Generating sets of diverse and plausible scenarios through approximated multivariate normal distributions

Eljas Aalto, Tuomo Kuosa and Max Stucki

European Journal of Operational Research, 2025, vol. 320, issue 1, 160-174

Abstract: This article presents a novel and broadly generalizable framework for generating diverse and plausible sets of scenarios. Potential future outcomes are decomposed using a set of uncertainties which are assumed to be multivariate normally distributed, regardless of whether the uncertainties actually present numerically quantifiable phenomena. The optimal scenarios are then chosen along the principal components of the distribution, and the results can be easily interpreted and visualized. Notably, our approach requires a relatively small number of numerical assessments, offering an efficient and practical solution for decision-makers. The framework also provides a testable setting for evaluating its performance and allows users to iteratively improve future-related assumptions and predictions. These findings are relevant for all fields that aim to understand potential future developments, such as, but not limited to, foresight, economics, business strategy and strategic intelligence analysis.

Keywords: Scenarios; Strategic planning; Decomposition methods; Forecasting (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221724006039
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:320:y:2025:i:1:p:160-174

DOI: 10.1016/j.ejor.2024.08.003

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().