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Discrete Approximations to Continuous Distributions in Decision Analysis

Robert Hammond, Chevron
December 16, 2015 (SDP Webinar co-sponsored by the Decision Analysis Society - Invited Talk)

Decision analyses often call for discretizing continuous uncertainties to represent them in decision trees. A few common methods have been in use for decades in practice, but there are many to choose from. This talk presents an overview of several discretization methods, their strengths and weaknesses, and the types of continuous distributions they are best suited for. All discretization methods have some approximation error, but the quality of a subjectively assessed distribution itself is often a very real concern as well. Factors such as cognitive biases can lead an expert to give their P15 value when asked for their P10, for example. The additional errors in assessments have implications for choosing a discretization method, but do not eliminate the need for accurate methods.

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Keywords: evaluation anamod, framing framestruc, discretization discz, uncertainty analysis uncanal, swanson megill, modeling modtree, decision tree dectree, Monte Carlo simulation mcsim, percentiles, Pearson-Tukey, distributions, probability assessment probass, cognitive biases cogbias

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