Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines

Mariia Kozlova () and Julian Scott Yeomans ()
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Mariia Kozlova: School of Business and Management, LUT University, 53850 Lappeenranta, Finland
Julian Scott Yeomans: Operations Management and Information Systems Area, Schulich School of Business, York University, Toronto, Ontario M3J 1P3, Canada

INFORMS Transactions on Education, 2022, vol. 22, issue 3, 147-159

Abstract: Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.

Keywords: Monte Carlo simulation; simulation decomposition; SimDec; uncertainty analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orited:v:22:y:2022:i:3:p:147-159

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