Currently working on:
Interested in practical exercises and theoretical considerations related to causal inference, forecasting and prioritization.
The ability to add links in bios would be great!
If we could make it so I can edit my bio like I would edit a post it would be even better.
EDIT: ohh the bio uses markdown, noted.
I run a digital art studio, and some of my work is inspired by Effective Altruism themes and ideas.
Particularly, Shared Identity, Shared Values and Science and Identity borrow heavily from the community.
Why both #announcements and #general? What is the use case for each?
Yes, that is right.
I don't have any recent examples in the EA Forum, but here is an article I wrote in LessWrong where the equations where very annoying to edit.
I expect I occassionally would use larger equations, better formated (with underbraces and such) if it was easier to edit in the WYSIWYG editor.
Thanks to you!
In hindsight, the footnotes was the thing I really wanted so I am a very happy user indeed!
Would be good to be able to switch between editors to do things like eg editing complicated LaTeX (right now its complicated to edit it in the WYIWYG editor). But maybe the more reasonable ask is to make the WYSIWYG equation editor span multiple lines for large equations.
TL;DR: I'd like to have a single board where to see a summary of the analytics for all my posts.
I've been really enjoying the analytics feature!I used it for example to notice that my post on persistence had become very popular, which led me to write a more accessible summary.
One thing I've noticed is that it is very time consuming to track the analytics of each post. That requires me to go to each post, click on analytics and have them load.
I think Medium has a much nicer interface. They have a main user board for stats, from which I can see overall engagement with my writing. It also shows my posts, ordered by recent engagement. I can click on a post to go to the page with the specific stats for that post.
I think this is great, and I'd like to have something similar in the EA forum!
I am sure that if you join the AI Alignment slack , Rob Miles discord server  or ask questions on LW you will find people willing to answer.
Finding a dedicated tutor might be harder, but if you can compensate them for their time. The bountied rationality Facebook group  might be a good place to ask.
I am so excited for this feature!
Finally I will be able to update my posts with real footnotes instead of awkwardly adding them at the end of my posts ^^
Thanks for chipping in Alex!
It's the other way around for me. Historical baseline may be somewhat arbitrary and unreliable, but so is 1:1 odds.
Agreed! To give some nuance to my recommendation, the reason I am hesitant is mainly because of lack of academic precedent (as far as I know).
If the motivation for extremizing is that different forecasters have access to independent sources of information to move them away from a common prior, but that common prior is far from 1:1 odds, then extremizing away from 1:1 odds shouldn't work very well.
Note that the data backs this up! Using "pseudo-historical" odds is quite better than using 1:1 odds. See the appendix for more details.
[...] use past estimates of the same question.[...] use the odds that experts gave it at some point in the past as a baseline with which to interpret more recent odds estimates provided by experts.
[...] use past estimates of the same question.
[...] use the odds that experts gave it at some point in the past as a baseline with which to interpret more recent odds estimates provided by experts.
I'd be interested in seeing the results of such experiments using Metaculus data!
Another possibility is to use two pools of forecasters [...]
This one is trippy, I like it!
UPDATE: Eric Neyman recently wrote about an extra assumption that I believe cleanly cuts into why this example fails.
The assumption is called the weak substitutes condition. Essentially, it means that there are diminishing marginal returns to each forecast.
The Jack, Queen and King example does not satisfy the weak substitutes condition, and forecast aggregation methods do not work well in it.
But I think that when the condition is met we can get often get good results with forecast aggregation. Furthermore I think it is a very reasonable condition to ask, and often met in practice.
I wrote more about Neyman's result here, though I focus more on the implications for extremizing the mean of logodds.