Partly from a scepticism about the highly speculative arguments for ‘direct’ longtermist work—on which I think my prior is substantially lower than most of the longtermist community (though I strongly suspect selection effects, and that this scepticism would be relatively broadly shared further from the core of the movement).
Partly from something harder to pin down, that good outcomes do tend to cluster in a way that e.g. Givewell seem to recognise, but AFAIK have never really tried to account for (in late 2022, they were still citing that post while saying ‘we basically ignore these’). So if we’re trying to imagine the whole picture, we need to have some kind of priors anyway.* Mine are some combination of considerations like
there are a huge number of ways in which people tend to behave more generously when they receive generosity, and it’s possible the ripple effects of this are much bigger than we realise (small ripples over a wide group of people that are invisibly small per-person could still be momentous);
having healthier, more economically developed people will tend to lead to more having more economically developed regions (I didn’t find John’s arguments against randomistas driving growth persuasive—e.g. IIRC it looked at absolute effect size of randomista-driven growth without properly accounting for the relative budgets vs other interventions. Though if he is right, I might make the following arguments about short term growth policies vs longtermism);
having more economically countries seems better for global political stability than having fewer, so reduce the risk of global catastrophes;
having more economically developed countries seems better for global resilience to catastrophe than having fewer, so reduce the magnitude of global catastrophes;
even ‘minor’ (i.e. non-extinction) global catastrophes can have a substantial reduction on our long-term prospects, so reducing their risk and magnitude is a potentially big deal
tighter feedback loops and better data mean we can learn more about incidental-optimisations than we can with longtermism work, including ones we didn’t know at the time we wanted to optimise for—we build up a corpus of real-world data that can be referred to whenever we think of a new consideration
tighter feedback loops also mean I expect the people working on it to be more effective at what they do, and less susceptible to (being selected by or themselves being subject to) systemic biases/groupthink/motivated reasoning etc.
the combination of greater evidence base and tighter feedback loops has countless other ineffable reinforcing-general-good benefits, like greater probability of shutting down when having 0 or negative effect; better signalling; greater reasoning transparency; easier measurement of Shapley values vs rather than counterfactuals; faster and better process refinement etc
Partly from a scepticism about the highly speculative arguments for ‘direct’ longtermist work—on which I think my prior is substantially lower than most of the longtermist community (though I strongly suspect selection effects, and that this scepticism would be relatively broadly shared further from the core of the movement).
Partly from something harder to pin down, that good outcomes do tend to cluster in a way that e.g. Givewell seem to recognise, but AFAIK have never really tried to account for (in late 2022, they were still citing that post while saying ‘we basically ignore these’). So if we’re trying to imagine the whole picture, we need to have some kind of priors anyway.* Mine are some combination of considerations like
there are a huge number of ways in which people tend to behave more generously when they receive generosity, and it’s possible the ripple effects of this are much bigger than we realise (small ripples over a wide group of people that are invisibly small per-person could still be momentous);
having healthier, more economically developed people will tend to lead to more having more economically developed regions (I didn’t find John’s arguments against randomistas driving growth persuasive—e.g. IIRC it looked at absolute effect size of randomista-driven growth without properly accounting for the relative budgets vs other interventions. Though if he is right, I might make the following arguments about short term growth policies vs longtermism);
having more economically countries seems better for global political stability than having fewer, so reduce the risk of global catastrophes;
having more economically developed countries seems better for global resilience to catastrophe than having fewer, so reduce the magnitude of global catastrophes;
even ‘minor’ (i.e. non-extinction) global catastrophes can have a substantial reduction on our long-term prospects, so reducing their risk and magnitude is a potentially big deal
tighter feedback loops and better data mean we can learn more about incidental-optimisations than we can with longtermism work, including ones we didn’t know at the time we wanted to optimise for—we build up a corpus of real-world data that can be referred to whenever we think of a new consideration
tighter feedback loops also mean I expect the people working on it to be more effective at what they do, and less susceptible to (being selected by or themselves being subject to) systemic biases/groupthink/motivated reasoning etc.
the combination of greater evidence base and tighter feedback loops has countless other ineffable reinforcing-general-good benefits, like greater probability of shutting down when having 0 or negative effect; better signalling; greater reasoning transparency; easier measurement of Shapley values vs rather than counterfactuals; faster and better process refinement etc