Producing result from rationale
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Producing result from rationale is a method telling how the information described in rationale (or definition) and its sub-attributes data, dependencies, and formula can be manipulated in aim to produce an answer (or result) to the research question that is described in the scope of a particular page. The result should naturally answer the question asked and be coherent with all valid information under rationale.
How should the information described in rationale (or definition) and its sub-attributes data, dependencies, and formula be manipulated in aim to produce an answer (or result) to the research question that is described in the scope of a particular page?
Sources of information. Information is numerical or textual description about some real-world phenomena. This should be a comprehensive list of all possible sources of information.
- direct observations about the issue of interest.
- information about issues closely related to although not exactly about the issue of interest. For example, observations from a previous year or from a related population.
- information about issues that are causally upstream, i.e. have causal impacts on the issue of interest.
- information about issues that are causally downstream, i.e. are causally influenced by the issue of interest.
- a probability function that describes what can be known about the issue of interest given data.
- a functional relationship between two or more issues. It is used to calculate the value of one issue given all others.
- information about how things were at a previous time point.
- a method to make inference about neighbouring issues based on knowledge that things don't change with arbitrary speed. (I have too poor understanding on this to actually describe this.)
- Gut feeling
- a sensation about having information although no clear source (those above) can be given. This is the weakest source of information.
Possible approaches to produce a result from rationale. The idea is that one or more sources of information (above) is used, and then one or more of the approaches (below) are used to manipulate the information to come up with a result. Information that is required by an approach is listed in italics. Software tools specifically designed for this purpose may be listed in bold. A generic software that can handle most cases is R.
- Application of direct observations about the issue. observations, likelihood
- Interpolation and extrapolation from observations about a related but another issue. neighbouring observations, likelihood, (krieging?)
- Deterministic inference with equation. parents, function
- Differential equation. parents, function, time-1
- Differential equation with feedback. parents, function, time-1, children Simupedia
- Marginal distributions and rank correlations in between (vines). observations, likelihood, parents, rank correlations Uninet
- Gibbs sampling (hierarchical Bayes models). observations, likelihood, parents, children OpenBUGS
- Descriptive models. observations, likelihoods / functions
- Neural networks. observations, likelihoods / functions
- Predicate logic. formal sentences
- Expert elicitation. any information Excalibur
- Heuristics. any information
The result should naturally answer the question asked and be coherent with all valid information under rationale.
- Predicate logic
- Opasnet modelling environment
- Opasnet structure
- Opasnet Base
- Opasnet Base Connection for R
- Operating intelligently with multidimensional arrays in R
Modelling, Opasnet, information