A Library of Systemic Relations
Birger Sevaldson 2011
Last version January 2016
Diagram of different ways to graphically treating relations between two entities. Line fonts and weight are used to codyfy the relations. (Birger Sevaldson, 2013, first version 2001 in article here >>>>> )
In our work we realized that we need to keep more attention to the relations. A simple line or arrow or a plus or minus like in casual loop diagrams is not sufficient. Therefore I developed this list of possible types of relations. It is probably incomplete and needs development. Go to the Forum to comment the list.
The colors are suggestions for color coding arrows or lines in a diagram. In addition to these one should use differentiation of fonts for lines and one should also label relations with descriptions.
This is a document in the making and under construction.
This is meant as an educational document and an ongoing research document. It is not a scientific document. For that it would need substantial discussions and literature review for each point something planned for later. Therefore many of the sources and links are at this time maybe off target and secondary and need to be treated with care.
The library suggests color coding and tagging with abbreviations for tagging the relations.
Revision: February 2016
December 2015: Changed the title to Library of Systemic Relations, to emphasize that this is not meant to be a typology.
28th January 2013
24th January 2013
26th October 2012
16th August 2011
An example of a relation oriented map as opposed to an object oriented map. The relations are described according to the library at that stage of development (Young et al 2013). Click map to see big version and use ctrl + and - to zoom in and out.
1. RELATIONS IN SYSTEMS THAT ARE DEPICTED WITH NODES AND CONNECTORS (typically objects connected with lines or arrows)
Network Theory >>>>>
Graph Theory >>>>>
1.1 STRUCTURAL RELATIONS, HIERARCHICAL SUPRA AND SUB SYSTEMS (GREENS) (STR)
More on hierarchies look here: >>>>>
1.1.1 Structural relations (Functional relations)
Very often systems are described as the assembly of parts where the sum is more than its parts. This is not a cause effect relationship but structural relationship.
Example: there is not a causal relationship between the wheels and the frames of a bicycle in the sense that e.g the frame decreases if the wheels increase. They are assembled in a structure where they generate together a surplus output. The whole is more than the sum of the parts.
More on structural relations here: >>>>>
1.1.2. Macro systemic relations (MSR):
Relations that are caused by the entities being subsystems in the same "supra-system" but without necessarily being inn direct contact with each other.
Example: Bikes and cars are related because they are sharing the same macro system: the roads. (They are related in additional ways than this)
1.1.3. Micro systemic relations (MiSR):
Systems that are related because they share a relation through a sub system:
Example: The rubber in the tires of the cars and the bikes come from the same producer.
1.1.4 Horizontal structural relations (HoSR)
Relations between branches in a tree structure.
1.2 SEMANTIC (Semiotic), THEMATIC, ASSOCIATIVE AND REPRESENTATIONAL RELATIONS (BLUE) (ATR)
1.2.1. Semantic relations (SR):
Semantic relations are entities being connected through a sentence where a word is forming the relation. Example: Fish - lives in - Water. Fish and water are the entities, lives in is the relation connector.
Cow - is a - mammal
See more here >>>>>
1.2.2. Categorical relations (CR):
Categorical relations are entities being part of the same thematic field or category. Themes and categories are manmade sorting devices and there is not necessarily e.g. a causal relation between members of a category.
Note: Categorization has its own problems especially with border line cases and items that fit into multiple categories. See also thematic relations as the term is used in linguistics.
Example: the relation between Universal Design and Ergonomics
1.2.3. Associative relations (AR):
Metaphors and analogies: These are the types of relations that pop up in brain storms by associations.
EuroVoc definition: The associative relationship is a relationship between two concepts which do not belong to the same hierarchical structure, although they have semantic or contextual similarities.
Example: If two people are very similar to each other in their look there is an associative relation.
For semiotic definitions on associative relations se Ferdinand Saussure
1.2.3. Representational relations (RR):
Images, representation, videos, simulations, VR and AR
The relation between a map and a landscape.
The relation between a diagram and the reality it represents.
The corelation between a VR environment for virtual prototyping and the reality it represents
1.3. SOCIAL RELATIONS (Yellows) (SR)
See also Social Network Analyses >>>>>
Read about social relations here >>>>>>
1.3.1. Structural social relations (SSR)
Example: Family, friends etc
Note: There are always multiple relations between e.g. members of a family, some are given others are optional. The structural (biological) relation between family members is given (constant), the social relation is optional or conditioned. One can choose to have a social relation with a relative. Or it is not possible to have a social relation to your ancient fore mothers.
1.3.2. Institutional social relations (ISR)
Example: Work, municipality, nation, culture, language, laws and regulations, money, contracts etc.
1.2.3. Actions (ASR)
Social relations created through action
Example: Sharing political interests.
