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• W2803447551 abstract "People make predictions about the future all of the time. Often these predictions take the form of single-value best estimates, with no associated statement of uncertainty. Sometimes that is just fine. For example, if you need to know to within a few meters where Venus or the Earth will be located with respect to the sun at noon Greenwich Mean Time on January 1, 2100, experts in orbital mechanics can tell you, and single-value answers will be more than sufficient. Of course, there is a miniscule probability that some large object from outside the solar system will disrupt those orbits. However, on the timescale of the next century, the probability that something like that will happen is so low that it can be ignored in virtually any human decision making.In other cases, people routinely make forecasts in the form of single-value or point estimates when in fact uncertainty is very high. Slowly but surely different communities have come to understand that doing this can result in serious problems and have begun to characterize uncertainty with probability distributions (1). Thanks to the efforts of Richard Moss and Steven Schneider (2) the climate science community has been in the forefront of working to describe the uncertainty in their estimates. However, other fields such as health risk assessment (3⇓–5) and energy forecasting (6⇓⇓⇓–10) have been slower to embrace the characterization of uncertainty in their forecasts.For this reason the paper published in PNAS by Christensen et al. (11) that provides probabilistic estimates of global per capita gross domestic product (GDP) in 2100 is an important step forward. These authors correctly note that “projections of long-run productivity growth and economic growth are primary inputs into analyses used to support long-term planning and decision-making on many … [↵][1]1Email: granger.morgan{at}andrew.cmu.edu. [1]: #xref-corresp-1-1" @default.
• W2803447551 created "2018-06-01" @default.
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• W2803447551 date "2018-05-14" @default.
• W2803447551 modified "2023-09-27" @default.
• W2803447551 title "Uncertainty in long-run forecasts of quantities such as per capita gross domestic product" @default.
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• W2803447551 doi "https://doi.org/10.1073/pnas.1805767115" @default.
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