tl;dr – The case for giving to GiveWell top charities is based on much more more than just expected value calculations.
The case for longtermism (CL) is not based on much more than expected value calculations, in fact many non-expected value arguments currently seem to point the other way. This has lead to a situation where there are many weak arguments against longtermsim and one very strong argument for longtermism. This is hard to evaluate.
We (longtermists) should recognise that we are new and there is still work to be done to build a good theoretical base for longtermism.
Hi Max,
Good question. Thank you for asking.
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The more I have read by GiveWell (and to a lesser degree by groups such as Charity Entrepreneurship and Open Philanthropy) the more it is apparent to me that the case for giving to the global poor is not based solely on expected value but is based on a very broad variety of arguments.
The rough pattern of these posts is that taking a broad variety of different decision making tools and approaches and seeing where they all converge and point too is better than just looking at expected value (or using any other single tool). That expected value calculations are not the only way to make decisions and that the arguments for giving to the global poor would be unconvincing if solely based on expected value cautions and not on historical evidence, good feedback loops, expert views, strategic considerations, etc, etc. then the authors would not be convinced.
For example in [1.] Holden describes how he was initially sceptical that: ”donations can do more good when targeting the developing-world poor rather than the developed-world poor “ but he goes onto says that: ”many (including myself) take these arguments more seriously on learning things like “people I respect mostly agree with this conclusion”; “developing-world charities’ activities are generally more robustly evidence-supported, in addition to cheaper”; “thorough, skeptical versions of ‘cost per life saved’ estimates are worse than the figures touted by charities, but still impressive”; “differences in wealth are so pronounced that “hunger” is defined completely differently for the U.S. vs. developing countries“; “aid agencies were behind undisputed major achievements such as the eradication of smallpox”; etc.”
– –
Now I am actually somewhat sceptical of some of this writing. I think much of it is a pushback against longtermism. Remember the global development EAs have had to weather the transition from “give to global health, it has the highest expected value” to “give to global health, it doesn’t have the highest expected value (longtermism has that) but is good for many other reasons”. So it is not surprising that they have gone on to express that there are many other reasons to care about global health that are not based in expected value calculations.
– –
But that possible “status quo bias” does not mean they are wrong. It is still the case that GiveWell have made a host of arguments for global health beyond expected value and that the longtermsim community has not done so. The longtermism community has not produced historical evidence or highlighted successful feedback loops or demonstrated that their reasoning is robust to a broad variety of possible worldviews or built strong expert consensus. (Although the case has been made that preventing extreme risks is robust to very many possible futures, so that at least is a good longtermist argument that is not based on expected value.)
In fact to some degree the opposite is the case. People who argue against longtermism have pointed to cases were long-term type planning historically led to totalitarianism or to the common-sense weirdness of longtermist conclusions etc. My own work into risk management suggests that especially when planning for disasters it is good to not put too much weight on expected value but to assume that something unexpected will happen.
The fact is that the longtermist community has much more weird conclusions than the global health community yet has put much less effort into justifying those conclusions.
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To me it looks like all this has lead to a situation where there are many weak arguments against longtermsim (CL) and one very strong argument for longtermism (AL->CL). This is problematic as it is very hard to compare one strong argument against many weak arguments and which side you fall on will depend largely on your empirical views and how you weigh up evidence. This ultimately leads to unconstructive debate.
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I think the longtermist view is likely roughly correct. But I think that the case for longtermism has not be made rigorously or even particularly well (certainly it does not stand up well to Holden’s “cluster thinking” ideals). I don’t see this as a criticism of the longtermist community as the community is super new and the paper arguing the case even just from the point of view of expected value is still in draft! I just think it is a misconception worth adding to the list that the community has finished making the case for longtermism – we should recognise our newness and that there is still work to be done and not pretend we have all the answers. The EA global health community has build this broad theoretical bases beyond expected value and so can we, or we can at least try.
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I would be curious to know the extent to which you agree with this?
Also, I think this way of mapping situation is a bit more nuanced here than in my previous comment so I want to acknowledge a subtle changing of views between by earlier comment and this one, ask that if you respond you respond to the views as set out here rather than above and of course thank you for your insightful comment that lead to my views evolving – thank you Max!
– – – –
(PS. On the other topic you mention. [Edited: I am not yet sure of the extent to which I think] the ‘beware suspicious convergence’ counter-argument [applies] in this context. Is it suspicious that if you make a plan for 1000 years it looks very similar to if you make a plan for 10000 years? Is it suspicious that if I plan for 100000 years or 100 years what I do in the next 10 years looks the same? Is it suspicious that if I want to go from my house in the UK to Oslo the initial steps are very similar to if I want to go from my house to Australia – ie. book ticket, get bus to train station, get train to airport? Etc? [Would need to give this more thought but it is not obvious] )
The cases for specific priorities or interventions that are commonly advocated based on a longtermist perspective (e.g. “work on technical AI safety”) are usually far from watertight. It could be valuable to improve them, by making them more “robust” or otherwise.
