Although you have addressed the question of uncertainty in recent work, I am not seeing it implemented fully in your tools. I’d like to see an in-depth treatment (and incorporation into your tools) of the position stated by Andreas Mogensen in his paper ‘Maximal Cluelessness’, Global Priorities Institute Working Paper No. 2/2019:
“We lack a compelling decision theory that is consistent with a long-termist perspective and does not downplay the depth of our uncertainty while supporting orthodox effective altruist conclusions about cause prioritisation.”
In my view, if one accepts 100% the implications of maximal cluelessness (which is ever more strongly supported by dynamical systems and chaos theory, the more longtermist the perspective), then the logical conclusion from that position is to fund projects randomly, with random amounts.
The RP team may wish to consider prioritising the study of complexity and dynamical systems etc. as part of their continuing professional development (CPD). I recommend the courses offered by the Santa Fe Institute. You can register for most courses at any time, but the agent-based modelling course requires registration and starts at the end of August: https://www.complexityexplorer.org/courses/183-introduction-to-agent-based-modeling
Stephen Hawking famously once said that the 21st century would be the century of complexity. I wholeheartedly agree. IMHO, in these non-linear times, it should be a part of every scientist’s (and philosopher’s) basic education.
Thanks for raising this point. We think that choosing the right decision theory that can handle imprecise probabilities is a complex issue that has not been adequately resolved. We take the point that Mogensen’s conclusions have radical implications for the EA community at large and we haven’t formulated a compelling story about where Mogensen goes wrong. However, we also believe that there are likely to be solutions that will most likely avoid those radical implications, and so we don’t need to bracket all of the cause prioritization work until we find them. Our tools may only be useful to those who see there to be work done on cause prioritization.
As a practical point, our Cross-Cause Cost-Effectiveness Model handles precise probabilities with Monte Carlo methods by randomly selecting individual values for parameters in each outcome from a distribution. We noted hesitance about enforcing a specific distribution over our range of radical uncertainty, but we stand behind this as a reasonable choice given our pragmatic aims. If the alternative is not to try to calculate relative expected values, we think that would be a loss, even if our own results have methodological doubts still attached to them.
Although you have addressed the question of uncertainty in recent work, I am not seeing it implemented fully in your tools. I’d like to see an in-depth treatment (and incorporation into your tools) of the position stated by Andreas Mogensen in his paper ‘Maximal Cluelessness’, Global Priorities Institute Working Paper No. 2/2019:
“We lack a compelling decision theory that is consistent with a long-termist perspective and does not downplay the depth of our uncertainty while supporting orthodox effective altruist conclusions about cause prioritisation.”
In my view, if one accepts 100% the implications of maximal cluelessness (which is ever more strongly supported by dynamical systems and chaos theory, the more longtermist the perspective), then the logical conclusion from that position is to fund projects randomly, with random amounts.
The RP team may wish to consider prioritising the study of complexity and dynamical systems etc. as part of their continuing professional development (CPD). I recommend the courses offered by the Santa Fe Institute. You can register for most courses at any time, but the agent-based modelling course requires registration and starts at the end of August: https://www.complexityexplorer.org/courses/183-introduction-to-agent-based-modeling
Stephen Hawking famously once said that the 21st century would be the century of complexity. I wholeheartedly agree. IMHO, in these non-linear times, it should be a part of every scientist’s (and philosopher’s) basic education.
Thanks for raising this point. We think that choosing the right decision theory that can handle imprecise probabilities is a complex issue that has not been adequately resolved. We take the point that Mogensen’s conclusions have radical implications for the EA community at large and we haven’t formulated a compelling story about where Mogensen goes wrong. However, we also believe that there are likely to be solutions that will most likely avoid those radical implications, and so we don’t need to bracket all of the cause prioritization work until we find them. Our tools may only be useful to those who see there to be work done on cause prioritization.
As a practical point, our Cross-Cause Cost-Effectiveness Model handles precise probabilities with Monte Carlo methods by randomly selecting individual values for parameters in each outcome from a distribution. We noted hesitance about enforcing a specific distribution over our range of radical uncertainty, but we stand behind this as a reasonable choice given our pragmatic aims. If the alternative is not to try to calculate relative expected values, we think that would be a loss, even if our own results have methodological doubts still attached to them.