Possibly relevant: the academic papers of Bard Harstad on game theory of international treaties (esp. in climate change policy).
https://www.sv.uio.no/econ/personer/vit/bardh/dokumenter/participation.pdf
https://www.sv.uio.no/econ/personer/vit/bardh/dokumenter/cmp.pdf
https://www.sv.uio.no/econ/personer/vit/bardh/dokumenter/iea.pdf
https://www.sv.uio.no/econ/personer/vit/bardh/dokumenter/prb.pdf
You may be interested in this 2021 WSJ article: “A Technology Race to Stop the Mass Killing of Baby Chicks: An estimated six billion newly hatched male chicks are killed world-wide each year. New technologies are being developed to stop that.”
I know George Church is a big name in bio/genetics who seems to have interests in transhumanism.
Another WSJ article just weeks ago: “Scientists at DeepMind and Meta Press Fusion of AI, Biology”.
I would think bio is a great thing to go into! However, I’d guess your point (4) has significant truth to it. If you do bio, I’d make sure you still learn your math! Math is the language of science. I think there are a lot of people that major in bio and get a kind of “soft” pre-med style bio education which is mostly memorizing stuff. I would see if you can do like a computational bio major or double major with math, applied-math, stats, or CS. I’d try to take several courses in probability/statistics/machine learning.
If that article is paywalled, try this: https://drive.google.com/file/d/1shcLzlS8qf7ODbqMqiHtwRzFminRpiC3/view
Interesting, well maybe I’m off base then.
I’m definitely not knowledgeable about AI, but my two cents is that there is a thing called the frame problem that makes AGI very hard to attain or even think about. I’m not gonna even try to exposit what that is, and that article is a bit dated, but I’d guess the problem still remains beyond anyone’s comprehension.
Tangential:
I think the whole issue of “one person’s modus ponens is another’s person’s modus tolens” is not very well understood by most people and including most philosophers and myself. In fact, I’m don’t think anyone knows quite how to think about these things. I guess it gets into Quinean holism and the intractability problems that accompany it.
But, presumably, it has something to do with Bayesian networks of beliefs and regularization in machine learning (~valuing simplicity) as well as Bayesian philosophy of science more generally. [Part IV of Itzhak Gilboa’s decision theory book gets into some of this stuff, which seemed pretty interesting.]
I don’t understand why much more attention is not paid to these things in philosophy, where formal epistemology seems to still be considered a pretty niche field.
I hope people think more about these issues.
OP here. After spending some more time with ChatGPT, I admit my appreciation for this field (AI Alignment) has increased a bit.
My 2 cents: I’d be cautious about restricting yourself to EA-aligned orgs as an undergrad. I would consider whatever opportunities would best help cultivate your intellectual/professional/skill development. This is especially true since you are studying technical fields that can take many years of investment in skill-building to see a (big) return. Not saying that EA-orgs can’t be good for that, but so can a lot of other opportunities.
I have opposite intuition actually—I’d guess that people closer to animals have more empathy for their suffering.
I also have that intuition.
I’d guess that does still hold after adjusting, but I did take it out.
One thing is that I’d guess working class, rural people are more likely to work in some area at least adjacent to the meat/fish/food industry, and so the vegetarian movement would go against their livelihood, which might make them more likely to oppose it. To be clear, I’m not blaming those people. I think the city-dwelling meat eater who deliberately shields themselves from the unpleasant sight of the process that makes their food is much more troublesome.
Also, working class areas just don’t have vegan food available as much.
I’m sure many farmers do care about their animals.
Thanks for the ref!
Some companies have started video chat speed dating.
Yash Kanoria has some interesting game theory analysis of such platforms. I think such models need to be more explicit about modeling gender differences, which academic papers are less likely to do since such things can sometimes be controversial / non-PC.
I would discourage framing this as motivated by it being high impact.
I’m mostly sympathetic to your view, but I don’t think that sort of thing should always be discouraged/avoided categorically. I think a balance has to be found with that sort of thing. How to find that balance? I don’t know.
CS=Computer Science. The key thing is that once you learn a bunch of math (and some coding skills), you will be able to pick up other fields way more easily than vice-versa.
CS would have some classes in software engineering and data science and would also give skills.
Math or CS keeps your options open. Can you do a joint or double major like math/bio or CS/bio?
Interesting post.
I have three points/thoughts in response:
1) Could it be useful to distinguish between “causal uncertainty” and “non-causal uncertainty” about who (and how many) will exist?
Causal uncertainty would be uncertainty resulting from the fact that you as a decision maker have not yet decided what to do, yet where your actions will impact who will exist—a strange concept to wrap my around. Non-causal uncertainty would be uncertainty (about who will exist) that stems from uncertainty about how other forces will play out that are largely independent of your actions.
Getting to your post, I can see why one might discount based on non-causal uncertainty (see next point for more on this), but discounting based on causal uncertainty seems rather more bizarre and almost makes my head explode (though see this paper).
2) You claim in your first sentence that discounting based on space and time should be treated similarly to each other, and in particular that discounting based on either should be avoided. Thus it appears you claim that absent uncertainty, we should treat the present and future similarly; [if that last part didn’t quite follow see point 3 below]. If so, one can ask should we also treat uncertainty about who will eventually come into existence similarly to how we treat uncertainty about who currently exists? For an example of the latter, suppose there are an uncertain number of people trapped in a well: either 2 or 10 with 50-50 odds and we can take costly actions to save them. I think we would weight the possible 10 people with only 50% (and similarly the possible 2 people), so in that sense I think we would and should discount on uncertainty about who currently exists. If so and if we answer yes to the third sentence in this paragraph, we should also discount future people based on non-causal uncertainty.
3) Another possibility is to discount not based on time per se (which you reject) but rather on current existence, so that future people will be discounting or ignored until they exist at which point they get full value. A potential difficulty with this approach is that you could be sure that 1 billion people are going to be born in a barren desert next year and you would, then, have no (or at discounted) reason to bring food to that desert until they were born, at which point you would suddenly have a humanitarian crisis on your hands which you quite foreseeably failed to prepare for. [Admittedly people come into existence through a gradual process (e.g. 9 months) so it wouldn’t be quite a split-second change of priorities about whether to bring food, which might attenuate the force of this objection a bit.]
Definitely math not economics. Once you understand a bit of multivariable real analysis (which would not be until towards the end of undergrad unless you’re very advanced), you can start learning some economic theory or econometrics.
I don’t know international issues or bio well, but fwiw I would resist the urge to spend a ton of time thinking about planning various possible career paths in the distant future as an young undergrad. The future is hard to predict. I’d focus on getting the skills that will give you options.
Respectfully, you may need to just “put your head down” for a couple years and just focus on studying really hard and not think too much about the distant future. I’d start by focusing your studies on foundational subjects like math and then gradually shift your coursework/time more to applied subjects (e.g. statistics → bio) as you go through undergrad and beyond. Take hard stem classes (within your level) and try to learn as much math as possible. Don’t worry about getting straight A’s. B’s in hard classes is better than A’s in easy classes. If you can understand, say, scientific computing, MV calc, linear algebra, real analysis + functional analysis (optional), probability, bayesian statistics, and machine learning/deep learning, and you also you also background in bio (esp. if you have some research experience), you will likely be accepted to good phd programs (let alone masters) in U.S. and elsewhere. Private sector jobs or research assistantships will likely also be available.
Every once in a while come up for air and reorient your direction. But I think it’s the long slow slog (through the textbooks, psets, coding assignments) that will bring you success rather than getting the perfect career plan from day one.