Simulation decomposition: Difference between revisions

Revision as of 08:55, 11 September 2023

Revision as of 12:44, 11 September 2023

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SimDec maps multivariable scenarios onto the [[Frequency (statistics)|distribution]] of the model output.<ref>Kozlova, M., & Yeomans, J. S. (2022). Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines. INFORMS Transactions on Education, 22(3), 147-159.</ref> This visual analytics approach exposes the underlying nature of the model behavior, including its nonlinear and multivariate [[Interaction (statistics) | interaction effects]].<ref>Kozlova, M., Moss, R. J., Yeomans, J. S., & Caers, J. (forthcoming). Uncovering Heterogeneous Effects in Computational Models for Sustainable Decision-making. Available at http://dx.doi.org/10.2139/ssrn.4550911 </ref>

SimDec is context-agnostic and can be used forin businessany applicationsrange of science, engineering, and social domains. Existing applications include business<ref>Kozlova, M., Collan, M., & Luukka, P. (2017). Simulation decomposition: New approach for better simulation analysis of multi-variable investment projects.</ref> and environmental issues,<ref>Deviatkin, I., Kozlova, M., & Yeomans, J. S. (2021). Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis. Socio-Economic Planning Sciences, 75, 100837.</ref>

<ref>Liu, Y. C., Leifsson, L., Pietrenko-Dabrowska, A., & Koziel, S. (2022). Analysis of Agricultural and Engineering Systems Using Simulation Decomposition. In International Conference on Computational Science (pp. 435-444). Springer, Cham.</ref> as well as in science, engineering, and social domains.

== Method==

SimDec operates on [[Monte Carlo Method | Monte Carlo]] simulation (or measured) data where both output and input values are recorded. At least one thousand observations (or simulated iterations) are generallytypically recommended to preserve the readability of the resulting histograms. An outline of the decomposition algorithm, which is readily available in multiple programming languages,<ref name="Software">Simulation Decomposition GitHub https://github.com/Simulation-Decomposition </ref> proceeds as follows:

# '''Select the input variables for decomposition'''. One can use sensitivity indices (see [[variance-based sensitivity analysis]]) to define the most influential variables for decomposition or choose them manually according to the decision-problem context (for example, only those input variables that the decision-maker hascan theact power to changeupon). Two to three input variables, ordered by decreasing value of their sensitivity indices, usually provide the most meaningful decomposition results.

# '''Divide the inputs into states'''. The numeric ranges of the inputs are split into several intervals with an equal number of observations in each. For categorical variables, the categories represent states.

# '''Form scenarios'''. All combinations of states of the selected input variables produce unique scenarios or subsets of the data. For example, if the range of ''X2'' is divided into ''low'', ''medium'' and ''high'', and ''X3'' takes values of 1 or 2, six scenarios are formed:

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! Effect strength!! Visual!! Decision-making implication

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| No effect || Sub-distributions are lying on top of each other, occupying fully overlapping ranges of the output.|| No matter how we push ''X'', it would have no significant effect on ''Y''.

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| Moderate effect || The border between sub-distributions is diagonal, there is a partial overall of ''Y'' range.|| The high state of ''X'' improves our chances of getting into high ''Y'', but does not guarantee the result. The same result (overlapping area) can be achieved by having a lower ''X''.