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'''SimDec''', or '''Simulation decomposition''', is a hybrid uncertainty and [[sensitivity analysis]] method, for visually examining the relationships between the output and input variables of a computational model.
SimDec maps multivariable scenarios onto the [[Frequency (statistics)|distribution]] of the model output.<ref
SimDec can be used in any range 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>
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=== Judging the importance of inputs ===
If an input variable has no effect on the output, its states (e.g., low & high) would lie on top of each other on the SimDec histogram, occupying fully overlapping ranges of the output. If an input variable has a strong effect and explains most of the [[ Variance-based sensitivity analysis|variance]] of the output, the border between its states on the SimDec histogram would be vertical. Such visualization has an important decision-making implication – e.g., if the high state of ''X'' can be achieved, it would guarantee a certain range of ''Y''. All cases in-between with low-to-strong effects would show a diagonal border between the states. The less they overlap, the larger the effect of ''X'' on ''Y''.<ref
While the horizontal displacement of sub-distributions on the SimDec histogram is the key to interpreting the results, the vertical disposition of sub-distributions is just a technical matter of the order of plotting the series of the stacked histogram.
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|'''Linear interaction''' is a characteristic of multiplicative models. On SimDec, the sub-distributions would be shifted more and more along the horizontal axis. The effect of one input on the output increases with the increasing value of another input. The sensitivity index computed for the second-order effect of such two input variables is non-zero.
|One input variable '''switches the direction of influence''' on the output in different states of another input variable. Such an effect might occur with a sign change in a model. The second-order effect is non-zero.
|Various types of '''nonlinear interactions''' can occur in models. For example, one input variable has no effect on the output in one state of another variable (lying on top of each other red-shaded sub-distributions) but has a strong effect otherwise (shifted blue sub-distributions). Such effect, too, will show up in the non-zero second-order sensitivity index.<ref
Understanding the nature of interaction effects in a computational model and its behavior in general is crucial for effective decision-making.
==References==
{{reflist|30em
<ref name="informs">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>
<ref name="kozlova_et_al_1">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>
}}
== See also ==
[[Sensitivity analysis]]
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