In general our ability to measure long term effects is kind of lousy. But if I wanted to look for interventions which don’t have that decay pattern it would be most natural to think of conservation work saving species from extinction. Once we’ve lost biodiversity, it’s essentially gone (maybe taking millions of years to build up again naturally). Conservation work can stop that. And with rises in conservation work over time it’s quite plausible that early saving species won’t just lead to them going extinct slightly later, but being preserved indefinitely.
I was not clear above, but I meant (posterior) counterfactual impact under expectedtotalhedonisticutilitarianism. Even if a species is counterfactually preserved indefinitely due to actions now, which I think would be very hard, I do not see how it would permanently increase wellbeing. In addition, I meant to ask for actual empirical evidence as opposed to hypothetical examples (e.g. of one species being saved and making an immortal conservationist happy indefinitely).
I think this is something where our ability to measure is just pretty bad, and in particular our ability to empirically detect whether the type of things that plausibly have long lasting counterfactual impacts actually do is pretty terrible.
I respond to that by saying “ok I guess empirics aren’t super helpful for the big picture question let’s try to build mechanistic understanding of things grounded wherever possible in empirics, as well as priors about what types of distributions occur when various different generating mechanisms are at play”, whereas it sounds like you’re responding by saying something like “well as a prior we’ll just use the parts of the distribution we can actually measure, and assume that generalizes unless we get contradictory data”?
I respond to that by saying “ok I guess empirics aren’t super helpful for the big picture question let’s try to build mechanistic understanding of things grounded wherever possible in empirics, as well as priors about what types of distributions occur when various different generating mechanisms are at play”, whereas it sounds like you’re responding by saying something like “well as a prior we’ll just use the parts of the distribution we can actually measure, and assume that generalizes unless we get contradictory data”?
Yes, that would be my reply. Thanks for clarifying.
Yeah, so I basically think that that response feels “spiritually frequentist”, and is more likely to lead you to large errors than the approach I outlined (which feels more “spiritually Bayesian”), especially in cases like this where we’re trying to extrapolate significantly beyond the data we’ve been able to gather.
In general our ability to measure long term effects is kind of lousy. But if I wanted to look for interventions which don’t have that decay pattern it would be most natural to think of conservation work saving species from extinction. Once we’ve lost biodiversity, it’s essentially gone (maybe taking millions of years to build up again naturally). Conservation work can stop that. And with rises in conservation work over time it’s quite plausible that early saving species won’t just lead to them going extinct slightly later, but being preserved indefinitely.
I was not clear above, but I meant (posterior) counterfactual impact under expected total hedonistic utilitarianism. Even if a species is counterfactually preserved indefinitely due to actions now, which I think would be very hard, I do not see how it would permanently increase wellbeing. In addition, I meant to ask for actual empirical evidence as opposed to hypothetical examples (e.g. of one species being saved and making an immortal conservationist happy indefinitely).
I think this is something where our ability to measure is just pretty bad, and in particular our ability to empirically detect whether the type of things that plausibly have long lasting counterfactual impacts actually do is pretty terrible.
I respond to that by saying “ok I guess empirics aren’t super helpful for the big picture question let’s try to build mechanistic understanding of things grounded wherever possible in empirics, as well as priors about what types of distributions occur when various different generating mechanisms are at play”, whereas it sounds like you’re responding by saying something like “well as a prior we’ll just use the parts of the distribution we can actually measure, and assume that generalizes unless we get contradictory data”?
Yes, that would be my reply. Thanks for clarifying.
Yeah, so I basically think that that response feels “spiritually frequentist”, and is more likely to lead you to large errors than the approach I outlined (which feels more “spiritually Bayesian”), especially in cases like this where we’re trying to extrapolate significantly beyond the data we’ve been able to gather.