Library of Systemic Relations

 

A Library of Systemic Relations

Birger Sevaldson 2011


Relations fonts web

 

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 realised that we needed to focus more of our attention on the relations. A simple line or arrow or a plus or minus, such as what one would find in casual loop diagrams, is not sufficient. Therefore, I developed this list of possible types of relations. It is probably incomplete and needs development. Please, go to the Forum to comment on the list and add more relations.

The colours are suggestions for colour-coded arrows or lines in a diagram. In addition to these, it would be wise to use different fonts for lines and label relations with descriptions.

This is a document under construction.

This is meant as an educational document and an ongoing research document. It is not a scientific document. For it to be this, it would need substantial discussions and a literature review for each point, something which is planned for later. Therefore, many of the sources and links might currently be off target and secondary and should be treated with care.

The library suggests colour coding and tagging with abbreviations for marking the relations.

 

Presentation from RSD5

 

Birger Sevaldson

 

Revision: December 2017

Earlier revisions:

January 2016

December 2015: Changed the title from TYPES to LIBRARY of Systemic Relations, to emphasize that this is not meant to be a typology.

February 2015 

28th January 2013

24th January 2013

26th October 2012

16th August 2011

 

web map

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 the map to see a larger 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)

 See also

Network Theory >>>>>

Graph Theory >>>>>

1.1 STRUCTURAL RELATIONS, HIERARCHICAL SUPRA- AND SUBSYSTEMS (GREENS) (STR)

 

More on hierarchies 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 and effect relationship; rather, it is a structural relationship.

Example: There is no causal relationship between the wheels and the frames of a bicycle in the sense that the frame becomes smaller if the wheels grow larger. They are assembled in a structure where what they generate together creates a surplus output. The whole is more than the sum of the parts.

Example: Think of the relation in the air traffic system between the planes and the control system. The number of planes does not automatically decrease if the control system is reduced. This only happens through institutional regulations.

More on structural relations here: >>>>>

1.1.2. Macro-systemic relations (MSR)

 


These are relations that are caused by the entities being subsystems in the same "supra-system" but without necessarily being in 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.)
Examples: A winter coat and bikini are both part of the clothing wardrobe of the same person.

1.1.3. Micro-systemic relations (MiSR)

 


These systems are related because they share a relation through a subsystem:

Example: The rubber in the tires of cars and bikes comes from the same producer.
Example: A Mixmaster and a hair dryer can share similar electronic parts from the same manufacturer.

1.1.4 Horizontal structural relations (HoSR)

 


These are 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 connected through a sentence where one word forms the relation.

Example: Fish — lives in — Water. Fish and water are the entities while lives in is the relation connector. 

Cow — is a — mammal

See more here >>>>>

1.2.2. Categorical relations (CR) 

 


Categorical relations are entities part of the same thematic field or category. Themes and categories are manmade sorting devices, and there is not necessarily a causal relation between the members of a category.

Note: Categorisation has its own problems, especially when it comes to borderline cases and items that fit into multiple categories. See also thematic relations because the term is used in linguistics.

Example: The relation between universal design and ergonomics.
Example: Genres of music. There are many possible relations between genres of music, but if we think of the relation between the music of Australian aborigines and a symphony by Bach, we can only think of a few relations, such as biological (music being programmed in our genes) and thematic relations (both being music).

1.2.3. Associative relations (AR)

 


Metaphors and analogies: These are the types of relations that pop up during brainstorming because of associating one thing with another.[.1] 

EuroVoc definition: The associative relationship is a relationship between two concepts that 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 apperances, there is an associative relation.
Example: If I say bird, you say fish....

For semiotic definitions on associative relations, see Ferdinand Saussure

Sources here >>>>>> in Daniel Chandlers Semiotics for Beginners

1.2.3. Representational relations (RR)

 


Images, representation, videos, simulations, VR and AR

Example: The relation between a map and landscape.
Example: The relation between a diagram and the reality it represents.
Example: The correlation between a VR environment for virtual prototyping and the reality it represents.

