Dan Wahl
Hey Pat, thanks for the heads up. You’re right that, despite working on desktop and via the LinkedIn mobile app, the search link doesn’t seem to work on mobile browsers.
One quick workaround is to request the desktop site on the mobile browser, which seems to load properly on my side.
Excited to see this is returning for another year! A few notes:
- This year’s match is (currently) “only” for up to $50,000 (for reference, last year a total of $620K was matched), and might not last very long
- See e.g. my Every.org profile for a list of ~75 EA-aligned orgs on the site (as of Nov. 2021)
- Note that you can fund your Every.org account straight from your DAF
- Here’s last year’s post, with some helpful info in the comments too
Ah! Ctrl+Enter does work in the Playground. I was doing most of my development in VS Code—not sure if it’s also supposed to work there, but I don’t see it in the keybindings.json.
Re: settings persistence in Playground, do they also come along with the share links? The critical ones for me would be Sample Count and the Function Display Settings.
Looking forward to auto-formatting as well!
Calculating up to
annually_averted_health_dalys_time_discountedwas taking me well over a minute in v0.3.0, but is down to ~5 seconds in v0.3.1--a big improvement!
I originally had to comment the actual model output (dollars_per_daly_equivalents_averted(20)) because it wouldn’t return at all in v0.3.0, but now it’s ~2 mins in v0.3.1.
For reference, the whole Causal model takes ~5 seconds to update.
One such project, already underway, is our work on interspecies comparisons of moral weight.
FYI this link gives me an “Access Denied” error.
This is interesting, thanks for writing it up! I recently did an analysis of US cities (mostly looking for a wintering location, not a full move), and Tulsa ended up scoring relatively low, which was disappointing since I know there’s a growing EA community there.
I’m really curious in your biking experience in particular, since that’s the category where it fared the worst. I looked at bike commuter data, but I guess that’s just a proxy for good commuter infrastructure, which is what I probably care about. Why do you think so few Tulsans bike at the moment?
I’ve been thinking about relocation recently too, though mostly through the lens of finding a better wintering location in the US. This post inspired me to at least upload (if not exactly document) my analysis to date. See here:
https://github.com/danwahl/schelling-out/blob/main/schelling-out.ipynb
And the current top 10:
biking housing vegan winter summer total City State Berkeley California 2.142016 -2.166879 2.770248 3.772590 0.0 1.303595 Gainesville Florida 1.117079 0.854543 0.780399 3.384664 -0.0 1.227337 Tempe Arizona 1.329176 -0.184675 1.161235 3.508773 -0.0 1.162902 Portland Oregon 1.999723 -0.720326 2.868659 1.473931 0.0 1.124397 New Orleans Louisiana 0.739080 0.053609 1.451345 3.197368 -0.0 1.088280 Hollywood Florida -0.717470 -0.222100 2.601399 3.434937 -0.0 1.019353 Boulder Colorado 2.702441 -1.017822 0.899562 2.222392 -0.0 0.961315 Cambridge Massachusetts 2.433792 -1.559463 1.255650 2.648835 -0.0 0.955763 Orlando Florida -1.171810 0.527952 1.594634 3.601198 -0.0 0.910395 St. Petersburg Florida -0.573223 0.002294 1.410371 3.478732 -0.0 0.863635(Scores are
log2where0is Chicago, andtotalis the average of each row.)Of the factors mentioned above, this focuses almost entirely on (my) “Personal fit” via considering things like weather, bike-ability, vegan-friendliness, etc. But I’m also keen to explore the “Coordination with other EAs working on shared cause areas and projects” and “Opportunities for movement-building in non-saturated EA hubs” points via new community Schelling points (hence the name).
Originally I made a digital SSC podcast (feed) so that I could listen through the back catalog of posts (the human reader version didn’t start until ~2017). I ended up getting used to the robot narrator, so I just kept it running on ACX. One small upside is that the digital versions get created within minutes of new posts.
This is awesome! I did something similar for Astral Codex Ten (feed, post) a while back. The human version is also good, if you like that kind of thing.
Here’s a sheet with a bunch of EA(-adjacent) tech initiatives: https://docs.google.com/spreadsheets/d/1yzmg02j8PnvjlV_KX3Vf_u9TNdAS63N-9xy2txmTg-A/edit?usp=sharing
Along these lines, preventing childhood lead poisoning is another potential candidate.
Thanks for this (somewhat overwhelming!) analysis. I tried to do something similar a few years back, and am pretty enthusiastic about the idea of incorporating more uncertainty analysis into cost effectiveness estimates, generally.
One thing (that I don’t think you mentioned, though I’m still working through the whole post) this allows you to do is use techniques from Modern Portfolio Theory to create giving portfolios with similar altruistic returns and lower downside risk. I’d be curious to see if your analysis could be used in a similar way.
I’ve done a little work on this, using techniques from modern portfolio theory, and uncertainty estimates from GiveWell and ACE to generate optimal charity portfolios. See here for a background post, and here for my 2016 update.
Please consider using
starscore (or approval) voting next year instead of RCV