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The Importance of Intelligent Colouring for Simulation Decomposition in Environmental Analysis

A. Alam1, M. Kozlova2, L. T. Leifsson3, and J. S. Yeomans4*

  1. Economics Department, Queen’s University, Kingston, ON, K7L3N6, Canada
  2. LUT Business School, LUT University, Lappeenranta, 53850, Finland
  3. School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, 47907, USA
  4. Schulich School of Business, York University, 4700 Keele Street, Toronto, ON, M3J 1P3 Canada

*Corresponding author. Tel.: 416 736-5074. E-mail address: (J. S. Yeomans).


“Real world” risk analysis in environmental contexts frequently requires the need to contrast numerous uncertain factors simultaneously and to communicate difficult-to-capture interactions. Monte Carlo simulation modelling of complex environmental sytems is frequently employed to integrate uncertain inputs and to construct probability distributions of the resulting outputs. Visual analytics and data visualization can then be employed for the processing, analyzing, and communicating of the influence of any multi-variable uncertainties on the system. The simulation decomposition (SimDec) analytical technique has recently been employed in the complex assessments of environmental systems. SimDec has proved to be beneficial in revealing interdependencies in complex models, lowering computational burdens, facilitating decision-maker perceptions, and especially, making analytical components visualizable. It has been demonstrated that many analytical findings would not have been revealed without the coloured visualizations provided by SimDec. However, an ad hoc colouring scheme of the distribution output is neither sufficient nor capable of producing much of the key visualizable information requisite for an effective SimDec analysis. Instead, an approach that has recently been referred to as an intelligent colouring has been proposed. This paper outlines, highlights, and demonstrates the importance of and best-practices in an intelligent colouring scheme needed for an effective SimDec analysis of complex environmental systems.

Keywords: Monte Carlo simulation, probability distribution, SimDec, global sensitivity analysis, interaction, intelligent colouring

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