This is a short follow up to my post on the optimal timing of spending on AGI safety work which, given exact values for the future real interest, diminishing returns and other factors, calculated the optimal spending schedule for AI risk interventions.
This has also been added to the post’s appendix and assumes some familiarity with the post.
Here I consider the most robust spending policies and supposes uncertainty over nearly all parameters in the model[1] Inputs that are not considered include: historic spending on research and influence, rather than finding the optimal solutions based on point estimates and again find that the community’s current spending rate on AI risk interventions is too low.
My distributions over the the model parameters imply that
Of all fixed spending schedules (i.e. to spend X% of your capital per year[2]), the best strategy is to spend 4-6% per year.
Of all simplespending schedules that consider two regimes: now until 2030, 2030 onwards, the best strategy is to spend ~8% per year until 2030, and ~6% afterwards.
I recommend entering your own distributions for the parameters in the Python notebook here[3]. Further, these preliminary results use few samples: more reliable results would be obtained with more samples (and more computing time).
I allow for post-fire-alarm spending (i.e., we are certain AGI is soon and so can spend some fraction of our capital). Without this feature, the optimal schedules would likely recommend a greater spending rate.
Caption: Fixed spending rate. See here for the distributions of utility for each spending rate.
Caption: Simple - two regime - spending rate
Caption: The results from a simple optimiser[4], when allowing for four spending regimes: 2022-2027, 2027-2032, 2032-2037 and 2037 onwards. This result should not be taken too seriously: more samples should be used, the optimiser runs for a greater number of steps and more intervals used. As with other results, this is contingent on the distributions of parameters.
Some notes
The system of equations—describing how a funder’s spending on AI risk interventions change the probability of AGI going well—are unchanged from the main model in the post.
This version of the model randomly generates the real interest, based on user inputs. So, for example, one’s capital can go down.
Caption: An example real interest function r(t), cherry picked to show how our capital can go down significantly. See here for 100 unbiased samples of r(t).
Caption: Example probability-of-success functions. The filled circle indicates the current preparedness and probability of success.
Caption: Example competition functions. They all pass through (2022, 1) since the competition function is the relative cost of one unit of influence compared to the current cost.
This short extension started due to a conversation with David Field and comment from Vasco Grilo; I’m grateful to both for the suggestion.
Inputs that are not considered include: historic spending on research and influence, the rate at which the real interest rate changes, the post-fire alarm returns are considered to be the same as the pre-fire alarm returns.
This notebook is less user-friendly than the notebook used in the main optimal spending result (though not un user friendly) - let me know if improvements to the notebook would be useful for you.
This is a short follow up to my post on the optimal timing of spending on AGI safety work which, given exact values for the future real interest, diminishing returns and other factors, calculated the optimal spending schedule for AI risk interventions.
This has also been added to the post’s appendix and assumes some familiarity with the post.
Here I consider the most robust spending policies and supposes uncertainty over nearly all parameters in the model[1] Inputs that are not considered include: historic spending on research and influence, rather than finding the optimal solutions based on point estimates and again find that the community’s current spending rate on AI risk interventions is too low.
My distributions over the the model parameters imply that
Of all fixed spending schedules (i.e. to spend X% of your capital per year[2]), the best strategy is to spend 4-6% per year.
Of all simple spending schedules that consider two regimes: now until 2030, 2030 onwards, the best strategy is to spend ~8% per year until 2030, and ~6% afterwards.
I recommend entering your own distributions for the parameters in the Python notebook here[3]. Further, these preliminary results use few samples: more reliable results would be obtained with more samples (and more computing time).
I allow for post-fire-alarm spending (i.e., we are certain AGI is soon and so can spend some fraction of our capital). Without this feature, the optimal schedules would likely recommend a greater spending rate.
Caption: Fixed spending rate. See here for the distributions of utility for each spending rate.
Caption: Simple - two regime - spending rate
Caption: The results from a simple optimiser[4], when allowing for four spending regimes: 2022-2027, 2027-2032, 2032-2037 and 2037 onwards. This result should not be taken too seriously: more samples should be used, the optimiser runs for a greater number of steps and more intervals used. As with other results, this is contingent on the distributions of parameters.
Some notes
The system of equations—describing how a funder’s spending on AI risk interventions change the probability of AGI going well—are unchanged from the main model in the post.
This version of the model randomly generates the real interest, based on user inputs. So, for example, one’s capital can go down.
Caption: An example real interest function r(t), cherry picked to show how our capital can go down significantly. See here for 100 unbiased samples of r(t).
Caption: Example probability-of-success functions. The filled circle indicates the current preparedness and probability of success.
Caption: Example competition functions. They all pass through (2022, 1) since the competition function is the relative cost of one unit of influence compared to the current cost.
This short extension started due to a conversation with David Field and comment from Vasco Grilo; I’m grateful to both for the suggestion.
Inputs that are not considered include: historic spending on research and influence, the rate at which the real interest rate changes, the post-fire alarm returns are considered to be the same as the pre-fire alarm returns.
And supposing a 50:50 split between spending on research and influence
This notebook is less user-friendly than the notebook used in the main optimal spending result (though not un user friendly) - let me know if improvements to the notebook would be useful for you.
The intermediate steps of the optimiser are here.