The issue is that our parameters can lead to different rates of cubic population growth. A 1% difference in the rate of cubic growth can lead to huge differences over 50,000 years. Ultimately, this means that if the right parameter values dictating population are sampled in a situation in which the effect of the intervention is backfires, the intervention might have an average negative value across all the samples. With high enough variance, the average sign will be determined by the sign of the most extreme value. If xrisk mitigation work backfires in 1⁄4 of cases, we might expect 1⁄4 of collections of samples to have a negative mean.
The issue is that our parameters can lead to different rates of cubic population growth. A 1% difference in the rate of cubic growth can lead to huge differences over 50,000 years. Ultimately, this means that if the right parameter values dictating population are sampled in a situation in which the effect of the intervention is backfires, the intervention might have an average negative value across all the samples. With high enough variance, the average sign will be determined by the sign of the most extreme value. If xrisk mitigation work backfires in 1⁄4 of cases, we might expect 1⁄4 of collections of samples to have a negative mean.
Thanks for clarifying, Derek!