Deciding where to draw the line is something I am struggling with a bit.
Hi Lizka,
Thank you for your response! I will contact OWID about this as well, that seems like a great idea!
On your sixth point: I am sorry for not explaining it well initially, my concern would be something like this:
A government opens a forecasting questions on whether it will achieve its emissions target for 2030 (Or a target for anything else).
Forecasters in aggregate predict that there is only an 5% chance of success.
This is seen as unacceptably low by policy-makers and new policy is announced and implemented.
Forecasters adjust and now think there is a 60% chance of success.
This happens several times.
Smart forecasters now understand that low aggregate forecasts will result in new policy initiatives, so a good strategy would be to consistently predict higher chances of success than their true belief under current policy.
I think this is roughly similar to the concern you expressed here under “Causality might diverge from conditionality”.
And of course I also doubt there are currently any governments responding enough to a prediction market / forecasting tournament for this to become a problem, but I am hoping that in future we might see a lot more government interest.
Hi all!
Nice to see that there is now a sub-forum dedicated to Forecasting, this seems like a good place to ask what might be a silly question.
I am doing some work on integrating forecasting with government decision making. There are several roadblocks to this, but one of them is generating good questions (See Rigor-Relevance trade-off among other things).
One way to avoid this might be to simple ask questions about the targets the government has already set for itself, a lot of these are formulated in a SMART [1] way and are thus pretty forecastable. Forecasts on whether the government will reach its target also seem like they will be immediately actionable for decision makers. This seemed like a decent strategy to me, but I think I have not seen them mentioned very often. So my question is simple: Is there some sort of major problem here I am overlooking?
The one major problem I could think of is that there might be an incentive for a sort of circular reasoning: If forecasters in aggregate think that the government might not be on its way to achieve a certain target then the gov might announce new policy to remedy the situation. Smart Forecasters might see this coming and start their initial forecast higher.
I think you can balance this by having forecasters forecast on intermediate targets as well. For example: Most countries have international obligations to reduce their CO2 emissions by X% by 2030, instead of just forecasting the 2030 target you could forecasts on all the intermediate years as well.
SMART stands for: Specific, Measurable, Assignable, Realistic, Time-related—See https://en.wikipedia.org/wiki/SMART_criteria
I think the websites look amazing!
It did give me a question: Is anyone doing focus groups or user interviews on these kinds of projects? I think this looks good, but I am already convinced that WAW is worth thinking about, so I am not really the target audience, but maybe the website is not so convincing for people who are not?
I think I agree that democratizing workplaces is a good idea, and I think it is an interesting argument that this is potentially effective because so many people spend so much time at the workplace, nevertheless I would guess that spending or working on this does not come close to the cost-effectiveness of EA charities, though I have not done the math and would love to see someone do an initial exploration of this.
On a slightly related note: I am always a bit surprised by this (co-op, worker empowerment ect.) movement’s focus on creating more co-ops or buying increasing shares of companies. It seems to me that if worker ownership is a good thing, then I think the smart political play would be to just keep strengthening co-determination laws until co-ops and other companies are basically identical. After all, creating new laws or convincing the state or others to spend loads of money is a lot more difficult then continually pushing to incrementally change existing laws.
I just wanted to say that this essay was really important for me getting into EA a few years ago, it really resonated with me emotionally in a way that little else did. So wanted to thank you for writing this.
I know this is not how EA is defined for most people, but I often think of EA as recognizing that we are always doing triage.
What caused the rather significant drop in the amount of funding for animal welfare?
I understand if they might be private in nature, but would you (or anyone else) be willing to share more of your policies?
I found the examples in this post very helpful, but I think I could benefit from hearing even more concrete ideas.
Just letting you know that the form link is not working for me :)
This seems like a very important issue and I am very happy you are looking into it!
Did you ever publish this anywhere? I see that this comment was placed a LONG time ago but I would still be interested in a really good summary of this work.
Thank you! :)
I hate to be this person, but is there an epub version available?
What do you think is the most common mistake people make when searching for a career that has a positive societal impact?