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.
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Önkal, D., Gönül, M.S., Goodwin, P. (2023). Supporting Judgment in Predictive Analytics: Scenarios and Judgmental Forecasts. In: Seifert, M. (eds) Judgment in Predictive Analytics. International Series in Operations Research & Management Science, vol 343. Springer, Cham. https://doi.org/10.1007/978-3-031-30085-1_9
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