Difference between revisions of "Variable"

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<noinclude>
 
[[Category:Universal object]]
 
[[Category:Universal object]]
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[[Category:Open policy practice]]
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[[Category:Decision analysis and risk management]]
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{{variable|moderator=Jouni}}
 
{{Guidebook}}
 
{{Guidebook}}
 
[[category:Glossary term]]
 
[[category:Glossary term]]
 
<section begin=glossary />
 
<section begin=glossary />
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:'''[[Variable]]''' is a description of a particular piece of reality. It can be a description of physical phenomena, or a description of value judgements. Also decisions included in an assessment are described as variables. Variables are continuously existing descriptions of reality, which develop in time as knowledge about them increases. Variables are therefore not tied into any single assessment, but instead can be included in other assessments. A variable is the basic building block of describing reality.<section end=glossary />
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:'''Variable''' is a description of a particular piece of reality. It can be a description of a physical phenomenon, or a description of value judgements. Also decisions included in an assessment are described as variables. Variables are continuously existing descriptions of reality, which develop in time as knowledge about the topic increases. Variables are therefore not tied into any single assessment, but instead can be included in other assessments. A variable is the basic building block of describing reality.<section end=glossary />
  
 +
== Question ==
  
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;The research question about the structure of a variable: What is a structure of a variable such that it
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What should be the structure of a variable such that it
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:* is able to systematically handle all kinds of information about the particular piece of reality that the variable is describing,
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* is able to systematically handle all kinds of information about the particular piece of reality that the variable is describing, especially
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:* is able to systematically describe causal relationships between variables,
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** it is generic enough to be a standard building block in decision support work (including interpretation of scientific information and political discussions),
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:* enables both quantitative and qualitative descriptions,
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* is able to systematically describe causal relationships between phenomena and variables that describe them,
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:* is suitable for any kinds of variables, especially physical phenomena, decisions, and value judgements,
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* enables both quantitative and qualitative descriptions,
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:* inherits its main structure from [[universal object]]s,
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* is suitable for any kinds of variables, especially physical phenomena, decisions, and value judgements,
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:* complies with the [[PSSP]] ontology,
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* inherits its main structure from [[universal object]]s,
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:* can be operationalised in a computational model system,
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* complies with the [[PSSP]] ontology,
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:* results in variables that are independent of the assessment(s) it belongs to;
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* can be operationalised in a computational model system,
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:* results in variables that pass the [[Plausibility test|clairvoyant test]].
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* results in variables that are independent of the assessment(s) they belong to;
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* results in variables that pass the [[Plausibility test|clairvoyant test]].
 +
* can be implemented on a website, and
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* is easy enough to be usable and understood by interested non-experts?
  
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== Answer ==
 +
 +
Variable is implemented as a web page in Opasnet wiki web-workspace. A variable page has the following structure.
  
 
{|{{prettytable}}
 
{|{{prettytable}}
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|+Table 3. The attributes of a variable.
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|+The attributes of a variable.
 
! [[Attribute]]
 
! [[Attribute]]
 
! Sub-attribute
 
! Sub-attribute
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! Comments specfic to the variable attributes
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! Comments specific to the variable attributes
 
|-----
 
|-----
 
| '''Name'''
 
| '''Name'''
 
|  
 
|  
 +
| An identifier for the variable. Each Opasnet page have two kinds of identifiers: the name of the page (e.g. Variable) and the page identifier (e.g. Op_en2022). The former is used e.g. in links, the latter in [[R]] code.
 +
|-----
 +
| '''Question'''
 
|  
 
|  
 +
| Gives the question that is to be answered. It defines the scope of the variable. The question should be defined in a way that it has relevance in many different situations, i.e. makes the variable re-usable. (Compare to an [[assessment]] question, which is more specific to time, place and user need.)
 
