Ok, although it’s probably worth noting that climate change is generally not considered to be an existential risk so I’m not sure considerations of emissions/net zero are all that relevant here. I think population change is more relevant in terms of impacts on economic growth / tech stagnation which in turn should have an impact on existential risk.
Ord (2020) listed climate change as an x-risk. Though, on reflection, he may have said that 1/1000 was an absolute upper bound and he thought the actual risk was lower than that.
I have a hard time understanding stories not mediated through climate change or resource shortage (which seems closely linked to climate change, in that many resource limits boil down to carbon emissions) about how population growth in Africa could lead to higher existential risk—particularly in a context where global population seems like it will hit a peak and then decline sometime in the second half of the 21st century. Most of the pathways I can imagine would point to lower existential risk. If the starting point is that bednet distribution leads to lower existential risk, there isn’t really a dilemma, and so that case seemed less interesting to analyse. So that’s probably one reason I saw more value in starting my analysis with the climate change angle.
However, there are probably causal possibilities I’ve missed. I’d be interested to hear what you think they might be. I do think someone should try to examine those more closely in order to try and put reasonable probabilistic bounds around them.
I certainly don’t think the analysis above is complete. As I said in the post, the intent was to demonstrate how we could “dissolve” or reduce some moral cluelessness to ordinary probabilistic uncertainty using careful reason and evidence to evaluate possible causal pathways. I think the analysis above is a start and a demonstration that we can reduce uncertainty through reasoned analysis of evidence. But we’d definitely need a more extended analysis to act. Then, we can take an expected value approach to work out the likely benefit of our actions.
Ok, although it’s probably worth noting that climate change is generally not considered to be an existential risk so I’m not sure considerations of emissions/net zero are all that relevant here. I think population change is more relevant in terms of impacts on economic growth / tech stagnation which in turn should have an impact on existential risk.
Ord (2020) listed climate change as an x-risk. Though, on reflection, he may have said that 1/1000 was an absolute upper bound and he thought the actual risk was lower than that.
I have a hard time understanding stories not mediated through climate change or resource shortage (which seems closely linked to climate change, in that many resource limits boil down to carbon emissions) about how population growth in Africa could lead to higher existential risk—particularly in a context where global population seems like it will hit a peak and then decline sometime in the second half of the 21st century. Most of the pathways I can imagine would point to lower existential risk. If the starting point is that bednet distribution leads to lower existential risk, there isn’t really a dilemma, and so that case seemed less interesting to analyse. So that’s probably one reason I saw more value in starting my analysis with the climate change angle.
However, there are probably causal possibilities I’ve missed. I’d be interested to hear what you think they might be. I do think someone should try to examine those more closely in order to try and put reasonable probabilistic bounds around them.
I certainly don’t think the analysis above is complete. As I said in the post, the intent was to demonstrate how we could “dissolve” or reduce some moral cluelessness to ordinary probabilistic uncertainty using careful reason and evidence to evaluate possible causal pathways. I think the analysis above is a start and a demonstration that we can reduce uncertainty through reasoned analysis of evidence. But we’d definitely need a more extended analysis to act. Then, we can take an expected value approach to work out the likely benefit of our actions.