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During this phase the variables described in the causal diagram are described more precisely, including also defining the causal relations in moe detail. It may also contain quality criteria and plans for collecting the necessary data or models needed to estimate the variable results.
Structure of the process
The work in this phase happens in iterative process between collection of information, synthesis of the information into the form of the information structure, and discussions about the descriptions of variables. The work of this phase is actually also a continuous interplay with the following phase, executing variables.
There are different kinds of variables
Variables are versatile objects. They are able to describe all of the following aspects of reality:
- Causal relationships linking variables in the different steps in the causal chain from source to impact (mainly in the definition/causality attribute);
- Different environmental, social, economic and infrastructural contexts in which risks might arise and play out (mainly in the scope attribute);
- Physical and chemical processes that generate, transform and transport the hazards (agents) from source to the target organs in the human body (mainly as variables that are defined as functions);
- Indicators to describe and communicate the causal chain and impacts (variables selected for reporting);
- Different policy measures that might be taken to address the risks, and thus different assessment scenarios that might be compared (decision variables);
- Appraisal of the impacts (and the policy scenarios to which they relate), in the light of agreed value systems and rules for evaluation (variables describing value judgements or derived from value judgement variables).
- Adaptation and feedback loops arising as a result of adaptation to the risks, at both individual and institutional level. A feedback loop is described as a variable that is indirectly dependent on the result of itself at a previous time point.
Ideally, all variables in the full-chain can be expressed quantitatively. In order to use the full chain approach quantitatively in an integrated assessment, it is necessary to acquire data for the variables, or to estimate these variables by modelling the underlying causal processes.
Proxies are not indicators
The term indicator is sometimes also (mistakenly, in the eyes of the new risk assessment method) used in the meaning of a proxy. Proxies are used as replacements for the actual objects of interest in a description if adequate information about the actual object of interest is not available. Proxies are indirect representations of the object of interest that usually have some identified correlation with the actual object of interest. At least within the context of the new risk assessment method, proxy and indicator have clearly different meanings and they should not be confused with each other. The figure below attempts to clarify the difference between proxies and indicators:
In the example, a proxy (PM10 site concentration) is used to indirectly represent and replace the actual object of interest (exposure to traffic PM2.5). Mortality due to traffic PM2.5 is identified as a variable of specific interest to be reported to the target audience, i.e. selected as an indicator. The other two nodes in the graph are considered as ordinary variables. The above graph has been made with Analytica, here is the File:Indicators and proxies.ANA.
Importance of indicators in the assessment process
Indicators have a special role in making the assessment. As mentioned above, indicators are the variables of most interest from the point of view of the use, users and other audiences of the assessment. The idea thus behind the indicator selection, specification and use is to highlight the most important and/or significant parts of the source-impact chain which are to be assessed and subsequently reported. The selected set of indicators guides the assessment process to address the relevant issues within the assessment scope according to the purpose of the assessment. It could be said that indicators are the leading variables in carrying out the assessment, other variables are subsidiary to specifying the indicators.
However, within the context of integrated risk assessment, selecting and specifying indicators may sound more straightforward than it actually is. Maybe, identification of indicators and specification of the causal network in line with the identified indicators, could grasp the essence of the process better. Instead of merely picking from a predefined set of indicators, selection here refers rather to identifying the most interesting phenomena within the scope of the assessment in order to describe and report them as indicators. Specification of indicators then is similar to specification of all other variables, although indicators are the ones that are primarily considered while other variables are considered secondarily, and mainly in relation to the indicators.
In principle, any variable could be chosen as an indicator and the set(s) of indicators could be composed of any types of indicators across the full-chain description. In practice, the generally relevant types of indicators, such as performance indicators can be somewhat predefined and even some detailed indicators can be defined in relation to commonly existing purposes and user needs. This kind of generality is also helpful in bringing coherence between the assessments.
On the generalizability of variables
Aim: Variables must be generalizable so that they can be used without additional knowledge of the context. In other words, the context must be described well enough inside the variable.
→ Because of this, the variables must be estimates about the truth, and not deliberate under- or overestimates. Biased estimates are common in risk assessment because usually the assessments want to avoid false negative results much more than false positive results. In other words, it is much worse if there is a risk and you don't find it than if there is no risk and you think there is.
- → Decisions may be based on risk aversion, but the estimates of variables must be best estimates, because you cannot know which decisions will be based on the variable.
Function in the pyrkilo method is a special case of a variable that has its parameters defined outside the variable itself. A simple example is variable Area of a rectangle, which is defined as Width*Height. This function can used within another variable, e.g. Area of Jouni's table, which is defined as Area of a rectangle(1.5 m, 0.8 m), and the result is 1.2 m2.
Note that any variable can be used as a function by replacing its original input parameters with other parameters.
Analytica to Mediawiki converter software converts Analytica models to Mediawiki code. Please note that Analytica files must be saved in xml-mode. Converter is available at:
This phase produces the descriptions of the attributes for all the included variables. The descriptions may be in the forms of e.g. text, tables, figures, hyperlinks, and computer code.