Hey! I am Mart, I learned about EA a few years back through LessWrong. Currently, I am pursuing a PhD in the theory of quantum technologies and learning more about doing good better in the EA Ulm local group and the EA Math and Physics professional group.
I am just coming from a What We Owe the Future reading group - thanks for reminding me of the gap between my moral untuitions and total utilitarianism!
One reason why I am not convinded by your argument is that I am not sure that the additional lifes lived due to the unintended pregnancies are globally net-positive:
the number of 100 pregnancies averted does not correspond to 100 fewer children being born in the end. A significant part of the pregnancies would only be shifted in time. I would be surprised if the true number is larger than 10 and expect it to be lower than this. My reasoning here is that the total number of children each set of parents is going to have will hardly be reduced by 100x from access to contraception. If this number started at 10 children and is reduced to a single child, we have a reduction that corresponds to 10 fewer births per death averted. And stated like this, even the number 10 seems quite high(sorry, there were a few confusions in this argument)This being said, the main reason why I am emotionally unconvinced by the argument you give is probably that I am on some level unable to contemplate "failing to have children" as something that is morally bad. My intuitions have somewhat cought up with the arguments that giving happy lives the opportunity to exist is a great thing, but they do not agree to the sign-flipped case for now. Probably, a part of this is that I do not trust myself (or others) to actually reason clearly on this topic and this just feels like "do not go there" emotionally.
It also does not seem obvious that we are above that number. Especially when trying to include topics like wild animal suffering. At least I feel confident that human population isn't off from the optimum by a huge factor. ↩︎
This is a good point, although I would argue that the reasons why practicing religion has these advantages is unrelated to it being a case of Pascal's wager (if we let Pascal's wager stand for promises of infinite value in general).
This is not enough to claim that Christianity as a whole holds this position, but there certainly exist sentiments in this direction such as
Revelation 3:15--16
After talking to GPT about this[1], I think that my concern is actually already covered by your
and could be addressed to a large degree with a few automatized checks and a user interface (one could even auto-fill the table given the first line of entries by assuming ~maximum resulting uncertainty for the unknown correlations). I feel like this could be really helpful for reflecting on one's values if done right, or overwhelming if done wrong.
[GPTs answer includes a summary, so I'll leave out my prompt:] I see your point now. You're referring to a potential inconsistency in the triangle inequality context for relative values. Let's use a concrete example to explore this. Suppose the relative value of 'chocolate' to 'vanilla' is given by a 90% credibility interval from 0.8 to 1.2, and 'banana' to 'vanilla' is from 1.0 to 1.5. This would imply that, based on your preferences for chocolate and banana ice cream relative to vanilla, 'banana' should have a relative value compared to 'chocolate' that lies within a certain range. If, however, you then provide a wide 90% credibility interval for 'chocolate' to 'banana' (e.g., 0.2 to 5), it may seem inconsistent with the narrower ranges implied by the comparisons with 'vanilla'. In this case, it may be that you need to revisit your estimates for 'chocolate' to 'vanilla' and 'banana' to 'vanilla'. If you feel a lot of uncertainty about 'chocolate' vs. 'banana', perhaps you also should feel more uncertainty about these options compared to 'vanilla' than you initially thought. You may have overestimated your confidence in these comparisons. Alternatively, if you're confident in your estimates of 'chocolate' to 'vanilla' and 'banana' to 'vanilla', you may want to narrow down your 90% credibility interval for 'chocolate' vs. 'banana'. In any case, it's a good point to keep in mind when building a relative value table. You want to be consistent in your uncertainty estimates across different comparisons. If there seems to be a contradiction, it's a sign that you may need to rethink some of your estimates. ↩︎
Thanks! I'll reply in separate comments
Okay, so maybe relative values are a more straightforward concept than I thought/feared :)
Ooh, that makes sense. Thanks!
So my idea of 'strict relative values' turns out to be an illusory edge case if we use distributions and not numbers, and in practice we'll usually be in the 'generalized case' anyway.
I fear, my not-grokking the implications remains. But at least, I don't mistakenly think I fully understood the concept any more.
It is probably not worth the effort for you to teach me all about the approach, but I'll still summarize some of my remaining questions. Possibly my confusions will be shared by others who try to understand/apply relative value functions in the future
As you write, this is not really well-defined as one would need correlations to combine the distributions perfectly. But there should still be some bounds one could get on the outcome distribution. ↩︎
For example, it might totally happen that I feel comfortable with giving precise monetary values to some things I enjoy, but feel much less certain if I try to compare them directly ↩︎
Thanks for your reply, you are probably right. Let my share my second attempt of understanding relative values after going through the web app.
'strict' relative values
If I did not overlook some part in the code, the tables created in the web app are fully compatible with having a single unit.
If this is the intent of how relative values are meant to be used, my impression of their advantages is:
This version of relative values (let's call it "strictly coherent relative values according to Mart's understanding v2" or "strict relative values" for short) feels quite intuitive to me and also seems significantly similar to how givewell's current cost-effectiveness analyses are done (except that they do not create a value table with all-to-all translations and there being no/fewer distributions[1].)
Your link to the usage of relative values in Finance seems to me to be compatible with this definition of relative values.
Beyond 'strict' relative values
But, from reading your OP (and the recommended section of the video), my impression is that relative values are intended to be used to describe situations more general than my "strict relative values".
Your
and also David Johnston's comments seem to refer to a much more general case.
For this more general version my 'strictness' equation value(item1)value(item2)=value(item1)value(reference)/value(item2)value(reference) would typically not be valid. Translated into David's notation, the 'strictness' equation would be xij=x0j/x0i where 0 is the reference value, and xij are the relative values comparing i and j.
David's
is clearly not compatible with 'strictness' [2].
In such a generalized case, I think that the philosophical status of what entries mean is much more complicated. I do not have a grasp on what the added degrees of freedom do and why it is good to have them. In my last comment, I kind of assumed that any deviation from strictness would be "irrational inconsistency" by definition. But maybe I am just missing the relevant background and this really does capture something important?
This impression is based on the 2023 spreadsheet. This might well be a mistaken impression ↩︎
Proof: Insert xij and xji into the 'strictness equation' and see that the results are the reciprocals of each other ↩︎
I would :)
Are there by any chance plans to collect the audio in a podcast feed?
I have little experience on quantifying value, so I don't feel that I have a relevant opinion about approaches to this topic. But improving our conceptual tools for this clearly seems valuable :)
I feel like a commonly occurring result would be that the comparison tables include contradictory properties. If someone asked me about my relative preferences for apples, bananas and cherries, I think the chance would be significant that I give a 'contradictory' answer like "5 cherries = 1 apple or 1 banana, but also 1 apple = 2 bananas". Using distributions might help, but I think a corresponding property of "tension between ratios" should still appear quite frequently.
It feels like a confusing property that one could get different results by converting to the final units, WELLBYs for example, in two steps instead of using a direct conversion. First translating everything to QALYs and then to WELLBYs would usually give different results than the direct path.[1]
Would the solution be to quantify all actions using their native units and then convert to the unit of interest without intermediate steps? I can see a case for this. If we are highly uncertain, avoiding unnecessary mental steps is a good idea.
Possibly something like this is the best we can do as long as we cannot define an explicit utility function. Still, I would be interested whether relative value functions could be a tool that helps us resolve confusions in what we value?
I think that this is actually the additional information which having such a table adds compared to using a single central unit of comparison. If there were no path dependency, the table would be redundant and could be replaced by a single central unit (= any single line of the table). This makes me extra curious about the question of what this "extra information" really means? ↩︎