I'm an independent researcher, hobbyist forecaster, programmer, and aspiring effective altruist.
In the past, I've studied Maths and Philosophy, dropped out in exhasperation at the inefficiency; picked up some development economics; helped implement the European Summer Program on Rationality during 2017, 2018 and 2019, and SPARC during 2020; worked as a contractor for various forecasting and programming projects; volunteered for various Effective Altruism organizations, and carried out many independent research projects. In a past life, I also wrote a popular Spanish literature blog, and remain keenly interested in Spanish poetry.
I like to spend my time acquiring deeper models of the world, and a good fraction of my research is available on nunosempere.github.io.
With regards to forecasting, I am LokiOdinevich on GoodJudgementOpen, and Loki on CSET-Foretell, and I have been running a Forecasting Newsletter since April 2020. I also enjoy winning bets against people too confident in their beliefs.
I was a Future of Humanity Institute 2020 Summer Research Fellow, and I'm working on a grant from the Long Term Future Fund to do "independent research on forecasting and optimal paths to improve the long-term." You can share feedback anonymously with me here.
Nice! One other cool thing about the Big List of Cause Candidates is that people have been coming up with suggestions, and I have been updating the list as they do so.
Incidentally, the Big List of Candidates post was selected as a project by using a very rudimentary forecasting/evaluation system, similar to the ones here and here. If you want to participate in that kind of thing by suggesting, carrying out or evaluating potential projects, you can sign up here.
In particular, as a novelty, I assigned a 50% chance that it would in fact get an EA forum prize.
Note that the forecast assumed that I was competing against fewer posts, but also that there would be fewer prizes, so the errors happily cancelled out.
I think that that kind of forecast/comment:
The other posts I thought were particularly strong are:
I correctly guessed My mistakes on the path to impact and "Patient vs urgent longtermism" has little direct bearing on giving now vs later.
This is not a mistake; you'll notice that the string "Hypermind" shares the letters "ermny" with „Germany". Anyways, in this case you might get more relevant results by clicking on "advanced results" and then on "2+ ★" or even "1+ ★", sacrificing some quality for breadth.
Thanks! Sure, I just did. Just search for "Hypermind" to see all of them, or for e.g., "covid-19" to get some results which include questions from Hypermind as well.
What instrumental goals have you pursued successfully?
To the extent that you have "a worldview" (in scare quotes), what is a short summary of that worldview?
I disagree with this. I'm writing this without having looked at the data, but autism / Asperger's syndrome, particularly in their high functioning versions, seems to be underdiagnosed, and it's seems to be a very reasonable inference that at least some of the leaders under discussion were in fact on the autistic spectrum, or otherwise non-neurotypical. We can check this with a Metaculus question if you want.
So for me, the motivation for categorizing altruistic projects into buckets (e.g., classifications of philanthropy) is to notice the opportunities, the gaps, the conceptual holes, the missing buckets. Some examples:
More generally, if you have an organizing principle, you can optimize across that organizing principle. So here in order to be useful, a division of cause areas by some principle doesn't have to be exhaustive, or even good in absolute terms, it just has to allow you to notice an axis of optimization. In practice, I'd also tend to think that having several incomplete categorization schemes among many axis is more useful than having one very complete categorization scheme among one axis.
"What are the top national/world priorities" is usually so complex, that it will remain to be a mostly subjective judgment. Then, how else would you resolve it than by looking for some kind of future consensus?
You could decompose that complex question into smaller questions which are more forecastable, and forecast those questions instead, in a similar way to what CSET is doing for geopolitical scenarios. For example:
This might require having infrastructure to create and answer large number of forecasting questions efficiently, and it will require having a good ontology of "priorities/mega-trends" (so most possible new priorities are included and forecasted), as well as a way to update that ontology.
This was also a point we discussed. Having something which builds upon someone else's work, or having something which will be built upon in the future generally makes a project more valuable. And in practice, I get the impression that it's mostly authors themselves which build upon their own work.