Supporting Judgment in Predictive Analytics: Scenarios and Judgmental Forecasts

Dilek Önkal*, M. Sinan Gönül, Paul Goodwin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)

Abstract

Despite advances in predictive analytics there is much evidence that algorithm-based forecasts are often subject to judgmental adjustments or overrides. This chapter explores the role of scenarios in supporting the role of judgment when algorithmic (or model-based) forecasts are available. Scenarios provide powerful narratives in envisioning alternative futures and play an important role in both planning for uncertainties and challenging managerial thinking. Through offering structured storylines of plausible futures, scenarios may also enhance forecasting agility and offer collaborative pathways for information sharing. Even though the potential value of using scenarios to complement judgmental forecasts has been recognized, the empirical work remains scarce. A review of the relevant research suggests the merit of supplying scenarios to judgmental forecasters is mixed and can result in an underestimation of the extent of uncertainty associated with forecasts, but a greater acceptance of model-based point predictions. These findings are generally supported by the results of a behavioral experiment that we report. This study was used to examine the effects of scenario tone and extremity on individual and group-based judgmental predictions when a model-based forecast was available. The implications of our findings are discussed with respect to (i) eliciting judgmental forecasts using different predictive formats, (ii) sharing scenarios with varying levels of optimism and pessimism, and (iii) incorporating scenario approaches to address forecast uncertainty.

Original languageEnglish
Title of host publicationJudgement in Predictive Analytics
EditorsMatthias Seifert
Place of PublicationCham, Switzerland
PublisherSpringer
Chapter9
Pages245-264
Number of pages20
Volume343
Edition1st
ISBN (Electronic)9783031300851
ISBN (Print)9783031300844, 9783031300875
DOIs
Publication statusPublished - 3 Jun 2023

Publication series

NameInternational Series in Operations Research & Management Science
PublisherSpringer
Volume343
ISSN (Print)0884-8289
ISSN (Electronic)2214-7934

Cite this