More here >>>>>>
And here 
>>>>>

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, for example, the members of a family; some are given while others are optional. The structural (biological) relation between family members is given (constant) while the social relation is optional or conditioned. One can choose to have a social relation with a relative. But it is not possible to have a social relation with your ancient foremothers.

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’ [.2] 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 cause an effect and what entities are being affected; the relations (normally arrows) depict the effect.

Example: If the heat is turned on, the kettle starts to boil.
Example: If the tolls for entering the city by car increase, the passengers using public transportation go up.

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 the micro-climate.

1.3.3. Tools (CRT)

 

Tools are typically modifying and influencing the relations, not the entities directly.

Example: AR used to increase a cultural understanding of biological systems.

1.3.4. Flows in human systems (FHS)

 


These are the concrete flows of values in society. They are driven by needs and economic forces.

Examples related to human society: Material flows, energy flows, information flows, knowledge, economic flows and stock markets
Examples: Traffic flow and crowds of people

1.3.5. Flows in natural systems (FNS) 

 


These are driven by pressure differences (field conditions) or by nuclear processes. At one level, these might be understood as causal relations, but on a more detailed level, they need to be understood as differentiations in uniform fields, such as flows in water, which are caused by the impact of heat causing internal differentiation of pressure, but the shapes of the flows themselves are generated by internal chaotic principles that resist simple cause and 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 from one node to the other or the influence from one node to the other. Stocks are the storing capacity of the nodes.

Example: A classic example is a bath tub: if the inflow of water is more than the flow out of the drain, the bathtub will fill up too fast and flood. If the flow out of the tub is larger, the tub will eventually be empty.
Example: The number of predators and prey will influence each other. If the amount (stock) of rabbits is increasing, the amount of foxes will increase, which will lead to a decrease of rabbits, which will lead to a decrease of foxes, which will lead to an increase of rabbits and so on.
Example: The relation between the price of goods and the availability of them

1.3.7. Negative relations (NCR)

-


If node A increases, node B decreases.

Example: The fox and rabbit example (tends to be a self-stabilising system)

1.3.8. Positive relations (PCR) 

 + 


If node A increases, the node B increases, or if node A decreases, then node B decreases.

Example: The a company showing increased profits on the stock market leads to an increase in the number of traders.

1.3.9. Feedback loops (Floop)

The effect of a chain of causal relations between variables that returns to the ‘starting node’.

Positive feedback loop (+Floop)

The sum of the relations is positive, so the system is unbalanced.

Example: (I find these very hard to get right because it is difficult to interpret, and it all depends on the variables one makes up.) Hostile negotiations accelerating into war

Negative feedback loop (-Floop)

The sum of the relations is negative, and the system is balanced.

Example: The fox and rabbit populations regulate each other.
Example: If the price goes up, the sales go down (-), then the price goes down (+) and the sales go up (-) and the price goes up (+). This is seemingly a self-stabilising system, but it's not a negative feedback loop because it's neutral (two -  and two +). The model is never quite like reality.

2. SYSTEMIC RELATIONS THAT RESIST THE MODEL OF NODES AND CONNECTORS

Not all systemic relations can be abstracted to nodes with connections. They must be diagrammed with spatial maps, intensity maps or along timelines.

In many cases, we should 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 the myriad of relations, they will generate a weaving that is so dense that it generates a sense of a field more than an overview of a large amount of relations.

Examples are schools of fish or flocks of starlings, the phenomena called hive minds, collective intelligence or 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., the user)

Examples: The relation between a chair and table. Of course, there is also a thematic relationship because they both are furniture and also may have a historic relationship because both could belong to the same style. There is also a functional and 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 relations here >>>>>

2.2. Temporal proximity (TP)

Elements share a temporal proximity in relation to an agent (e.g., the user).

Example: Traffic regulation systems that are timed according to rush hours, which of course 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, variations 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 over the course of one day

2.4.2 Timing, rhythms and repetitions (TRR)

The same elements are appearing in a recognisable pattern.

Example: The repetitions in a music composition
Example: The rhythms of intensity in the density of traffic
Example: The rhythms and patterns of usage of the rooms in a house