TY - JOUR
T1 - Estimating uncertainties using judgmental forecasts with expert heterogeneity
AU - Bansal, Saurabh
AU - Gutierrez, Genaro J.
N1 - Funding Information:
Funding: The technical development described in the paper was supported by the U.S. Federal Aviation Administration Office of Environment and Energy through ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment, project 12447380 through FAA Award Number 13-C-AJFE-PSU-031 under the supervision of Nate Brown and Kristin Lewis. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA.
Publisher Copyright:
© 2020 INFORMS.
PY - 2020/3
Y1 - 2020/3
N2 - In this paper, we develop a new characterization of multiple-point forecasts provided by experts and use it in an optimization framework to deduce actionable signals, including the mean, standard deviation, or a combination of the two for underlying probability distributions. This framework consists of three steps: (1) calibrate experts' point forecasts using historical data to determine which quantile they provide, on average, when asked for forecasts, (2) quantify the precision in the experts' forecasts around their average quantile, and (3) use this calibration information in an optimization framework to deduce the signals of interest. We also show that precision and accuracy in expert judgments are complementary in terms of their informativeness. We also discuss implementation of the development and the realized benefits at a large government project in the agribusiness domain.
AB - In this paper, we develop a new characterization of multiple-point forecasts provided by experts and use it in an optimization framework to deduce actionable signals, including the mean, standard deviation, or a combination of the two for underlying probability distributions. This framework consists of three steps: (1) calibrate experts' point forecasts using historical data to determine which quantile they provide, on average, when asked for forecasts, (2) quantify the precision in the experts' forecasts around their average quantile, and (3) use this calibration information in an optimization framework to deduce the signals of interest. We also show that precision and accuracy in expert judgments are complementary in terms of their informativeness. We also discuss implementation of the development and the realized benefits at a large government project in the agribusiness domain.
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U2 - 10.1287/opre.2019.1938
DO - 10.1287/opre.2019.1938
M3 - Article
AN - SCOPUS:85084719824
SN - 0030-364X
VL - 68
SP - 363
EP - 380
JO - Operations Research
JF - Operations Research
IS - 2
ER -