In recent discussion Patrick Kaczmarek informs me I’m absolutely mistaken to think it can problem with decision theory and helpfully suggested the issue might be the bridging principle between one’s axiology and one’s decisions theory.
The problem seems essentially the same as Parfit’s Hitchhiker: you must pre-commit to win, but you know that when the time comes to pay/spend, you’ll want to change your mind.
In this scenario you could (mathematically) save your wealth for an infinite number of periods and then donate it, generating infinite utility.
How is there anything (i.e. “and then”) after an infinite amount of periods (taking altogether an infinite amount of time)? Are you introducing hyperreals or nonstandard analysis? Are you claiming this is just a possibility (from our ignorance about the nature of time) or a fact, conditional on the universe lasting forever?
I think it’s extremely unlikely that time works this way, but if you’re an EU maximizer and assign some positive probability to this possibility, then, sure, you can get an infinite return in EU. Most likely you’ll get nothing. It’s a lot like Pascal’s wager.
Thinking from the perspective of a beneficiary, I would rather get $100 than remove a 1⁄10,000,000 risk of death.
Would you also volunteer to be killed so that 10,000,000 people just like you could have $100 that they could only spend to counterfactually benefit themselves?
I think the probability here matters beyond just its effect on the expected utility, contrary, of course, to EU maximization. I’d take $100 at the cost of an additional 1⁄10,000,000 risk of eternal torture (or any outcome that is finitely but arbitrarily bad). On the other hand, consider the 5 following worlds:
A. Status quo with 10,000,000 people with finite lives and utilities. This world has finite utility.
B. 9,999,999 people get an extra $100 compared to world A, and the other person is tortured for eternity. This world definitely has a total utility of negative infinity.
C. The 10,000,000 people each decide to take $100 for an independent 1⁄10,000,000 risk of eternal torture. This world, with probability ~ 1-1/e ~ 0.63 (i.e. “probably”) has a total utility of negative infinity.
D. The 10,000,000 people together decide to take $100 for a 1⁄10,000,000 risk that they all are tortured for eternity (i.e. none of them are tortured, or all of them are tortured together). This world, with probability 9,999,999⁄10,000,000 has finite utility.
E. Only one out of the 10,000,000 people decides to take $100 for a 1⁄0,000,000 risk of eternal torture. This world, with probability 9,999,999⁄10,000,000 has finite utility.
I would say D >> E > A >>>> C >> B, despite the fact that in expected total utility, A >>>> B=C=D=E. If I were convinced this world will be reproduced infinitely many times (or e.g. 10,000,000 times) independently, I’d choose A, consistently with expected utility.
So, when I take $100 for a 1⁄10,000,000 risk of death, it’s not because I’m maximizing expected utility; it’s because I don’t care about any 1⁄10,000,000 risk. I’m only going to live once, so I’d have to take that trade (or similar such trades) hundreds of times for it to even start to matter to me. However, I also (probably) wouldn’t commit to taking this trade a million times (or a single equivalent trade, with $100,000,000 for a ~0.1 probability of eternal torture; you can adjust the cash for diminishing marginal returns). Similarly, if hundreds of people took the trade (with independent risk), I’d start to be worried, and I’d (probably) want to prevent a million people from doing it.
Relevant recent systematic review:
Quality Assessment of Economic Evaluations of Suicide and Self-Harm Interventions: A Systematic Review.
http://psycnet.apa.org/record/2017-41357-001
PDF: http://psycnet.apa.org/fulltext/2017-41357-001.pdf
One thing that’s very useful about having separate cause areas is that it helps people decide what to study and research in depth, e.g. get a PhD in. This probably doesn’t need to be illustrated, but I’ll do it anyway:
If you consider two fields of study, A and B, such that A has only one promising intervention, and B has two, and all three interventions are roughly equal in expectation (or whatever other measures are important to you); then it would be better to study B, because if one of its two interventions don’t pan out, you can more easily switch to the other; with A, you might have to move onto a new field entirely. Studying B actually has higher expected value than studying A, despite all three interventions being equal in expectation.
