Application of a variance‐based sensitivity analysis method to the Biomass Scenario Learning Model

Paige Jadun, Laura J. Vimmerstedt, Brian W. Bush, Daniel Inman and Steve Peterson

System Dynamics Review, 2017, vol. 33, issue 3-4, 311-335

Abstract: Variance‐based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance‐based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. We show that application of variance‐based sensitivity analysis to the model allows us to test for non‐additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1002/sdr.1594

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:sysdyn:v:33:y:2017:i:3-4:p:311-335

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0883-7066

Access Statistics for this article

More articles in System Dynamics Review from System Dynamics Society
Bibliographic data for series maintained by Wiley Content Delivery ().