Library of Systemic Relations

Focusing more of our attention on the relations in our work.

The library of systemic relations suggests categories, colour coding and tagging with abbreviations for marking the relations between nodes in a system.

A simple line or arrow or a plus or minus, such as one would find in casual loop diagrams, is insufficient. Therefore, this project identifies possible types of relations. It is in an early stage and primed for development.

The colours are suggestions for arrows or lines in a diagram. In addition, different fonts for lines and label relations with descriptions would further delineate relationships.

Relations fonts web

Diagram of different ways to graphically treating relations between two entities. Line fonts and weight are used to codyfy the relations.

Sevaldson, B. (2016). A Library of Systemic Relations. In Proceedings of Relating Systems Thinking and Design (RSD5) 2016 Symposium. OCAD University, Toronto, Ontario, October 14-15, 2016.

Note: The library of systemic relations is meant as an educational document and an ongoing research document. It is not a scientific document. Substantial discussions and a literature review are needed to validate the library. Many sources and links are preliminary and should be treated with care. This is a document under construction, and further work is anticipated in the future.

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).

Types of Systemic Relations Map

Young Eun Choi, Birger Sevaldson, AHO 2013


– Typically objects are connected with lines or arrows.

Reference: Wikipedia Network Theory | Graph Theory



Wikipedia Hierarchies


Carnie, Andrew (2006). Structural Relations: The mathematical properties of phase structure trees.

Carnie, Andrew (2020). Video introduction: Carnie 2021 Syntax 4th Edition.

Structural Relations

1.1.1 Structural relations (Functional relations) (SRFR)

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 1: 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 creates a surplus output. The whole is more than the sum of the parts.

Example 2: Think of the relationship 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.

1.1.2. Macro-systemic relations (SRMA)

These are relations caused by the entities being subsystems in the same “supra-system” but without necessarily being in direct contact with each other.

Example 1: Bikes and cars are related because they are sharing the same macro-system: the roads. (They are related in additional ways than this.)

Example 2: A winter coat and bikini are both parts of the clothing wardrobe of the same person.

1.1.3. Micro-systemic relations (SRMI)

These systems are related because they share a relationship through a subsystem.

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

1.1.4 Horizontal structural relations (SRHO)

These are relations between branches in a tree structure.

1.1.5. Vertical structural relations (SRVE)

1.2 Semantic (semiotic), thematic, associative, and representational relations (SA) – Blues



Wikipedia Semantic Networks


Chandler, Daniel (n.d). Semiotics for Beginners Introduction and Paradigms and Syntagms.


Wikipedia Representation (arts)

1.2.1. Semantic relations (SASR)

Semantic relations exist between entities connected through a sentence where one word forms the relation.

Example: A fish — lives in — water. Fish and water are the entities, while “lives in” is the relation connector.

1.2.2. Categorical relations (SACR)

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 relationship 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 1: The relation between universal design and ergonomics.

Example 2: Genres of music. There are many possible relations between genres of music. Still, 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 (SAAR)

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

The associative relationship links two descriptors that are neither equivalent nor hierarchical, although they have semantic or conceptual similarities. – EuroVoc

Example 1: If two people are very similar in appearance, there is an associative relation.

Example 2: If I say bird, you say fish …

1.2.3. Representational relations (SARR)

Images, representation, videos, simulations, VR and AR

Example 1: The relation between a map and landscape.

Example 2: 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.

1.3 Social Relations (SO) – Yellows


Social Network Analyses

Social relations


Wikipedia Social Actions

Note Max Weber’s work, including seven different types of social actions.

1.3.1. Structural social relations (SOSR)

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 (SOIR)

Example: Work, municipality, nation, culture, language, laws and regulations, money, contracts, etc.

1.3.3. Actions (SASR)

Social relations created through action

Example: Sharing political interests

1.3.4. Emotional relations (SAER)




Wikipedia Causality

1.4.1 Causal relations (HRCR)

Cause and effect models: The nodes depict what entities cause an effect and what entities are being affected; the relations (normally arrows) depict the impact.

Example 1: If the heat is turned on, the kettle starts to boil.

Example 2: If the tolls for entering the city by car increase, the number of passengers using public transportation goes up.

1.4.2. Qualitative causal relation (HRQR)

The amount or intensity will not be influenced, but the quality will be changed.

Example: The relation between architectural space and the micro-climate.

1.4.3. Relational Tools (HRRT)

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

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

1.4.4. Flows in human systems (HRFH)

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

Example 1 (related to human society): Material flows, energy flows, information flows, knowledge, economic flows and stock markets.

Example 2: Traffic flow and crowds of people.

1.4.5. Flows in natural systems (HRFN)

These are driven by pressure differences (field conditions) or nuclear processes. At one level, these might be understood as causal relations. At 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, driving internal differentiation of pressure. Still, the shapes of the flows themselves are generated by chaotic internal principles that resist simple cause-and-effect analyses.

Examples related to natural phenomena are water, air, magma, cosmic particle flows, etc.

1.4.6. Variables, stocks and flows (HRSF)

This is the standard 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 of one node to the other. Stocks are the storage capacity of the nodes.

Example 1: A classic example is a bathtub: if the inflow of water is more than the flow out of the drain, the bathtub will fill up too fast and flood. If there is no inflow, the outflow will eventually empty the tub.

Example 2: The number of predators and prey will influence each other. If the amount (stock) of rabbits is increasing, the number of foxes will increase, which will lead to a decrease of rabbits, which will lead to a decline in the number of foxes, which will lead to an increase in rabbits and so on.

Example 3: The relation between the price of goods and their availability.

1.4.7. Negative relations (HRNR)

If node A increases, node B decreases.

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

1.4.8. Positive relations (HRPR)

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

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

1.4.9. Feedback loops (HRFloop)

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

Positive feedback loop (HR+Floop)

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

Example: Hostile negotiations accelerating into war. (Note: these are hard to get right because it is difficult to interpret and is dependent on the variables one makes up.) 

Negative feedback loop (HR-Floop)

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

Example 1: The fox and rabbit populations regulate each other.

Example 2: 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.


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 a continuum. When mapping out the myriad of relations, they will generate a weaving so dense that it generates a sense of a field more than an overview of a large number 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 Proximinty (SP)


Wikipedia The  Proximity Principle

2.1.1 Elements sharing the same space within an operational proximity for the agent (e.g., the user).

Example 1: The relation between a chair and a table. Of course, there is also a thematic relationship because they both are furniture and 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 2: The proximity between a neighbourhood and a park Example: The proximity of the Bygdøy museums.

2.2 Temporal Proximity (TP)

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

Example 1: 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 2: A cafe serving lunch at lunch hours.

Example 2: The proximity between a neighbourhood and a park.

Example 3: The proximity of the Bygdøy museums.

2.3. Spatial distribution (SD)

2.3.1 Intensity fields, variations and differentiation of the distribution of similar elements in space

Example 1: Temperature across a room with a stove in one corner.

Example 2: The density and distribution of sunbathers in a park.

2.4 Temporal Distribution (TD)

2.4.1. The distribution of elements over time

Example 1: The distribution of intensities in music composition.

Example 2: The distribution of traffic density over the course of one day.

2.4.2 Timing, rhythms and repetitions (TRR)

The same elements appear in a recognisable pattern.

Example 1: The repetitions in music composition.

Example 2: The rhythms of intensity in the density of traffic.

Example 3: The rhythms and patterns of usage of the rooms in a house.

© Birger Sevaldson


04 December 2022 SOD 2.0 revision (formatting and line edits)

Earlier revisions

27th May 2018
December 2017
January 2016
December 2015: Title changed 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


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