Read more on Social Actions (based on Max Webers work) including seven different types of social actions >>>>>>
1.3. HARD RELATIONS, CAUSAL RELATIONS, FLOWS ETC. (REDS) (CR)
1.3.1 Causal relations (CR)
Cause and effect models: The nodes depict what entities causes an effects and what entities are being affected while the relations (normally arrows) depict the effect.
Example: If the heat is turned on the kettle starts to boil
Read more about Causality here: >>>>>>
1.3.2. Qualitative Causal Relation (QCR)
The amount or intensity will not be influenced but the quality will be changed
Example: The relation between architectural space and micro climate
1.3.3. Tools (CRT):
Tools are typically modifying and influencing the relations and not the entities directly.
Example: AR used to increase cultural understanding of biological systems.
1.3.4. Flows in human systems (FHS):
These are the concrete flows of values in our society. They are driven by needs and economic forces.
Examples related to human society: Material flows, Energy flows, Information flows, knowledge, Economic flows, Stock markeds.
1.3.5. Flows in natural systems (FNS)
These are driven by pressure differences (field conditions) and/ or by nuclear processes. On the high level these might be understood as causal relations, but on a detailed level they need to be understood as differentiations in uniform fields, like e.g. flows in water. They are caused by heat impact causing internal differentiation of pressure, but the shapes of the flows themselves are generated by internal chaotic principles resisting simple cause effect analyses.
Examples related to natural phenomena: water, air, magma, cosmic particle flows etc.
1.3.6. Variables, stocks and flows
This is the normal way of describing systems in Systems Dynamics. Variables are nodes that might change under the influence of other nodes. Flows are the flows of the content of the nodes form one to the other or the influence from one node to the other. Stocks are the storing capacity of the nodes.
Example: Classic example is a bath tub: if the inflow is bigger than the flow out of the drain the bathtub will be filled and flooded. If the flow out of the tub is bigger the tub will eventually be empty.
1.3.7. Negative relations (NCR)
If node A increases, node B decreases
Examples: The fox and rabbit example, (this tends to be a self stabilizing system)
1.3.8. Positive relations (PCR):
If node A increases, the node B increases or if node A decreases node B decreases:
Example: The increase of profit on the stock market leads to the increase of the amount of traders
1.3.9. Feedback loops (Floop):
The effect of a chain of causal relations between variables returns to the "starting node"
Positive feedback loop (+Floop):
The sum of the relations is positive, The system is unbalanced
Example ? (I find these very hard to get right because it is very difficult to interpret and it is all dependent on the variables one makes up) Hostile negotiations accelerating into war.
Negative feedback loop (-Floop):
The sum of the relations is negative: the system is balanced.
Example? Fox and rabbit.
2. SYSTEMIC RELATIONS THAT RESIST THE MODEL OF NODES AND CONNECTORS
Not all systemic relations can be abstracted to nodes with connections. They will have to be diagrammed with spatial maps, intensity maps or along time lines.
We should in many cases challenge the predominant systems model of entities and relations. In many cases it is more useful to use a model of continuum. When mapping out myriads of relations they will generate a weaving that is so dense that it generates a sense of a field more than a overview of a large amount of relations.
Examples are schools of fish or better flocks of starlings, the phenomena called Hive Minds, collective intelligence, continuums as in oceans and weather systems.
2.1. Spatial proximity (SP):
Elements sharing the same space within an operational proximity for the agent (e,g, user)
Examples: The relation between a chair and a table. There is of course also a thematic relationship because they both are furniture and also maybe a historic relationship because both belong to the same style. There is also a functional / structural relationship. (Who said this is simple?)
Example: the proximity between a neighbourhood and a park.
Example: the proximity of the Bygdøy museums
Different use of proximity-based relation here >>>>>
2.2. Temporal proximity (TP):
Elements share a temporal proximity in relation to an agent (e.g. user)
Example: Traffic regulation systems that are timed according to rush hours that again are caused by the working hours which again are influenced by the planetary system (day length).
Example: A cafe serving lunch at lunch hours.
2.3. Spatial distribution (SD):
Intensity fields, variation and differentiation of the distribution of similar elements in space.
Example: temperature across a room with a stove in one corner.
Example: The density and distribution of sunbathers in a park.
2.4. Temporal distribution (TD)
2.4.1. The distribution of elements over time,
Example: the distribution of intensities in a music composition.
Example: The distribution of traffic density during one day
2.4.2 Timing, rhythms, repetitions (TRR):
Same elements are appearing in a recognisable pattern.
Example: The repetitions in a music compositions.
Example: The rhythms of intensity in the density of traffic.
Example: the rhythms and patterns of usage of the rooms in a house.