|-----
 
|-----
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| '''Scope'''
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| '''Answer'''
 
|  
 
|  
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| This includes a verbal definition of the spatial, temporal, and other limits (system boundaries) of the variable. The scope is defined according to the use purpose of the assessment(s) that the variable belongs to.
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| An answer presents an understandable and useful answer to the question. Its essence is often a machine-readable and human-readable probability distribution (which can in a special case be a single number), but an answer can also be non-numerical such as "very valuable" or a descriptive table like on this page. The units of interconnected variables need to be coherent with each other given the functions describing causal relations. The units of variables can be used to check the coherence of the causal network description. This is a so called [[Plausibility test|unit test]]. Typically the answer contains an [[R]] code that fetches the ovariable created under Rationale/Calculations and evaluates it.
 
|-----
 
|-----
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| rowspan="4" | '''Definition'''
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| rowspan="5" | '''Rationale'''
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| Causality
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|  
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| Causality tells what we know about how upstream variables (i.e. causal parents) affect the variable. Causality lists the parents and expresses their functional relationships (the variable as a function of its parents) or probabilistic relationships (conditional probability of the variable given its parents). The expression of causality is '''independent''' of the data about the magnitude of the result of the variable.
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| Rationale contains anything that is necessary to convince a critical reader that the answer is credible and usable. It presents the reader the information required to derive the answer and explains how it is formed. Typically it has the following sub-attributes, but also other are possible. Rationale may also contain lengthy discussions about relevant topics.
 
|----
 
|----
 
| Data
 
| Data
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| Data tells what we know about the magnitude of the result of the variable. Data describes any non-causal information about the particular part of reality that is being described, such as direct measurements, measured data about an analogous situation (this requires some kind of error model), or expert judgment.
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| Data tells about direct observations (or expert judgements) about the variable itself.
 +
|----
 +
| Dependencies
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| Dependencies {{reslink|Dependencies instead of causality}} tells what we know about how upstream variables (i.e. causal parents) affect the variable. In other words, we attempt to estimate the answer indirectly based on information of causal parents. Sometimes also reverse inference is possible based on causal children. Dependencies list the causal parents and expresses their functional relationships (the variable as a function of its parents) or probabilistic relationships (conditional probability of the variable given its parents).
 
|----
 
|----
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| Unit
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| Calculations
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| Unit describes, in what measurement units the result is presented. The units of interconnected variables need to be coherent with each other given the functions describing causal relations. The units of variables can be used to check the coherence of the causal network description. This is a so called [[Plausibility test|unit test]].  
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| Calculations {{reslink|Discussion on formula attribute}} is an operationalisation of how to calculate or derive the answer. Formula uses algebra, computer code, or other explicit methods if possible. Typically it is [[R]] code that produces and stores the necessary [[ovariable]]s to compute the current best answer to the question.
 
|----
 
|----
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| Formula
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| Data not used
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| Formula {{disclink|Discussion on formula attribute}} is an operationalisation of how to calculate or derive the result based on ''Causality'', ''Data'', and ''Unit'', making a synthesis of the three. Formula uses algebra, computer code, or other explicit methods if possible.
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| Data not used are relevant for the research question, but for some reason they were not used in producing the current answer. I may be that the data was found after the synthesis, and an update has not yet been done; or it has been unclear how to merge these to the existing data. In any case, it is important to be differentiate and be explicit about whether data is irrelevant (and therefore removed from the page) or relevant but not used (and therefore waiting for further work).
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|-----
 
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| '''Result'''
 
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|
 
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| A result is an estimate about the particular part of reality that is being described. It is preferably a probability distribution (which can in a special case be a single number), but a result can also be non-numerical such as "very valuable".  
 
 
|}
 
|}
  
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In addition, it is practical to have additional subtitles on a variable page. These are not attributes, though.
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* See also
 +
* Keywords (not always used)
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* References
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* Related files
  
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[[image:Variable definition.PNG]]
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== Rationale ==
  
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'''Specific issues related to variable attributes
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[[File:Information_flow_within_open_policy_practice.png|thumb|450px]]
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The structure is based on extensive discussions between Mikko Pohjola and Jouni Tuomisto in 2006-2008 and intensive application in Opasnet ever since.
  
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In a general form, the formula can be described as
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For more detailed description about variables as information objects, see [[knowledge crystal]].
  
