Warning This library is under active development and things can change at anytime! Suggestions and help are greatly appreciated.

Simulation decomposition or SimDec is an uncertainty and sensitivity analysis method, which is based on Monte Carlo simulation. SimDec consists of three major parts:

1. computing sensitivity indices,
2. creating multi-variable scenarios and mapping the output values to them, and
3. visualizing the scenarios on the output distribution by color-coding its segments.

SimDec reveals the nature of causalities and interaction effects in the model. See our publications and join our discord community.

Python API

The library is distributed on PyPi and can be installed with:

pip install simdec

Dashboard

A live dashboard is available at:

https://simdec.io

Citations

The algorithms and visualizations used in this package came primarily out of research at LUT University, Lappeenranta, Finland, and Stanford University, California, U.S., supported with grants from Business Finland, Wihuri Foundation, and Finnish Foundation for Economic Education.

If you use SimDec in your research we would appreciate a citation to the following publications:

• 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. Available here.
• Kozlova, M., Moss, R. J., Yeomans, J. S., & Caers, J. (2024). Uncovering Heterogeneous Effects in Computational Models for Sustainable Decision-making. Environmental Modelling & Software, 171, 105898. https://doi.org/10.1016/j.envsoft.2023.105898
• Kozlova, M., Moss, R. J., Roy, P., Alam, A., & Yeomans, J. S. (forthcoming). SimDec algorithm and guidelines for its usage and interpretation. In M. Kozlova & J. S. Yeomans (Eds.), Sensitivity Analysis for Business, Technology, and Policymaking. Made Easy with Simulation Decomposition. Routledge.