Jérémy Andréoletti
Bio
Participation3
PhD student at ENS Paris in evolutionary biology. I co-founded EffiSciences, an organisation dedicated to promoting high-impact research in France (https://www.effisciences.org). I also co-started a local EA group at ENS.
How others can help me
Growing a new organisation on one's free time is difficult, and so is research prioritisation on a large scale. If you have any advice on either of these, please do not hesitate to contact me!
Also, as EffiSciences evolves, we're eager to receive feedback on our updated roadmap and theory of change, and to explore partnerships. Especially in AI Safety, if you wish to join our growing community in France, or replicate some of our activities abroad, let's connect!
How I can help others
I'd be happy to share the experience I've gained over the past year in nonprofit management, including strategic planning and fundraising. I've also been thinking quite a bit about the impact of academic research, and how to build talent pipelines within universities.
In addition, with my research experience in macroevolution and mathematical modeling, I can share some insights into the complexities of species extinction and Bayesian inference methods.
Comments2
Thanks for raising this question! Following other comment, I find the use of somewhat unsatisfactory.
Perhaps some of the confusion could be reduced by i) taking into account the number of interventions and ii) distinguishing the following two situations:
1. Epistemic uncertainty: the magic intervention will always save 1 life, or always save 100 lives, or always save 199 lives, we just don't know. In this case, one can repeat the intervention as many times as one wants, the expected cost-effectiveness will remain ~$3,400/life.
2. True randomness: sometimes the magic intervention will save 1 life, sometimes 100 lives, sometimes 199 lives. What happens then if you repeat it n times? If , your expectation is still ~$3400/life (tail risk of a single life saved). But the more interventions you do, the more you converge to a combined cost-effectiveness $100/life (see figure below), because failed interventions will probably be compensated by very successful ones.
(R code to reproduce the plot : X <- sample(1:20,1000000, replace=T) ; Y <- sapply(X,function(n)mean(10000*n/sum(sample(c(1,100,199), n, replace = T)))) ; plot(X, Y, log="y", pch=19, col=alpha("forestgreen", 0.3), xlab="Number of interventions", ylab="Cost-effectiveness ($/life, log scale)", main="Expected cost to save a live decreases with more interventions") ; lines(sort(unique(X)), sapply(sort(unique(X)), function(x)mean(Y[X==x])), lwd=3, col=alpha("darkgreen",0.5)))
I'm not sure how to translate this into practice, especially since you can consider EA interventions as a portfolio even if you don't repeat the intervention 10 times yourself. But do you find this framing useful?
The Lion, The Dove, The Monkey, and the Bees