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result = formula(causal parameters, data parameters, unit),
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== See also ==
  
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:where formula is a function (expressed as algebra or computer code for a specified software) for calculating the result using the causal parameters (information from causally upstream variables) and the data parameters (information from observed data) as input.
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* [[Ovariable]]
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* [[:Category:Variables | List of all variables]] in Opasnet
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* [[Universal object]]
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* [[Open assessment]]
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* [http://en.opasnet.org/w/index.php?title=Variable&oldid=5596 A previous version of this page] contains much of the discussion from the Intarese deliverables D17 and D18, which has been edited with a hard hand.
  
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>>#It should be noted that the result is the distribution itself, although it can be expressed as some kind of description of the distribution, such as mean and standard deviation. The result should be described in such a detailed way that the full distribution can be reproduced from the information presented under this attribute. A technically straightforward way to do this is to provide a large random sample from the distribution.
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== References ==
  
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The result may be a different number for different ''locations'', such as geographical positions, population subgroups, or other determinants. Then, the result is described as
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<references/>
  
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  R given x<sub>1</sub>,x<sub>2</sub>,...
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== Related files ==
−
 
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</noinclude>
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where R is the result and x<sub>1</sub> and x<sub>2</sub> are defining the locations. A ''dimension'' means a property along which there are multiple locations and the result of the variable may have different values when the location changes. In this case, x<sub>1</sub> and x<sub>2</sub> are dimensions, and particular values of x<sub>1</sub> and x<sub>2</sub> are locations. A variable can have zero, one, or more dimensions. Even if a dimension is continuous, it is usually operationalised in practice as a list of discrete locations. Such a list is called an ''[[index]]'', and each location is called a ''row'' of the index.
 
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Uncertainty about the true value of the variable is one dimension. The index of the uncertainty dimension is called the ''[[Sample]]'' index, and it contains a list of integers 1,2,3... . Uncertainty is operationalised as a sequence of random samples from the probability distribution of the result. The i<sup>th</sup> random sample is located in the i<sup>th</sup> row of the Sample index. In other words random samples are used to describe the distribution of the results performing Monte Carlo analysis. {{disclink|MC as uncertainty dimension of a variable?}}
 
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'''Technical issues in Mediawiki'''
 
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|
 
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{{comment|#(number): |This should be moved. Where?|--[[User:Jouni|Jouni]] 20:00, 9 June 2008 (EEST)}}
 
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* Each variable is a page in the ''Variable'' namespace. The '''name''' of the variable is also the name of the page. However, draft variables may be parts of other pages.
 
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* All attributes except name are second-level (==) sub-titles on the page.
 
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* Description of the attribute content is added at the end of that content; discussions on the content are added to the Talk page, each discussion under an own descriptive title.
 
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* References to external sources are added to the text with the <nowiki><ref>Reference information</ref></nowiki> tag. The references are located in the  end of the page under subtitle References. However, reference is not an attribute of the variable despite it is technically similar.
 
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* In the formula, computer code for a specific software may be used. The following are in use.
 
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**Analytica_id: Identifier of the respective node in an Analytica model. <anacode>Place your Analytica code here. Use a space in front of each line.</anacode>
 
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** <rcode>Place you R code here. Use a space in front of each line.<rcode>
 
−
 
 
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'''Event-substance
 
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{{comment|#(number): |This paragraph should be deleted or removed. Where?|--[[User:Jouni|Jouni]] 00:40, 8 June 2008 (EEST)}}
 
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Variables are objects of event-medium composite -type. They thus describe both the events that occur within the scope of the variable and the medium where these particular events take place. In practice, the events can only be observed through the changes in the state of the medium, and it is therefore reasonable to describe the events and particular media as such composites rather than as separately.
 
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In open assessment, all the variables included in an assessment must be causally related, directly or indirectly, to the endpoints of the assessment, and the causal relations must be defined. The event-media structure is the carrier of the [[Causality | cause-effect relations]] between the variables. An event occuring in a medium causes a change in state of that medium leading to another event to occur changing the state of that medium, causing yet another event to occur and so on. In addition to variables, also classes as generalizations of properties possessed by variables can be causally related to each other.
 