I’m not sure I’d put it only on X-risk people. My understanding is that disease burden and DALYs are calculated using as a reference the highest life expectancy of any country by gender, which was previously Japanese women (now South Korean women?), and somewhere between 80 and 90 years. This means that deaths after this reference life expectancy simply don’t count towards disease burden at all. I’d like to hypothesize that this and some of the downvotes may be due to what I suspect is a common intuition (perhaps not common in EA; I don’t know): everyone ought to have an overall good life with a decent lifespan, i.e. “fair innings”.
This “fair innings” might be part of why EAs are generally more concerned with global health and poverty than anti-aging. Maybe the stronger evidence for specific poverty/health interventions explains this better, though.
Mostly guesses on my part, of course.
allocate funds to the top charities in their cause area, and donate to those charities on a regular basis until the fund manager comes along and updates the allocation
Because of discount rates, wouldn’t it then be better to do all of the disbursements between updates right after the update, instead of dragging them out?
Or, since people will continue donating between disbursements, disburse as funding becomes available, and save a chunk (everything after a certain date) for the next update because it will be better allocated.
There are protected characteristics, like race and gender, and the only way I can see EA/non-EA being covered is through beliefs. This first link only says religion specifically, but the second includes philosophical beliefs more generally:
https://www.gov.uk/discrimination-your-rights
http://www.legislation.gov.uk/ukpga/2010/15/section/10
More here: http://www.legislation.gov.uk/ukpga/2010/15/contents
I would guess that nonprofits that only serve people of a certain protected characteristics can also be legal, e.g. women’s shelters. Maybe it could fall under Services and public functions, Premises or Associations:
http://www.legislation.gov.uk/ukpga/2010/15/part/3
http://www.legislation.gov.uk/ukpga/2010/15/part/4
http://www.legislation.gov.uk/ukpga/2010/15/part/7
V*ganism shows very high ‘recidivism’ rates in the general population. Most people who try to stop eating meat/animal products usually end up returning to eat these things before long.
FWIW, based on Faunalytics surveys, the recidivism rate seems to be about 50% for vegans motivated by animal protection specifically:
https://www.facebook.com/groups/EffectiveAnimalAdvocacy/permalink/906516599519212/
The theoretical results don’t depend on the scale being 3-point. Their argument deals directly with the assumed underlying normal distributions and transforms them into log-normal distributions with the order of the expected values reversed, so it doesn’t matter how you’ve estimated the parameters of the normal distributions or if you’ve even done it at all.
In the case of life satisfaction scales, is there any empirical evidence we could use to decide the form of the underlying continuous distribution?
They do suggest that you could “use objective measures to calibrate cardinalizations of happiness”, e.g. with incidence of mental illness, or frequencies of moods, as the authors have done something similar here https://www.nber.org/papers/w19243.
I think that the evidence you present in section 4, e.g. that people interpret scales as equal-interval and that immigrants have similar SWB, could be a good response to this paper, though, because it suggests that we can interpret the discrete life satisfaction scale as cardinal and just aggregate it instead.
Regarding interpersonal comparisons,
So long as these differences are randomly distributed, they will wash out as ‘noise’ across large numbers of people
I think this is a crucial assumption that may not hold when comparing groups, i.e. there could be group differences (which could involve the same people, but before and after some event) in interpretations of the scales, due to differences in experiences between the two groups, e.g. a disability.
That immigrants to Canada seem to use the scales similarly to Canadians doesn’t mean they weren’t using the scales differently before they came to Canada. I think we actually discussed scale issues with life satisfaction on Facebook before (prompted by you?), and differences after adjusting for item responses seem to suggest different interpretations of the scale (or the items), or different relationships between the items. Two examples (cited in one of the papers in your reading list, https://www.researchgate.net/publication/230603396_Theory_and_Validity_of_Life_Satisfaction_Scales):
http://www.people.virginia.edu/~so5x/IRT%20JRP%20revision%202.pdf
But there’s an obvious response here: we should use IRT to adjust for different scale interpretations.
