Note that “humanity is doomed” is not the same as ‘direct extinction’, as there are many other ways for us to waste our potential.
I think its an interesting argument, but I’m unsure that we can get to a rigorous, defensible distinction between ‘direct’ and ‘indirect’ risks. I’m also unsure how this framework fits with the “risk/risk factor” framework, or the ‘hazard/vulnerability/exposure’ framework that’s common across disaster risk reduction, business + govt planning, etc. I’d be interested in hearing more in favour of this view, and in favour of the 2 claims I picked out above.
We’ve talked about this before, but in general I’ve got such uncertainty about the state of our knowledge and the future of the world that I incline towards grouping together nuclear, bio and climate as being in roughly the same scale/importance ‘tier’ and then spending most of our focus seeing if any particular research strand or intervention is neglected and solvable (e.g. your work flagging something underexplored like cement).
On your food production point, as I understand it the issue is more shocks than averages. Food system shocks that can lead to “economic shocks, socio-political instability as well as starvation, migration and conflict” (from the ‘causal loop diagram’ paper). However, I’m not a food systems expert, I’d suggest the best people to discuss this with more are our Catherine Richards and Asaf Tzachor, authors of e.g. Future Foods For Risk-Resilient Diets.
I’m not sure I understand why you don’t think the in/direct distinction is useful.
I have worked on climate risk for many years and I genuinely don’t understand how one could think it is in the same ballpark as AI, biorisk or nuclear risk. This is especially true now that the risk of >6 degrees seems to be negligible. If I read about biorisk, I can immediately see the argument for how it could kill more than 50% of the population in the next 10-20 years. With climate change, for all the literature I have read, I just don’t understand how one could think that.
You seem to think the world is extremely sensitive to what the evidence suggests will be agricultural disturbances that we live through all the time: the shocks are well within the normal range of shocks that we might expect to see in any decade, for instance. This chart shows the variation in the food price index. Between 2004 and 2011, it increased by about 200%. This is much much bigger than any posited effects of climate change that I have seen. One could also draw lots of causal arrows from this to various GCRs. Yet, I don’t see many EAs argue for working on whatever were the drivers of these changes in food prices.
I have worked on climate risk for many years and I genuinely don’t understand how one could think it is in the same ballpark as AI, biorisk or nuclear risk
Note that the OP did not include “AI” in their list of risks that they think of as the same tier as climate risk.
Many thanks to you & the others in the comments for the insightful discussion. Could you clarify a few points:
You state that 2.5 degrees warming by 2100 is widely accepted as the likely outcome of ‘business as usual’ - does this correspond to one of the IPCC scenarios?
You state that >6 degrees warming by 2100 is highly unlikely (risk seems ‘negligible’). Again, is this conclusion drawn from the IPCC report
If you have any additional resources to back these statements up I would love to read them—thanks!
I think all effects in practice are indirect, but “direct” can be used to mean a causal effect about which we have direct evidence, i.e. we made observations about the cause on the outcome without need for discussing intermediate outcomes, not from piecing multiple steps of causal effects together in a chain. The longer the causal chain, the more likely there are to be effects in the opposite direction along parallel chains. Furthermore, we should generally be skeptical of any causal claim, so the longer the causal chain, the more claims of which we should be skeptical, and the weaker we should expect the overall effect.
Note that “humanity is doomed” is not the same as ‘direct extinction’, as there are many other ways for us to waste our potential.
I think its an interesting argument, but I’m unsure that we can get to a rigorous, defensible distinction between ‘direct’ and ‘indirect’ risks. I’m also unsure how this framework fits with the “risk/risk factor” framework, or the ‘hazard/vulnerability/exposure’ framework that’s common across disaster risk reduction, business + govt planning, etc. I’d be interested in hearing more in favour of this view, and in favour of the 2 claims I picked out above.
We’ve talked about this before, but in general I’ve got such uncertainty about the state of our knowledge and the future of the world that I incline towards grouping together nuclear, bio and climate as being in roughly the same scale/importance ‘tier’ and then spending most of our focus seeing if any particular research strand or intervention is neglected and solvable (e.g. your work flagging something underexplored like cement).
On your food production point, as I understand it the issue is more shocks than averages. Food system shocks that can lead to “economic shocks, socio-political instability as well as starvation, migration and conflict” (from the ‘causal loop diagram’ paper). However, I’m not a food systems expert, I’d suggest the best people to discuss this with more are our Catherine Richards and Asaf Tzachor, authors of e.g. Future Foods For Risk-Resilient Diets.
I’m not sure I understand why you don’t think the in/direct distinction is useful.
I have worked on climate risk for many years and I genuinely don’t understand how one could think it is in the same ballpark as AI, biorisk or nuclear risk. This is especially true now that the risk of >6 degrees seems to be negligible. If I read about biorisk, I can immediately see the argument for how it could kill more than 50% of the population in the next 10-20 years. With climate change, for all the literature I have read, I just don’t understand how one could think that.
You seem to think the world is extremely sensitive to what the evidence suggests will be agricultural disturbances that we live through all the time: the shocks are well within the normal range of shocks that we might expect to see in any decade, for instance. This chart shows the variation in the food price index. Between 2004 and 2011, it increased by about 200%. This is much much bigger than any posited effects of climate change that I have seen. One could also draw lots of causal arrows from this to various GCRs. Yet, I don’t see many EAs argue for working on whatever were the drivers of these changes in food prices.
Note that the OP did not include “AI” in their list of risks that they think of as the same tier as climate risk.
Hi John,
Many thanks to you & the others in the comments for the insightful discussion. Could you clarify a few points:
You state that 2.5 degrees warming by 2100 is widely accepted as the likely outcome of ‘business as usual’ - does this correspond to one of the IPCC scenarios?
You state that >6 degrees warming by 2100 is highly unlikely (risk seems ‘negligible’). Again, is this conclusion drawn from the IPCC report
If you have any additional resources to back these statements up I would love to read them—thanks!
I think you’ll find answers to those questions in section 1 of John and Johannes’s recent post on climate projections. IIRC the answers are yes, and those numbers correspond to RCP4.5.
I think all effects in practice are indirect, but “direct” can be used to mean a causal effect about which we have direct evidence, i.e. we made observations about the cause on the outcome without need for discussing intermediate outcomes, not from piecing multiple steps of causal effects together in a chain. The longer the causal chain, the more likely there are to be effects in the opposite direction along parallel chains. Furthermore, we should generally be skeptical of any causal claim, so the longer the causal chain, the more claims of which we should be skeptical, and the weaker we should expect the overall effect.