If you “find it hard to imagine an ultimate ethical theory that isn’t based on some form of utilitarianism” then you probably don’t have a very strong understanding of normative ethics.
FWIW I’d like to take this opportunity to advertise my list of recommended readings about non-utilitarian normative ethics, which some utilitarians may find educational.
Maybe someone can write a similar list for metaethics.
Tl;dr. Sounds like you’re criticizing some views/approaches, perhaps rightly so. Do you have an alternative approach you suggest in place of those that you criticize?
[Apologies if the following is a bit off-topic since the post appears more concerned with “political” theory than “axiology”, but:]
I think that a thorough and generous engagement with common sense “non-utilitarian” perspectives on population ethics such as the “intuition of neutrality [about making more people]” may benefit from some engagement with the metaphysics and logic of future contingents. Below are a few references that may be of relevance. I don’t think studying these will lead to any quick answers, but I think in the long run it may benefit the field.
- Future Contingents SEP
- Nuel Belnap Branching Space-Times: Theory and Applications
- Nuel Belnap Facing the Future: Agents and Choices in Our Indeterminist World
- Andrea Borghini & Giuliano Torrengo “The Metaphysics of the Thin Red Line”
- Ted Sider, “Presentism and Ontological Commitment”
Interesting post. Definitely a subject that must be studied from multiple angles, but FWIW I’ll mention the field of organzational economics.
My two cents (not knowing anything about biology/pandemics):
If you want to do almost anything related to science of any kind, I would recommend getting a solid grounding in math, statistics, and computer programming in addition to your content-specific studies (e.g. biology or epidemiology). This will give you vastly greater options to adjust your career as you go than if you just do some specific content area (e.g. biology). It will also likely also help you understand the particular content area much better.
Toward this end, I would consider doing a double major like 1) math, statistics, or computer science and 2) your content area [e.g. biology]. If you can only do one major, I would choose the technical major (e.g. applied math) rather than the content major (e.g. bio).
We found no good evidence that any insect failed a criterion.
Is there any animal that is found to fail these criteria?
Classically, deontology is the view that some actions are intrinsically right or wrong, regardless of the consequences. It holds that there are certain things that we ought never to do, even if doing them would lead to good consequences. For example, it is always wrong to torture an innocent person, regardless of whether or not doing so would lead to some greater good.
I think that what you describe is better termed “absolute deontology”. Arguably, absolute deontology is (under most precisifications) a pretty radical view that most people would reject (at least if they really thought about it). More common is what Zamir and Medina call “moderate” (or “threshold”) deontology. Moderate deontology allows one to do things like lie, cheat, steal, or cause direct bodily harm to innocents when the benefits of doing so massively outweigh the costs. It prohibits one from doing so when the benefits only moderately outweigh the costs.
Does stealing from the rich and giving to the poor count as a case where the benefit massively outweighs the costs?
It should be noted that, under these views, the ‘benefits and costs’ will not be just total welfare, but may instead involve things like deservingness, automony, rights, equality, justice.
Of course, there are different degrees of moderate deontology.
How best to think about moderate deontology under uncertainty may depend on the case, but notions of ex-ante and ex-post harm can be used.
Although I agree with your general point that thinking in somewhat non-consequentialist ways can often be a more effective way to pursue good consequences (if that’s what you want to do), this is not always true. If you are a real a utilitarian, there is a non-trival chance that at some point in your life, you’re gonna have to do something which is seriously at odds with conventional morality.
These issues about how to discipline one’s mind to effectively pursue good consequences (directly or indirectly) are very important in practice for consequentialists, but I think are probably not fully resolvable in theory. Ultimately, pursuing good consequences is an art.
Are you interested in grad school or just working in non-profit sector, or private sector?
My background is more theory so take all of this with grain of salt.
During your undergrad, I would recommend to focus on learning as much math, coding, and statistics as you can. I worry less about learning economics for now.
With some RAships, you might not learn that much from them (they will just be grunt work). You will be more likely to get better RA positions slightly later once you have built up more skill/knowledge. So I would consider waiting to do RAs till late in college (summer after 3rd year at the earliest) or even after college when you have solid statistics and coding knowledge. I would even recommend considering switching majors away from economics towards math (or mathematical economics) or statistics if you can (though not necessary).
Regarding your courses. I wouldn’t prioritize taking any of those economics classes except econometrics. The probability and statistics class would be very good as long as it’s more advanced than the statistics class you already took. Probability is something that you will need to study multiple times to understand well.