Besides the person-affecting views and disvalue of life covered here, if an individual has an Epicurean view of life and death (another kind of person-affecting view), i.e. death is not bad, then improving wellbeing should probably take priority. And while Epicureanism assigns 0 disvalue to death (ignoring effects on others), one could assign values arbitrarily close to 0.
There are also issues with dealing with infinities that make utilitarianism non-action guiding (it doesn’t tell us what to do in most practical cases); you could probably throw these in with nihilism. E.g. if the universe is unbounded (“infinite”) in space or time, then we can’t change the total sum of utility, and that number is not even well-defined (not even +infinity or -infinity) with the usual definitions of convergence in the real numbers. If you assign any nonzero probability to an infinite universe, you end up with the same problem, but it’s actually pretty likely that the universe is spatially unbounded. There are several attempts at solutions, but all of them have pretty major flaws, AFAIK.
Some person-affecting views can help, i.e. using a Pareto principle, but then it’s not clear how to deal with individuals whose exact identities depend on your decisions (or maybe we just ignore them; many won’t like that solution), and there are still many cases that can’t be handled. There’s discussion in this podcast, with some links for more reading (ctrl-F “Pareto” after expanding the transcript): https://80000hours.org/podcast/episodes/amanda-askell-moral-empathy/
Rounding sufficiently small probabilities to 0 and considering only parts of the universe we’re extremely confident we can affect can help, too. This proposed solution and a few others are discussed here: https://nickbostrom.com/ethics/infinite.pdf
You could also have a bounded vNM utility function, but this means assigning decreasing marginal value to saving lives, and how you divide decisions/events matters, e.g. “saving 1 life and then saving 1 life” > “saving 2 lives and then saving 0 lives”.
For the unbounded time case (assuming we can handle or avoid issues with unbounded space, and people might prefer not to treat time and space differently): https://forum.effectivealtruism.org/posts/9D6zKRPfaALiBhnnN/problems-and-solutions-in-infinite-ethics
You can get similar value-independence in favour of extinction by using “bads” instead of “goods”. Many of the values in Oesterheld’s list have opposites which could reasonably be interpreted as “bads”, and some of them are already “bads”, e.g. suffering, pain and racism.
Assuming that future agents are mostly indifferent towards the welfare of their “tools”, their actions would affect powerless beings only via (in expectation random) side-effects. It is thus relevant to know the “default” level of welfare of powerless beings.
By “in expectation random”, do you mean 0 in expectation? I think there are reasons to expect the effect to be negative (individually), based on our treatment of nonhuman animals. Our indifference to chicken welfare has led to severe deprivation in confinement, more cannibalism in open but densely packed systems, the spread of diseases, artificial selection causing chronic pain and other health issues, and live boiling. I expect chickens’ wild counterparts (red jungle fowls) to have greater expected utility, individually, and plausibly positive EU (from a classical hedonistic perspective, although I’m not sure either way). Optimization for productivity seems usually to come at the cost of individual welfare.
Even for digital sentience, if designed with the capacity to suffer—regardless of our intentions and their “default” level of welfare, and especially if we mistakenly believe them not to be sentient—we might expect their levels of welfare to decrease as we demand more from them, since there’s not enough instrumental value for us to recalibrate their affective responses or redesign them with higher welfare. The conditions in which they are used may become significantly harsher than the conditions for which they were initially designed.
It’s also very plausible that many of our digital sentiences will be designed through evolutionary/genetic algorithms or other search algorithms that optimize for some performance (“fitness”) metric, and because of how expensive these approaches are computationally, we may be likely to reuse the digitial sentiences with only minor adjustments outside of the environments for which they were optimized. This is already being done for deep neural networks now.