Expected-value calculations that are based on a single quantitative model have significant limitations. They can be useful as one of many inputs to a decision, but it would usually be bad to use them as one’s sole decision tool.
(I am actually a big fan of the GiveWell/Holden Karnofsky posts you link to. When I disagree with other people it often comes down to me favoring more “cluster thinking”. For instance, these days this happens a lot to me when talking to people about AI timelines, or other aspects of AI risk.)
However, I think I disagree with your characterization of the case for CL more broadly, at least for certain uses/meanings of CL.
Here is one version of CL which I believe is based on much more than just expected-value calculations within a single model: This is roughly the claim that (i) in our project of doing as much good as possible we should at the highestlevel be mostly guided by very long-run effects and (ii) this makes an actual difference for how we plan and prioritize at intermediate levels.
Here are I have a picture in mind that is roughly as follows:
Lowest level: Which among several available actions should I take right now?
Intermediate levels:
What are the “methods” and inputs (quantitative models, heuristics, intuitions, etc.) I should use when thinking about the lowest level?
What systems, structures, and incentives should we put in place to “optimize” which lowest-level decision situations I and other agents find ourselves in in the first place?
How do I in turn best think about which methods, systems, structures, etc. to use for answering these intermediate-level questions?
Etc.
Highest level: How should I ultimately evaluate the intermediate levels?
So the following would be one instance of part (i) of my favored CL claim: When deciding whether to use cluster thinking or sequence thinking for a decision, we should aim to choose whichever type of thinking best helps us find the option with most valuable long-run effects. For this it is not required that I make the choice between sequence thinking or cluster thinking by an expected-value calculation, or indeed any direct appeal to any long-run effects. But, ultimately, if I think that, say, cluster thinking is superior to sequence thinking for the matter at hand, then I do so because I think this will lead to the best long-run consequences.
And these would be an instances of part (ii): That often we should decide primarily based on the proxy of “what does most reduce existential risk?”; that it seems good to increase the “representation” of future generations in various political contexts; etc.
Regarding what the case for this version of CL rests on:
For part (i), I think it’s largely a matter of ethics/philosophy, plus some high-level empirical claims about the world (the future being big etc.). Overall very similar to the case for AL. I think the ethics part is less in need of “cluster thinking”, “robustness” etc. And that the empirical part is, in fact, quite “robustly” supported.
[This point made me most want to push back against your initial claim about CL:] For part (ii), I think there are several examples of proxy goals, methods, interventions, etc., that are commonly pursued by longtermists which have a somewhat robust case behind them that does not just rely on an expected value estimate based on a single quantitative model. For instance, avoiding extinction seems very important from a variety of moral perspectives as well as common sense, there are historical precedents of research and advocacy at least partly motivated by this goal (e.g. nuclear winter, asteroid detection, perhaps even significant parts of environmentalism), there is a robust case for several risks longtermists commonly worry about (including AI), etc. More broadly, conversations involving explicit expected value estimates, quantitative models, etc. are only a fraction of the longtermist conversations I’m seeing. (If anything I might think that longtermists, at least in some contexts, make too little use of these tools.) E.g. look at the frontpage of LessWrong, or their curated content. I’m certainly not among the biggest fans of LessWrong or the rationality community, but I think it would be fairly inaccurate to say that a lot of what is happening there is people making explicit expected value estimates. Ditto for longtermist content featured in the EA Newsletter, etc. etc. I struggle to think of any example I’ve seen where a longtermist has made an important decision based just on a single EV estimate.
Rereading your initial comment introducing AL and CL, I’m less sure if by CL you had in mind something similar to what I’m defending above. There certainly are other readings that seem to hinge more on explicit EV reasoning or that are just absurd, e.g. “CL = never explicitly reason about anything happening in the next 100 years”. However, I’m less interested in these versions since they to me would seem to be a poor description of how longtermists actually reason and act in practice.
Not sure this “many weak arguments” way of looking at it is quite correct either had a quick look at the arguments given against longtermism and there are not that many of them. Maybe a better point is that there are many avenues and approaches that remain unexplored.
tl;dr – The case for giving to GiveWell top charities is based on much more more than just expected value calculations.
The case for longtermism (CL) is not based on much more than expected value calculations, in fact many non-expected value arguments currently seem to point the other way. This has lead to a situation where there are many weak arguments against longtermsim and one very strong argument for longtermism. This is hard to evaluate.
