Milton Friedman’s thermostat and sensitivity analysis of control policies

If you’re reading this blog, you probably already know that correlation does not necessarily imply causation. However, does a lack of correlation necessarily imply a lack of causation? Despite widespread misconception, even by Nobel Laureates, it does not. This is especially true for controlled systems, as explained in Milton Friedman’s thermostat example. The act of […]

Determining the appropriate number of samples for a sensitivity analysis

Sensitivity analysis aims at assessing the relative contributions of the different sources of uncertainty to the variability in the output of a model. There are several mathematical techniques available in the literature, with variance-based approaches being the most popular, and variance-based indices the most widely used, especially “total effects” indices. Literature also provides several estimators/approximators […]

Sensitivity Analysis Tools

Sensitivity analysis (SA) is one of the main themes of the Water Programming Blog. There are several decent blog posts that go over theoretical aspects of sensitivity analysis (for example, here , here, and here). Also, many blog posts explain how to efficiently and elegantly visualize sensitivity analysis results (for example, here, and here). In […]

A Python Implementation of grouped Radial Convergence Plots to visualize Sobol Sensitivity Analysis results

TDLR; A Python implementation of grouped radial convergence plots based on code from the Rhodium library. This script is will be added to Antonia’s repository for Radial Convergence Plots. Radial convergence plots are a useful tool for visualizing results of Sobol Sensitivities analyses. These plots array the model parameters in a circle and plot the […]

Radial convergence diagram (aka chord diagram) for sensitivity analysis results and other inter-relationships between data

TLDR; Python script for radial convergence plots that can be found here. You might have encountered this type of graph before, they’re usually used to present relationships between different entities/parameters/factors and they typically look like this: In the context of our work, I have seen them used to present sensitivity analysis results, where we are […]

Magnitude-varying sensitivity analysis and visualization (Part 2)

In my last post, I talked about producing these flow-duration-curve-type figures for an output time-series one might be interested in, and talked about their potential use in an exploratory approach for the purpose of robust decision making. Again, the codes to perform the analysis and visualization are in this Github repository. As already discussed, there are […]

Magnitude-varying sensitivity analysis and visualization (Part 1)

Various posts have discussed sensitivity analysis and techniques in this blog before. The purpose of this post is to show an application of the methods and demonstrate how they can be used in an exploratory manner, for the purposes of robust decision making (RDM). RDM aims to evaluate the performance of a policy/strategy/management plan over an […]