Simulation decomposition: Difference between revisions

Revision as of 13:23, 9 September 2023

Revision as of 08:55, 11 September 2023

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[[File:Simdec interactions.svg|thumb|upright=2.5 |Appearance of different interaction types on SimDec visualization]]

=== Exploring the interaction of inputs ===

When two or more input variables are used for decomposition, it becomes possible to examine their joint effects. A schematic visualization portrays how different types of joint effects of input variables on the output appear on SimDec visualization.

{{Ordered list |list_style_type=upper-alpha

:<p>A. '''No interaction '''. Sub-distributions of an additive model with both input variables that are equally important would be shifted uniformly. The second-order effect of such inputs would be equal to zero.

:<p>B|'''No interaction'''. Sub-distributions of an additive model with both input variables that are equally important would be shifted uniformly. The second-order effect of such inputs would be equal to zero.
|'''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.

:<p>C. |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.

:<p>D. |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>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>}}

Understanding the nature of interaction effects in a computational model and its behavior in general is crucial for effective decision-making.