We (longtermists) should recognise that we are new and there is still work to be done to build a good theoretical base for longtermism.
Hi Max,
Good question. Thank you for asking.
– –
The more I have read by GiveWell (and to a lesser degree by groups such as Charity Entrepreneurship and Open Philanthropy) the more it is apparent to me that the case for giving to the global poor is not based solely on expected value but is based on a very broad variety of arguments.
For example I recommend reading:
https://blog.givewell.org/2014/06/10/sequence-thinking-vs-cluster-thinking
https://blog.givewell.org/2011/08/18/why-we-cant-take-expected-value-estimates-literally-even-when-theyre-unbiased/
https://www.givewell.org/modeling-extreme-model-uncertainty
https://forum.effectivealtruism.org/posts/h6uXkwFzqqr2JdZ4e/joey-savoie-tools-for-decision-making
The rough pattern of these posts is that taking a broad variety of different decision making tools and approaches and seeing where they all converge and point too is better than just looking at expected value (or using any other single tool). That expected value calculations are not the only way to make decisions and that the arguments for giving to the global poor would be unconvincing if solely based on expected value cautions and not on historical evidence, good feedback loops, expert views, strategic considerations, etc, etc. then the authors would not be convinced.
For example in [1.] Holden describes how he was initially sceptical that:
”donations can do more good when targeting the developing-world poor rather than the developed-world poor “
but he goes onto says that:
”many (including myself) take these arguments more seriously on learning things like “people I respect mostly agree with this conclusion”; “developing-world charities’ activities are generally more robustly evidence-supported, in addition to cheaper”; “thorough, skeptical versions of ‘cost per life saved’ estimates are worse than the figures touted by charities, but still impressive”; “differences in wealth are so pronounced that “hunger” is defined completely differently for the U.S. vs. developing countries“; “aid agencies were behind undisputed major achievements such as the eradication of smallpox”; etc.”
– –
Now I am actually somewhat sceptical of some of this writing. I think much of it is a pushback against longtermism. Remember the global development EAs have had to weather the transition from “give to global health, it has the highest expected value” to “give to global health, it doesn’t have the highest expected value (longtermism has that) but is good for many other reasons”. So it is not surprising that they have gone on to express that there are many other reasons to care about global health that are not based in expected value calculations.
– –
But that possible “status quo bias” does not mean they are wrong. It is still the case that GiveWell have made a host of arguments for global health beyond expected value and that the longtermsim community has not done so. The longtermism community has not produced historical evidence or highlighted successful feedback loops or demonstrated that their reasoning is robust to a broad variety of possible worldviews or built strong expert consensus. (Although the case has been made that preventing extreme risks is robust to very many possible futures, so that at least is a good longtermist argument that is not based on expected value.)
In fact to some degree the opposite is the case. People who argue against longtermism have pointed to cases were long-term type planning historically led to totalitarianism or to the common-sense weirdness of longtermist conclusions etc. My own work into risk management suggests that especially when planning for disasters it is good to not put too much weight on expected value but to assume that something unexpected will happen.
The fact is that the longtermist community has much more weird conclusions than the global health community yet has put much less effort into justifying those conclusions.
– –
To me it looks like all this has lead to a situation where there are many weak arguments against longtermsim (CL) and one very strong argument for longtermism (AL->CL). This is problematic as it is very hard to compare one strong argument against many weak arguments and which side you fall on will depend largely on your empirical views and how you weigh up evidence. This ultimately leads to unconstructive debate.
– –
I think the longtermist view is likely roughly correct. But I think that the case for longtermism has not be made rigorously or even particularly well (certainly it does not stand up well to Holden’s “cluster thinking” ideals). I don’t see this as a criticism of the longtermist community as the community is super new and the paper arguing the case even just from the point of view of expected value is still in draft! I just think it is a misconception worth adding to the list that the community has finished making the case for longtermism – we should recognise our newness and that there is still work to be done and not pretend we have all the answers. The EA global health community has build this broad theoretical bases beyond expected value and so can we, or we can at least try.
– –
I would be curious to know the extent to which you agree with this?
Also, I think this way of mapping situation is a bit more nuanced here than in my previous comment so I want to acknowledge a subtle changing of views between by earlier comment and this one, ask that if you respond you respond to the views as set out here rather than above and of course thank you for your insightful comment that lead to my views evolving – thank you Max!
