1. Statistical Screening
Contributors: Andy Ford, Tim Taylor and David Ford
Tags:
Status: Functional
Description: Statistical screening quantifies parameter influence on a specified performance variable throughout a simulation, thereby describing the evolution of exogenous impacts on behavior. Statistical screening uses multiple simulations generated by varying model input parameters to calculate correlation coefficients that measure the direction and strength of the relationship between input parameters and a user-defined system performance variable.
Related Publications: StatScreen_Description, StatScreen-Articles
StatScreen-Templates, StatScreen-Example-WORLD3, StatScreen-Example-TippingPoint
2. Vensim© to Mathematica© transformer
Contributor: Rogelio Oliva
Tags: Utility
Status: Functional
Description: This online utility creates, from a Vensim model (*.mdl format), a model description suitable for the Mathematica routines to perform the Loop Eigenvalue Elasticity Analysis (LEEA) as described in Kampmann and Oliva (2005). The output is an *.nb file formatted to work with Mathematica. The utility can be accessed from this link.
3. Tool set for Loop Eigenvalue Elasticity Analysis (with Mathematica®)
Contributor: Rogelio Oliva
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Status:
Description: The tool set developed to automate and standardize Loop Eigenvalue Elasticity Analysis (LEEA) as described in Loop Eigenvalue Elasticity
Analysis: Three case studies by Christian E. Kampmann and Rogelio Oliva. Please refer to the paper for a description of LEEA, its capabilities and limitations.
Related Publication: Kampmann and Oliva (2006)
4. Vensim® Module to Calculate Summary Statistics for Historical Fit
Contributor: Rogelio Oliva
Tags: Validation, Behaviour Analysis
Status:
Description: This Vensim® module calculates the summary statistics for historical fit (Theil inequality statistics) as described by Sterman (1984). It also generates a series of graphs and tables to facilitate the analysis and diagnosis of fit and residuals.
5. Automated Eigenvalue analysis of SD models (with MatLab®)
Contributors: Ahmed A. Abdelgawad, Bahaa E. Aly Abdel Aleem, Mohamed M. Saleh and Pål I. Davidsen
Status: Functional – No further development
Description: The method used allows for an investigation of how model behaviour is created from the underlying model structure and how this behaviour feeds back to change the relative significance of the model behaviour. The method also allows us to identify the dynamics of the relative significance of the various parameters that governs the gains of the links and loops of the model. The method has been implemented as Matlab user-made toolbox for the purpose of facilitating Eigenvalue analysis of models representing complex, dynamic systems. This work is based on control theory as well as the previous work on eigenvalue analysis in the system dynamics.
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6. Model Structure Analysis (MSA) (with Matlab®)
Contributor: Rogelio Oliva
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Status:
Description: Model Structure Analysis (MSA) package is a set of user-defined functions (M-files) developed for Matlab(R) to facilitate the analysis of structural complexity of system dynamics models. Full description of the algorithms used in these functions, and the rationale behind their usage is described in Oliva (2004)
Related Documents: Oliva (2004), MSA User Manual by Oliva, MSA files
6. Model Structure Analysis (MSA) (with Matlab®)
Contributor: Rogelio Oliva
Tags:
Status:
Description: Model Structure Analysis (MSA) package is a set of user-defined functions (M-files) developed for Matlab(R) to facilitate the analysis of structural complexity of system dynamics models. Full description of the algorithms used in these functions, and the rationale behind their usage is described in Oliva (2004)
Related Documents: Oliva (2004), MSA User Manual by Oliva, MSA files
7. Toolset to perform Behavioral Decomposition Weight (BDW) (with Mathematica®)
Contributor: Rogelio Oliva
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Description:
Related Documents:
8. Exploratory Modeling and Analysis (EMA) Workbench
Contributor: Jan Kwakkel
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Status:
Description: Exploratory Modeling and Analysis (EMA) is a research methodology that uses computational experiments to analyze complex and uncertain systems. That is, exploratory modeling aims at offering computational decision support for decision making under deep uncertainty and supports robust decision making. EMA can be understood as searching or sampling over an ensemble of models that are plausible, given a priori knowledge, or are otherwise of interest. The EMA workbench is aimed at providing support for doing EMA on models developed in various modelling packages and environments. Currently, Vensim, Excel, Python, and Netlogo (an agent-based simulation environment) are supported.