Modelling in Opasnet

This page is a knowledge crystal of subtype method. The page identifier is Op_en5318
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Question

How should modelling be done in Opasnet in practice? This page should be a general guidance on principles, not a technical manual for using different tools.

Answer

All variable pages should have a clear question and a clear answer. The answer should be in a form of a data table, which has all indices (explanatory columns) needed for an answer detailed enough.

The answer should be a clear and concise answer to the specific question, not a general description or discussion of the topic.

The answer should be convincing to a critical reader who reads the following data and believes it is correct:

• The Rationale section of the page.
• The Result sections of all upstream variables listed in the Dependencies section.
• In some cases, also downstream variables may be used in inference (e.g. in hierarchical Bayes models).
• It should be noted that the data mentioned above should itself be backed up by reliable sources, good rationale etc., which again should be backed up ad infinitum (but in practice slightly less).
• It should also be noted that ALL information that is needed to convince the reader must be put into the places mentioned and not somewhere else. In other words, when the reader has read the rationale and the the relevant results, (s)he should be able to trust that s(he) is now aware of all such major points related to the specific topic that have been described in Opasnet.
  • This is therefore guidance for info producers: if there is a relevant piece of information that you are aware of but it is not mentioned, you should add it.

The indices, i.e. explanatory columns, should match in variables that are causally connected by a causal diagram. This does not mean that they must be the same (as not all explanations are relevant for all variables) but it should be possible to see which parts of the results of two variables belong together. An example is a geographical grid for two connected variables such as a concentration field of a pollutant and an exposure variable for a population. If the concentration and population use the same grid, the exposure is easy to compute. However, they can be used together with different grids, but then there is a need to explain how one data can be converted into the other grid for calculating exposures.

Rationale

A draft based on own thinking. Not even the topics are clear yet.

Related files

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