Help:Extended causal diagram

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Extended causal diagrams are graphical representations of a particular situation, where the objects described are causally related to each other. In addition, the diagrams contain non-causal elements such as value judgements or inferences based on data. Extended causal diagrams utilise the ideas of directed acyclic graphs, but they have additional features.

Overview

Extended causal diagram notation.PNG

The pyrkilo method has been developed to facilitate the Science-Policy Interface.

There is a need for methods facilitating the flow of information and understanding between science and policy. The principle is to describe a risk situation in a formal manner. Pyrkilo uses enhanced causal diagrams that contain items along a causal pathway (or network) from e.g. abatement strategies to emissions to dispersion to exposure to effects. They have been designed to describe also non-causal connections such as non-causal reasoning, values, preferences, and arguments.

These diagrams use the Analytica(TM) software platform, a graphical Monte Carlo simulation program. It is based on nodes. They are used to describe and define all the pieces needed for a description of the situation under scrutiny. Of course, similar diagrams may be produced with any graphics software, providing that calculation functions of Analytica are not required.

Many nodes are used as described in Analytica manuals. However, there are also special colours and shapes representing features that are important for pyrkilo diagrams. See Description of each node for more details. You can see the definitions and descriptions by clicking or double-clicking the nodes.

Colour description: xLyT describes the coordinates in the colour palette of Analytica, xth cell from left and yth cell from top. Directions are L left, R right, T top, B bottom, e.g. 1R1B is the right bottom cell. Automatic means that the colour is the default for that node shape.

Node types

Node type Object Colour code in Analytica Comments
General variable.png General variable 8R3B (automatic) This is a deterministic function of the quantities it depends on.
Chance variable.png Chance variable 11L4B (autom) This is a variable which is uncertain and uncontrollable (in a direct sense).
Data-driven variable.png Data-driven variable 3R1B A general variable where the result is mostly driven by data (observations or literature).
Author judgement variable.png Author judgement variable 4R2B A general variable where the result is mainly driven by author judgement (estimates with poor or no data).
Decision variable.png Decision variable 9L3B This is the variable that a decision-maker has the power to control. The decision variable should always be at the top of the chain of causality, even if this is a subchain i.e. it should not have any parent variables. Essentially the decision variable should be regarded as a decision that has to be made; since many factors affect all decisions it is not (in the case of INTARESE) an efficient use of resources to attempt to model what leads a decision-maker to make his decision.
Objective variable.png Indicator 1R3B (autom) This is a variable of special interest. One of the indicators in an assessment may be the quantitative criterion that you are trying to optimize.

A particularly important variable in relation to the interests of the intended users of the assessment output (i.e. it must be a means of effective communication of assessment results).

  • It must be in causal connection to the endpoints of the assessment and thus address causality throughout the full chain.
  • It should reflect the use/purpose of the assessment.
  • It should address and be adapted according to the target audience.
  • It should be the ‘leading component’ in the assessment process.
Value judgement variable.png Value judgement variable 8L4B A preference or value that a person or a group assigns to a particular condition or state of the world.
Index variable.png Index (or dimension) 5R2B (autom) This identifies the dimensions of the variable to which it is linked. Note that these dimensions do not have to be numeric, but can also be classes etc.
Risk assessment node.png Risk assessment 8R3B (autom)
Scope node.png Scope 6R1B The scope of the object
Conclusion node.png Conclusion 6L3B A conclusion of the risk assessment (Result/Conclusion attribute).
Module node.png Module 6R3B (autom) A group of variables that are put together for illustrative or other practical reasons.
Proxy variable.png Data 2L3B (autom) Contents of the Definition/Data attribute of a variable. If the Result attribute of a variable is used as Data for another variable, the first variable is called a proxy, and this node is used in the diagram. If an arrow or line is drawn between these objects, it must be noticed that this is NOT a causal link but an inference link. The direction of the arrow would be from the proxy to the variable.
Argument node.png Argument 8R2B A piece of argumentation related to an object (variable, risk assessment, or class)
Formula node.png Formula 9L3B Contents of the Definition/Formula attribute of a variable.
Class node.png Class 1L2B A class object (a set of objects that share a particular property).
Function node.png Function 4R2B (autom) A special kind of class. The particular property that is shared contains a full description of the Scope and the Definition attributes with given parameters.
Causal arrow.png Causal arrow This states a causal relationship (or influence) of one variable onto another. Note that causal arrows can only exist between two arrows; any arrows to or from non-causal objects are non-causal inference arrows.
Non-causal arrow.png Non-causal arrow This states an inference relationship between two objects. This means that the object where the arrow starts from is in the Data attribute of the other object. It is thus used to infer something about the value of the result of the latter object. Either object can be a variable or a non-variable. Note that Analytica is only able to show one kind of arrows, so in some cases the nature of the arrow (causal or inference) must be concluded from the context.