– –
– –
(PS. On the other topic you mention. [Edited: I am not yet sure of the extent to which I think] the ‘beware suspicious convergence’ counter-argument [applies] in this context. Is it suspicious that if you make a plan for 1000 years it looks very similar to if you make a plan for 10000 years? Is it suspicious that if I plan for 100000 years or 100 years what I do in the next 10 years looks the same? Is it suspicious that if I want to go from my house in the UK to Oslo the initial steps are very similar to if I want to go from my house to Australia – ie. book ticket, get bus to train station, get train to airport? Etc? [Would need to give this more thought but it is not obvious] )
Hi Sam, thank you for your thoughtful reply.
Here are some things we seem to agree on:
The cases for specific priorities or interventions that are commonly advocated based on a longtermist perspective (e.g. “work on technical AI safety”) are usually far from watertight. It could be valuable to improve them, by making them more “robust” or otherwise.
Expected-value calculations that are based on a single quantitative model have significant limitations. They can be useful as one of many inputs to a decision, but it would usually be bad to use them as one’s sole decision tool.
(I am actually a big fan of the GiveWell/Holden Karnofsky posts you link to. When I disagree with other people it often comes down to me favoring more “cluster thinking”. For instance, these days this happens a lot to me when talking to people about AI timelines, or other aspects of AI risk.)
However, I think I disagree with your characterization of the case for CL more broadly, at least for certain uses/meanings of CL.
Here is one version of CL which I believe is based on much more than just expected-value calculations within a single model: This is roughly the claim that (i) in our project of doing as much good as possible we should at the highest level be mostly guided by very long-run effects and (ii) this makes an actual difference for how we plan and prioritize at intermediate levels.
Here are I have a picture in mind that is roughly as follows:
Lowest level: Which among several available actions should I take right now?
Intermediate levels:
What are the “methods” and inputs (quantitative models, heuristics, intuitions, etc.) I should use when thinking about the lowest level?
What systems, structures, and incentives should we put in place to “optimize” which lowest-level decision situations I and other agents find ourselves in in the first place?
How do I in turn best think about which methods, systems, structures, etc. to use for answering these intermediate-level questions?
Etc.
Highest level: How should I ultimately evaluate the intermediate levels?
So the following would be one instance of part (i) of my favored CL claim: When deciding whether to use cluster thinking or sequence thinking for a decision, we should aim to choose whichever type of thinking best helps us find the option with most valuable long-run effects. For this it is not required that I make the choice between sequence thinking or cluster thinking by an expected-value calculation, or indeed any direct appeal to any long-run effects. But, ultimately, if I think that, say, cluster thinking is superior to sequence thinking for the matter at hand, then I do so because I think this will lead to the best long-run consequences.
And these would be an instances of part (ii): That often we should decide primarily based on the proxy of “what does most reduce existential risk?”; that it seems good to increase the “representation” of future generations in various political contexts; etc.
Regarding what the case for this version of CL rests on:
For part (i), I think it’s largely a matter of ethics/philosophy, plus some high-level empirical claims about the world (the future being big etc.). Overall very similar to the case for AL. I think the ethics part is less in need of “cluster thinking”, “robustness” etc. And that the empirical part is, in fact, quite “robustly” supported.
[This point made me most want to push back against your initial claim about CL:] For part (ii), I think there are several examples of proxy goals, methods, interventions, etc., that are commonly pursued by longtermists which have a somewhat robust case behind them that does not just rely on an expected value estimate based on a single quantitative model. For instance, avoiding extinction seems very important from a variety of moral perspectives as well as common sense, there are historical precedents of research and advocacy at least partly motivated by this goal (e.g. nuclear winter, asteroid detection, perhaps even significant parts of environmentalism), there is a robust case for several risks longtermists commonly worry about (including AI), etc. More broadly, conversations involving explicit expected value estimates, quantitative models, etc. are only a fraction of the longtermist conversations I’m seeing. (If anything I might think that longtermists, at least in some contexts, make too little use of these tools.) E.g. look at the frontpage of LessWrong, or their curated content. I’m certainly not among the biggest fans of LessWrong or the rationality community, but I think it would be fairly inaccurate to say that a lot of what is happening there is people making explicit expected value estimates. Ditto for longtermist content featured in the EA Newsletter, etc. etc. I struggle to think of any example I’ve seen where a longtermist has made an important decision based just on a single EV estimate.
Rereading your initial comment introducing AL and CL, I’m less sure if by CL you had in mind something similar to what I’m defending above. There certainly are other readings that seem to hinge more on explicit EV reasoning or that are just absurd, e.g. “CL = never explicitly reason about anything happening in the next 100 years”. However, I’m less interested in these versions since they to me would seem to be a poor description of how longtermists actually reason and act in practice.
Not sure this “many weak arguments” way of looking at it is quite correct either had a quick look at the arguments given against longtermism and there are not that many of them. Maybe a better point is that there are many avenues and approaches that remain unexplored.