Similarly, we might expect more human suffering (individually) from AGI with goals orthogonal to our welfare, an argument against positive expected human welfare.
Isn’t it equally justified to assume that their welfare in the conditions they were originally optimized/designed for is 0 in expectation? If anything, it makes more sense to me to make assumptions about this setting first, since it’s easier to understand their motivations and experiences in this setting based on their value for the optimization process.
Apart from that, I am not sure if the two assumptions listed as bullet points above will actually hold for the majority of “sentient tools”.
We can ignore any set of tools that has zero total wellbeing in expectation; what’s left could still dominate the expected value of the future. We can look at sets of sentient tools that we might think could be biased towards positive or negative average welfare:
1. the set of sentient tools used in harsher conditions,
2. the set used in better conditions,
3. the set optimized for pleasure, and
4. the set optimized for pain.
Of course, there are many other sets of interest, and they aren’t all mutually exclusive.
The expected value of the future could be extremely sensitive to beliefs about these sets (their sizes and average welfares). (And this could be a reason to prioritize moral circle expansion instead.)
My rough answer to this is: If someone wants to die (after thinking about it for a long time and having time to reflect on it), let them die.
Some people don’t have the choice to die, because they’re prevented from it, like victims of abuse/torture or certain freak accidents.
I don’t see how the atrocities that are experienced by humans outweigh the benefits, given that the vast majority of humans seem to have a pretty decent will to live.
I think this is a problem with the idea of “outweigh”. Utilitarian interpersonal tradeoffs can be extremely cruel and unfair. If you think the happiness can aggregate to outweigh the worst instances of suffering:
1. How many additional happy people would need to be born to justify subjecting a child to a lifetime of abuse and torture?
2. How many extra years of happy life for yourself would you need to justify subjecting a child to a lifetime of abuse and torture?
The framings might invoke very different immediate reactions (2 seems much more accusatory because the person benefitting from another’s abuse and torture is the one making the decision to subject them to it), but for someone just aggregating by summation, like a classical utilitarian, they’re basically the same.
I think it’s put pretty well here, too:
There’s ongoing sickening cruelty: violent child pornography, chickens are boiled alive, and so on. We should help these victims and prevent such suffering, rather than focus on ensuring that many individuals come into existence in the future. When spending resources on increasing the number of beings instead of preventing extreme suffering, one is essentially saying to the victims: “I could have helped you, but I didn’t, because I think it’s more important that individuals are brought into existence. Sorry.”
For the Rethink Priorities project, why not also look into consciousness in plant species (e.g. mimosa and some carnivorous plants), AI (especially reinforcement learning) and animal/brain simulations (e.g. OpenWorm)? Whether or not they’re conscious (or conscious in a way that’s morally significant), they can at least provide some more data to adjust our credences in the consciousness of different animal species; they can still be useful for comparisons.
I understand that there will be little research to use here, but I expect this to mean proportionately less time will be spent on them.
FWIW, this is aimed at developing countries.
Couldn’t you say the same about GiveWell’s evaluation of AMF, TLYCS’s evaluation of PSI or the evaluation of any other charity or intervention that would predictably affect population sizes? ACE doesn’t consider impacts on wild animals for most of the charities/interventions it looks into, either, despite the effects of agriculture on wild animals.
My impression is that Charity Science/Entrepreneurship prioritizes global health/poverty and animal welfare, so we shouldn’t expect them to consider the effects on technological advancement or GCRs anymore than we should expect GiveWell, TLYCS or ACE to.
They have worked on evaluating animal welfare, though, so it would be nice to see this work applied here for wild animals.
EDIT: Oh, is the concern that they’re looking at a more biased subset of possible effects (by focusing primarily on effects that seem positive)?
We can make necessitarianism asymmetric: only people who will necessarily exist OR would have negative utility (or less than the average/median utility, etc.) count.
Some prioritarian views, which also introduce some kind of asymmetry between good and bad, might also work.