I think this is a very good point, and it’s helping shape my ideas on this topic, thank you!
I guess it’s true that most/all candidates for longtermist interventions that I’ve seen are based on attractor states. At the same time, it’s useful to think about whether we might be missing any potential longtermist interventions by focusing on these attractor state cases. One such example that plausibly might fit into this category is an intervention that broadly improves institutional decision-making. Perhaps here, interventions plausibly have a long run positive impact on future value but we are worried that this will be “washed out” by other factors. It’s not clear that there’s an obvious attractor state involved. (Note that I’m not very confident in this; I could easily be persuaded otherwise. Maybe people advocate for improving institutional decision-making on the basis that it reduces the risk of many different bad attractor states.)
Thinking about this type of intervention, the results of my model can be read either pessimistically or optimistically from the longtermist’s perspective (depending on your beliefs about the nature of the parameters):
Optimistic: there are potentially cases where a longtermist intervention that’s not based on an attractor state can have very large long-run benefits. If forecasting error increases sub-linearly or just relatively slowly, then an intervention can be good from a longtermist perspective even if there’s no attractor state involved.
Pessimistic: for lots of plausible parameter values (e.g. high alpha, linearly increasing forecasting error), long run benefits wash out. If this is true across a wide range of potential interventions, then attractor states are perhaps the only way out of this trap.
I think this is a very good point, and it’s helping shape my ideas on this topic, thank you!
I guess it’s true that most/all candidates for longtermist interventions that I’ve seen are based on attractor states. At the same time, it’s useful to think about whether we might be missing any potential longtermist interventions by focusing on these attractor state cases. One such example that plausibly might fit into this category is an intervention that broadly improves institutional decision-making. Perhaps here, interventions plausibly have a long run positive impact on future value but we are worried that this will be “washed out” by other factors. It’s not clear that there’s an obvious attractor state involved. (Note that I’m not very confident in this; I could easily be persuaded otherwise. Maybe people advocate for improving institutional decision-making on the basis that it reduces the risk of many different bad attractor states.)
Thinking about this type of intervention, the results of my model can be read either pessimistically or optimistically from the longtermist’s perspective (depending on your beliefs about the nature of the parameters):
Pessimistic: for lots of plausible parameter values (e.g. high alpha, linearly increasing forecasting error), long run benefits wash out. If this is true across a wide range of potential interventions, then attractor states are perhaps the only way out of this trap.