Estimating uncertainties using judgmental forecasts with expert heterogeneity

Saurabh Bansal, Genaro J. Gutierrez

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)363-380
Number of pages18
JournalOperations Research
Volume68
Issue number2
DOIs
StatePublished - Mar 2020

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Estimating uncertainties using judgmental forecasts with expert heterogeneity'. Together they form a unique fingerprint.

Cite this