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title: "Explaining a model's assumptions"
canonical: "https://modelassist.epixanalytics.com/space/EA/26575463/Explaining%20a%20model's%20assumptions"
format: markdown
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The key to gaining acceptance to a model's results is very often the acceptance of the model's structure and assumptions. We recommend that you are very explicit about your assumptions, and make a summary of them in a prominent place in the report, rather than just have them scattered through the report in the explanation of each model component.

  


A risk analysis model will often have a fairly complex structure and the analyst needs to find ways of explaining the model that can quickly be checked. The first step is usually to draw up a schematic diagram of the structure of the model. The type of schematic diagram will obviously depend on the problem being modeled: GANTT charts, site plans with phases, work breakdown structure, flow diagrams, event trees, etc. – any pictorial representation that conveys the required information.

  


The next step is to show the key quantitative assumptions that are made for the model's variables.

  


### Distribution parameters

Using the parameters of a distribution to explain how a model variable has been characterized will often be the most informative when explaining a model's logic. We tend to use tables of formulae for more technical models where there are a lot of [parametric distributions](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575199/) and probability equations, because the logic is apparent from the relationship between a distribution's parameters and other variables. For [non-parametric distributions](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575199/), which are generally used to model expert opinion, or to represent a data set, a thumbnail sketch helps the reader most. Influence diagram plots are excellent for showing the flow of the logic and inter-relationships between model components, but not the mathematics underlying the links:

 

> Macro (gliffy)

  


  


Example of an influence diagram model (©Lumina Decision Systems)

  


### Graphical illustrations of quantitative assumptions

These are particularly useful when [non-parametric distributions](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575199/) have been used. For example, a sketch of a [General](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575434/) or [Cumulative](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575435/) distribution will be a lot more informative than noting its parameters values. Sketches are also very good when you want to explain partial model results. For example, [summary plots](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575483/) are useful for demonstrating the numbers that come out of what might be a quite complex [time series model](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575444/). [Scatter plots](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575484/) are useful for giving an overview of what might be a very complicated [correlation structure](https://epixanalytics.atlassian.net/wiki/spaces/EA/pages/26575455/) between two or more variables.

  


  


  


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