General suspicion of the move away from expected-value calculations and cost-effectiveness analyses.
This is a portion taken from a (forthcoming) post about some potential biases and mistakes in effective altruism that I’ve analyzed via looking at cost-effectiveness analysis. Here, I argue that the general move (at least outside of human and animal neartermism) away from Fermi estimates, expected values, and other calculations just makes those biases harder to see, rather than fix the original biases.
I may delete this section from the actual post as this point might be a distraction from the overall point.
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I’m sure there are very good reasons (some stated, some unstated) for moving away from cost-effectiveness analysis. But I’m overall pretty suspicious of the general move, for a similar reason that I’d be suspicious of non-EAs telling me that we shouldn’t use cost-effectiveness analyses to judge their work, in favor of say systematic approaches, good intuitions, and specific contexts like lived experiences (cf. Beware Isolated Demands for Rigor):
I’m sure you have specific arguments for why in your case quantitative approaches aren’t very necessary and useful, because your uncertainties span multiple orders of magnitude, because all the calculations are so sensitive to initial assumptions, and so forth. But none of these arguments really point to verbal heuristics suddenly (despite approximately all evidence and track records to the contrary) performing better than quantitative approaches.
In addition to the individual epistemic issues with verbal assessments unmoored by numbers, we also need to consider the large communicative sacrifices made by not having a shared language (mathematics) to communicate things like uncertainty and effect sizes. Indeed, we have ample evidence that switching away from numerical reasoning when communicating uncertainty is a large source of confusion.
To argue that in your specific situation, verbal judgment is superior without numbers than with numbers, never mind that your proposed verbal solutions obviates the biases associated with trying to do numerical cost-effectiveness modeling of the same, the strength of your evidence and arguments needs to be overwhelming. Instead, I get some simple verbal heuristic-y arguments, and all of this is quite suspicious.
Or more succinctly:
It’s easy to lie with numbers, but it’s even easier to lie without them
So overall I don’t think moving away from explicit expected value calculations and cost-effectiveness analyses is much of a solution, if at all, for the high-level reasoning mistakes and biases that are more clearly seen in cost-effectiveness analyses. Most of what the shift away from EV does is makes things less grounded in reality, less transparent and harder to critique (cf. “Not Even Wrong”).
What kinds of things do you think it would be helpful to do cost effectiveness analyses of? Are you looking for cost effectiveness analyses of problem areas or specific interventions?
I think it would be valuable to see quantitative estimates of more problem areas and interventions. My order of magnitude estimate would be that if one is considering spending $10,000-$100,000, one should do a simple scale, neglectedness, and tractability analysis. But if one is considering spending $100,000-$1 million, one should do an actual cost-effectiveness analysis. So candidates here would be wild animal welfare, approval voting, improving institutional decision-making, climate change from an existential risk perspective, biodiversity from an existential risk perspective, governance of outer space etc. Though it is a significant amount of work to get a cost-effectiveness analysis up to peer review publishable quality (which we have found requires moving beyond Guesstimate, e.g. here and here), I still think that there is value in doing a rougher Guesstimate model and having a discussion about parameters. One could even add to one of our Guesstimate models, allowing a direct comparison with AGI safety and resilient foods or interventions for loss of electricity/industry from a long-term perspective.
Hmm one recent example is that somebody casually floated to me an idea that can potentially entirely solve an existential risk (though the solution might have downside risks of its own) and I realized then that I had no idea how much to price the solution in terms of EA $s, like whether it should be closer to 100M, 1B or $100B.
My first gut instinct was to examine the solution and also to probe the downside risks, but then I realized this is thinking about it entirely backwards. The downside risks and operational details don’t matter if even the most optimistic cost-effectiveness analyses isn’t enough to warrant this being worth funding!
General suspicion of the move away from expected-value calculations and cost-effectiveness analyses.
This is a portion taken from a (forthcoming) post about some potential biases and mistakes in effective altruism that I’ve analyzed via looking at cost-effectiveness analysis. Here, I argue that the general move (at least outside of human and animal neartermism) away from Fermi estimates, expected values, and other calculations just makes those biases harder to see, rather than fix the original biases.
I may delete this section from the actual post as this point might be a distraction from the overall point.
____
I’m sure there are very good reasons (some stated, some unstated) for moving away from cost-effectiveness analysis. But I’m overall pretty suspicious of the general move, for a similar reason that I’d be suspicious of non-EAs telling me that we shouldn’t use cost-effectiveness analyses to judge their work, in favor of say systematic approaches, good intuitions, and specific contexts like lived experiences (cf. Beware Isolated Demands for Rigor):
Or more succinctly:
So overall I don’t think moving away from explicit expected value calculations and cost-effectiveness analyses is much of a solution, if at all, for the high-level reasoning mistakes and biases that are more clearly seen in cost-effectiveness analyses. Most of what the shift away from EV does is makes things less grounded in reality, less transparent and harder to critique (cf. “Not Even Wrong”).
What kinds of things do you think it would be helpful to do cost effectiveness analyses of? Are you looking for cost effectiveness analyses of problem areas or specific interventions?
I think it would be valuable to see quantitative estimates of more problem areas and interventions. My order of magnitude estimate would be that if one is considering spending $10,000-$100,000, one should do a simple scale, neglectedness, and tractability analysis. But if one is considering spending $100,000-$1 million, one should do an actual cost-effectiveness analysis. So candidates here would be wild animal welfare, approval voting, improving institutional decision-making, climate change from an existential risk perspective, biodiversity from an existential risk perspective, governance of outer space etc. Though it is a significant amount of work to get a cost-effectiveness analysis up to peer review publishable quality (which we have found requires moving beyond Guesstimate, e.g. here and here), I still think that there is value in doing a rougher Guesstimate model and having a discussion about parameters. One could even add to one of our Guesstimate models, allowing a direct comparison with AGI safety and resilient foods or interventions for loss of electricity/industry from a long-term perspective.
I agree with the general flavor of what you said, but am unsure about the exact numbers.
Hmm one recent example is that somebody casually floated to me an idea that can potentially entirely solve an existential risk (though the solution might have downside risks of its own) and I realized then that I had no idea how much to price the solution in terms of EA $s, like whether it should be closer to 100M, 1B or $100B.
My first gut instinct was to examine the solution and also to probe the downside risks, but then I realized this is thinking about it entirely backwards. The downside risks and operational details don’t matter if even the most optimistic cost-effectiveness analyses isn’t enough to warrant this being worth funding!