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'''See also'''
 
−
 
 
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* [[Heande:Structures of the building blocks of open risk assessments]]
 
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* [[Open assessment]]
 
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* [http://heande.pyrkilo.fi/heande/index.php?title=Variable&oldid=5596 A previous version of this page] contains much of the discussion from the Intarese deliverables D17 and D18, which has been edited with a hard hand.
 
−
<<#
 

Latest revision as of 13:33, 16 December 2015


<section begin=glossary />

Variable is a description of a particular piece of reality. It can be a description of a physical phenomenon, or a description of value judgements. Also decisions included in an assessment are described as variables. Variables are continuously existing descriptions of reality, which develop in time as knowledge about the topic increases. Variables are therefore not tied into any single assessment, but instead can be included in other assessments. A variable is the basic building block of describing reality.<section end=glossary />

Question

What should be the structure of a variable such that it

  • is able to systematically handle all kinds of information about the particular piece of reality that the variable is describing, especially
    • it is generic enough to be a standard building block in decision support work (including interpretation of scientific information and political discussions),
  • is able to systematically describe causal relationships between phenomena and variables that describe them,
  • enables both quantitative and qualitative descriptions,
  • is suitable for any kinds of variables, especially physical phenomena, decisions, and value judgements,
  • inherits its main structure from universal objects,
  • complies with the PSSP ontology,
  • can be operationalised in a computational model system,
  • results in variables that are independent of the assessment(s) they belong to;
  • results in variables that pass the clairvoyant test.
  • can be implemented on a website, and
  • is easy enough to be usable and understood by interested non-experts?

Answer

Variable is implemented as a web page in Opasnet wiki web-workspace. A variable page has the following structure.

The attributes of a variable.
Attribute Sub-attribute Comments specific to the variable attributes
Name An identifier for the variable. Each Opasnet page have two kinds of identifiers: the name of the page (e.g. Variable) and the page identifier (e.g. Op_en2022). The former is used e.g. in links, the latter in R code.
Question Gives the question that is to be answered. It defines the scope of the variable. The question should be defined in a way that it has relevance in many different situations, i.e. makes the variable re-usable. (Compare to an assessment question, which is more specific to time, place and user need.)
Answer An answer presents an understandable and useful answer to the question. Its essence is often a machine-readable and human-readable probability distribution (which can in a special case be a single number), but an answer can also be non-numerical such as "very valuable" or a descriptive table like on this page. The units of interconnected variables need to be coherent with each other given the functions describing causal relations. The units of variables can be used to check the coherence of the causal network description. This is a so called unit test. Typically the answer contains an R code that fetches the ovariable created under Rationale/Calculations and evaluates it.
Rationale Rationale contains anything that is necessary to convince a critical reader that the answer is credible and usable. It presents the reader the information required to derive the answer and explains how it is formed. Typically it has the following sub-attributes, but also other are possible. Rationale may also contain lengthy discussions about relevant topics.
Data Data tells about direct observations (or expert judgements) about the variable itself.
Dependencies Dependencies R↻ tells what we know about how upstream variables (i.e. causal parents) affect the variable. In other words, we attempt to estimate the answer indirectly based on information of causal parents. Sometimes also reverse inference is possible based on causal children. Dependencies list the causal parents and expresses their functional relationships (the variable as a function of its parents) or probabilistic relationships (conditional probability of the variable given its parents).
Calculations Calculations R↻ is an operationalisation of how to calculate or derive the answer. Formula uses algebra, computer code, or other explicit methods if possible. Typically it is R code that produces and stores the necessary ovariables to compute the current best answer to the question.
Data not used Data not used are relevant for the research question, but for some reason they were not used in producing the current answer. I may be that the data was found after the synthesis, and an update has not yet been done; or it has been unclear how to merge these to the existing data. In any case, it is important to be differentiate and be explicit about whether data is irrelevant (and therefore removed from the page) or relevant but not used (and therefore waiting for further work).

In addition, it is practical to have additional subtitles on a variable page. These are not attributes, though.

  • See also
  • Keywords (not always used)
  • References
  • Related files

Rationale

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The structure is based on extensive discussions between Mikko Pohjola and Jouni Tuomisto in 2006-2008 and intensive application in Opasnet ever since.

For more detailed description about variables as information objects, see knowledge crystal.

See also

References


Related files

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