I would say Linear Algebra is essential so take that for sure. Personally I very much like the book linear algebra done wrong by Sergei Treil (though it is a little advanced). Gilbert Strang also has a nice online course on LA. I would steer clear of “abstract” linear algebra courses/books such as Axler. Keep in mind that this is a visual subject; don’t get lost in the algebra.
I would also recommend a good sequence in econometrics (ideally at least 2 courses that have probability/stats as a prereq). Other good courses if your more advanced would be bayesian statistics and/or probabilistic machine learning, maybe monte carlo simulation.
If you want to go to grad school, you need at least one course in real analysis. For grad school, I would also recommend some sort of “intro to higher math”-type course in pure math like discrete math, number theory, or intro to proofs or something—this will help your mathematical maturity which will pay dividends in the long run in your other courses.
Learn to code in Stata, R, and/or Python (esp. Pandas package). I would take a course in one of these if possible e.g. a course in statistical computing. Stata is probably best for empirical academic economics research (e.g. development econ). But other languages are probably better if you want more options for other career paths. Maybe also consider doing a certificate online class in one of these languages e.g. from Coursera or Stata Corp or something. Learn to merge, clean, and analyze datasets.
I would take at least a couple economics classes at some point and try to find a good professor that can mentor you a bit; maybe like a department undergrad advisor. They may also help you network for RAships and give you letters of recommendation.
Learning this stuff is hard and you might not get some of the material the first time around. Don’t get too discouraged if you get a bad grade or even fail a class. Keep reviewing the material and learning it even after the class ends. Study hard, and keep up with good study techniques. Don’t get behind. Make sure you are keeping up with the material and if there is something you don’t understand (keep a list), ask for help on that. Practice regular self-quizzing: Every day, think about what you learned 2 days ago!
Coming from an underprivileged background is tough and you should be proud of your achievements so far!
Also, some good online resources (for self study) (maybe slightly advanced):
Ben Elsner Causal Inference videos
causal inference with R
causal inference with Python
Ben Lambert Econometrics playlist
Mathematical Monk Machine Learning Playlist
Good books on econometrics (advanced but accessible to advanced undergrads). I don’t know the best undergrad level books; maybe others can comment.
Mostly harmless econometrics.
Causal inference mixtape.
Bruce Hansen Econometrics
Jeff Wooldridge Econometric Analysis of Cross Section and Panel Data
We identified three barriers as the most problematicFeeling unhealthy on one’s veg*n diet,Not seeing veg*nism as part of one’s identity, andBelieving that society sees veg*nism negatively.
We identified three barriers as the most problematic
Feeling unhealthy on one’s veg*n diet,
Not seeing veg*nism as part of one’s identity, and
Believing that society sees veg*nism negatively.
Sorry am I missing something? Isn’t the main barrier that vegan food is often less enjoyable to eat (even unpleasant)? And that getting/making good vegan food can be hard, time-consuming, or expensive?
Interpersonal relationships when trying to live a EA/utilitarian lifestyle seems like a potentially important omitted topic based on your second to last paragraph.
It might be tough to make a lot of progress on these things until we’re allowed to start poking people in the brain and asking them about it. My sense is that the current science and philosophy of the emotions (and adjacent topics) is not well developed at all. Perhaps once we have a better grasp of those things maybe we can start to think more usefully about metaethics (though maybe not).
I think it’s too early to decide specifically what you’re going to work on for your career. I would just put your head down and focus mostly on learning math for a couple years then have a rethink once you’re a 3rd year or so. As long as you have good math and CS skills, you will have many options later.
See also Storable Votes
Some people claim we should care about only those future people that will actually exist, not those that could have but won’t.
It’s a bit hard to make sense of what that means, but in any case, it’s unclear what they want to say when we are uncertain about who will exist, whether because of uncertainty about our own future actions or uncertainty about how other events beyond our control will play out.
Further, I wonder if how we approach uncertainty about who will exist in the future should be treated differently from uncertainty about who currently exists?
Suppose there are between 1-5 people trapped at the bottom of the well, but we don’t know exactly how many. It seems hard to argue that we should discount the uncertain existence of those people by more than their probability of existing.
FWIW, I thought Chapter 1 of John Roemer’s Theories of Distributive Justice gave me a helpful introduction to some of these scale/measurability issues as they relate to social choice.