(Note that the Uncertain Future software is mostly supposed to be a conceptual demonstration; as mentioned in the accompanying conference paper, a better probabilistic forecasting guide would take historical observations and uncertainty about constant underlying factors into account more directly, with Bayesian model structure. The most important part of this would be stochastic differential equation model components that could account for both parameter and state uncertainty in nonlinear models of future economic development from past observations, especially of technology performance curves and learning curves. Robin Hanson’s analysis of the random properties of technological growth modes has something of a similar spirit.)
(Note that the Uncertain Future software is mostly supposed to be a conceptual demonstration; as mentioned in the accompanying conference paper, a better probabilistic forecasting guide would take historical observations and uncertainty about constant underlying factors into account more directly, with Bayesian model structure. The most important part of this would be stochastic differential equation model components that could account for both parameter and state uncertainty in nonlinear models of future economic development from past observations, especially of technology performance curves and learning curves. Robin Hanson’s analysis of the random properties of technological growth modes has something of a similar spirit.)