Endogenous Growth theory, Economic Growth and Research Policy all seem to be building mathematical models that attempt to generalize over our experience of how much government funding leads to increased growth, how quickly human capital feeds back into societal or individual wealth, or what interventions have helped poor countries to develop faster. None of them, AFAICT, have been concrete enough to lead to solid policy prescription that have reliably led to anyone or any country to recreate the experiences that led to the models.
In order to have a model solid enough to use as a basis for theorizing about the effects on growth of a new crop of self-improving AGIs, we’d need to have a much more mechanistic model behind endogenous growth. Fermi’s model told him how to calculate how many neutrons would be released given a particular density of uranium of a particular purity, how much would be absorbed by a particular quantity of shielding, and therefore where the crossover would be from a k of less than 1 to greater than 1. None of those models gives numerical models that we can apply to human intelligence, much less any abstractions that we could extend to cover the case of intelligences learning faster than we do.
Endogenous Growth theory, Economic Growth and Research Policy all seem to be building mathematical models that attempt to generalize over our experience of how much government funding leads to increased growth, how quickly human capital feeds back into societal or individual wealth, or what interventions have helped poor countries to develop faster. None of them, AFAICT, have been concrete enough to lead to solid policy prescription that have reliably led to anyone or any country to recreate the experiences that led to the models.
In order to have a model solid enough to use as a basis for theorizing about the effects on growth of a new crop of self-improving AGIs, we’d need to have a much more mechanistic model behind endogenous growth. Fermi’s model told him how to calculate how many neutrons would be released given a particular density of uranium of a particular purity, how much would be absorbed by a particular quantity of shielding, and therefore where the crossover would be from a k of less than 1 to greater than 1. None of those models gives numerical models that we can apply to human intelligence, much less any abstractions that we could extend to cover the case of intelligences learning faster than we do.