Alexander I. Shlyakhter

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For energy forecasts to be useful in modelling or in policy efforts, the associated uncertainties must be known reliably. We analyse the actual errors in past forecasts of over 170 energy producing and consuming sectors of the US economy. We find that the often assumed normal distribution fails to model frequency of extreme outcomes (those lying far from(More)
I use an analogy with the history of physical measurements, population and energy projections, and analyze the trends in several data sets to quantify the overconfidence of the experts in the reliability of their uncertainty estimates. Data sets include (i) time trends in the sequential measurements of the same physical quantity; (ii) national population(More)
Risk assessors attempting to use probabilistic approaches to describe uncertainty often find themselves in a data-sparse situation: available data are only partially relevant to the parameter of interest, so one needs to adjust empirical distributions, use explicit judgmental distributions , or collect new data. In determining whether or not to collect(More)
Based on imperfect data and theory, agencies such as the United States Environmental Protection Agency (USEPA) currently derive "reference doses" (RfDs) to guide risk managers charged with ensuring that human exposures to chemicals are below population thresholds. The RfD for a chemical is typically reported as a single number, even though it is widely(More)
This paper discusses several factors that should be considered m integrnted risk nnnlyses of global climate change. We begin by describing how the problem of global climate change cnn be subdivided into largely independent parts that can be linked together in nn onnlytioolly tractable fashion. Uncertainty ploys n central role in integrated risk onnlyses of(More)
We present a simple method for estimating uncertainty in modeling and forecasts based upon an analysis of errors in old measurements and projections. Probabilities of large deviations are parametrized by an exponential function with one free parameter. We illustrate this formulation by quantifying uncertainties in national population projections and by(More)
Results of a systematic analysis of actual vs. estimated uncertainty in scientific models are presented. Data sets include: i) time trends in the sequential measurements of the same physical quantity; ii) national population projections; iii) projections for the United States' energy sector. Probabilities of large deviations from the true values are(More)