Was thinking a bit about the how to make it real for people that the quarantine depressing the economy kills people just like Coronavirus does.
Was thinking about finding a simple good enough correlation between economic depression and death, then creating a “flattening the curve” graphic that shows how many deaths we would save from stopping the economic freefall at different points. Combining this was clear narratives about recession could be quite effective.
On the other hand, I think it’s quite plausible that this particular problem will take care of itself. When people begin to experience depression, will the young people who are the economic engine of the country really continue to stay home and quarantine themselves? It seems quite likely that we’ll simply become stratified for a while where young healthy people break quarantine, and the older and immuno-compromised stay home.
But getting the time of this right is everything. Striking the right balance of “deaths from economic freefall” and “deaths from an overloaded medical system” is a balancing act, going too far in either direction results in hundreds of thousands of unnecessary deaths.
Then I got to thinking about the effect of a depressed economy on x-risks from AI. Because the funding for AI safety is
1. Mostly in non-profits
and
2. Orders of magnitude smaller than funding for AI capabilities
It’s quite likely that the funding for AI safety is more inelastic in depressions than than the funding for AI capabilities. This may answer the puzzle of why more EAs and rationalists aren’t speaking cogently about the tradeoffs between depression and lives saved from Corona—they have gone through this same train of thought, and decided that preventing a depression is an information hazard.
It’s interesting because you would intuitively think this, but there is actually not terrible evidence linking periods of economic growth to increased mortality.
It’s interesting because you would intuitively think this, but there is actually not terrible evidence linking periods of economic growth to increased mortality.
Wow that is fascinating. It does make the case harder to make because you have to start quantifying happiness/depression, etc and trade off against lives. Much much harder to simplify enough to make it viral. Updates towards capitalism being horrible.
Is non-profit funding really that inelastic in depression?
It probably varies quite a bit by sector, and where funding comes from for different non-profits. In the case of AI safety I think it’s likely more inelastic than AI capability.
Wait, you received evidence that didn’t just refute your hypothesis, it reversed it. If you accept that, shouldn’t you also reverse your proposed remedy? Shouldn’t you now argue _IN FAVOR_ of shutting down more completely—it saves lives both directly by limiting the spread of the virus AND indirectly by slowing the economy.
(note: this is intended to be semi-humorous—my base position is that the economic causes and effects are far too complex and distributed to really predict impact on that level, or to predict what policies might improve what outcomes).
Was thinking a bit about the how to make it real for people that the quarantine depressing the economy kills people just like Coronavirus does.
Was thinking about finding a simple good enough correlation between economic depression and death, then creating a “flattening the curve” graphic that shows how many deaths we would save from stopping the economic freefall at different points. Combining this was clear narratives about recession could be quite effective.
On the other hand, I think it’s quite plausible that this particular problem will take care of itself. When people begin to experience depression, will the young people who are the economic engine of the country really continue to stay home and quarantine themselves? It seems quite likely that we’ll simply become stratified for a while where young healthy people break quarantine, and the older and immuno-compromised stay home.
But getting the time of this right is everything. Striking the right balance of “deaths from economic freefall” and “deaths from an overloaded medical system” is a balancing act, going too far in either direction results in hundreds of thousands of unnecessary deaths.
Then I got to thinking about the effect of a depressed economy on x-risks from AI. Because the funding for AI safety is
1. Mostly in non-profits
and
2. Orders of magnitude smaller than funding for AI capabilities
It’s quite likely that the funding for AI safety is more inelastic in depressions than than the funding for AI capabilities. This may answer the puzzle of why more EAs and rationalists aren’t speaking cogently about the tradeoffs between depression and lives saved from Corona—they have gone through this same train of thought, and decided that preventing a depression is an information hazard.
It’s interesting because you would intuitively think this, but there is actually not terrible evidence linking periods of economic growth to increased mortality.
Here is the article in nature.
Is non-profit funding really that inelastic in depression?
Wow that is fascinating. It does make the case harder to make because you have to start quantifying happiness/depression, etc and trade off against lives. Much much harder to simplify enough to make it viral. Updates towards capitalism being horrible.
It probably varies quite a bit by sector, and where funding comes from for different non-profits. In the case of AI safety I think it’s likely more inelastic than AI capability.
It was brought to my attention on Lesswrong that depressions actually save lives.
Which would make it much harder to build a simple “two curves to flatten” narrative out of.
Wait, you received evidence that didn’t just refute your hypothesis, it reversed it. If you accept that, shouldn’t you also reverse your proposed remedy? Shouldn’t you now argue _IN FAVOR_ of shutting down more completely—it saves lives both directly by limiting the spread of the virus AND indirectly by slowing the economy.
(note: this is intended to be semi-humorous—my base position is that the economic causes and effects are far too complex and distributed to really predict impact on that level, or to predict what policies might improve what outcomes).
I did update from this quite significantly.