All the code and data to produce the figures below can be found in this repository. In the second post (up in a couple days), I’ll discuss sensitivity analysis for the system as well as some visuals. In this first post, I’ll introduce the types of figures I’ll be talking about, and some visualization code. Most importantly, this process allows the decision maker to examine the implications of their assumptions about the world (or how it will unfold) on their candidate strategies. RDM aims to evaluate the performance of a policy/strategy/management plan over an ensemble of deeply uncertain parameter combinations – commonly referred to as “states of the world” (SOWs) – and then identify the policies that are most robust to those uncertainties. 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). Various posts have discussed sensitivity analysis and techniques in this blog before.
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