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

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.

Technical Advances in Aviation Electrification: Enhancing Strategic R&D Investment Analysis through Simulation Decomposition

“…In order to progress beyond a selected scenario approach, the Monte Carlo study undertaken will be extended using the recently introduced, innovative approach called simulation decomposition (SimDec) [24]. The SimDec method has been created to expand the analytical capacity of simulation by significantly broadening its cause-and-effect ex-planatory powers [24][25][26][27]. SimDec provides a very powerful, straightforward approach for visually analyzing the impacts of combinations of variables on output measures [24,26].…”

“…The SimDec method has been created to expand the analytical capacity of simulation by significantly broadening its cause-and-effect ex-planatory powers [24][25][26][27]. SimDec provides a very powerful, straightforward approach for visually analyzing the impacts of combinations of variables on output measures [24,26]. It is a generally usable method that is not context-dependent [25].…”

“…It is a generally usable method that is not context-dependent [25]. In SimDec, selected uncertain input variable combinations are used to "decompose" output distributions into a number of state-influenced sub-distributions [24]. These sub-distributions are superimposed onto an output distribution, thereby permitting an explicit visualization of the cause-and-effect impacts of the decomposed multi-variable groups of input combinations and/or their various interactions [24].…”

“…In SimDec, selected uncertain input variable combinations are used to "decompose" output distributions into a number of state-influenced sub-distributions [24]. These sub-distributions are superimposed onto an output distribution, thereby permitting an explicit visualization of the cause-and-effect impacts of the decomposed multi-variable groups of input combinations and/or their various interactions [24]. The practical contributions from the decomposed visualization facilitates subsequent decision-maker insights with respect to the underlying simulation model.…”

“…The practical contributions from the decomposed visualization facilitates subsequent decision-maker insights with respect to the underlying simulation model. SimDec supplies both sensitivity and scenario contributions that are frequently employed by decision-makers in conjunction with "real world" quantitative analyses [24][25][26][27]. At the strategic level, SimDec enables a visual analytic display in continuous numerical space of the simultaneous interaction between multiple different factors that affect the flying range of electrical aircraft, thereby more fully portraying the financial and environmental benefits of aviation electrification to the decision-makers.…”