Sources are collected across a range of Cybernetic sources, but principally quoted or paraphrased are: Primary Source(s): Beer (Brain of the Firm 1981, pp.401-403; Heart of the Enterprise 1979); Pask (An Approach to Cybernetics 1968, pp. 114-116); Bertalanffy (General System Theory, 1978);
Adaptation – The process of change required to maintain ‘fit’ between a current state and an outcome state. A good example is adaptation of an organism to an environment, or a product to customer needs, or a person and their desired identity. Activity continues until a measure of ‘fit’ is produced.
Algorithm – a set of steps to reach a known goal, also: Protocol, Procedure (depending upon domain). (see also partner term, ‘Heuristic’).
Amplification – From Latin ‘to extend’. In Cybernetics, it means to increase a variable within a system. The variable could be variety itself, or a volume of signal, or any other aspect within or without a System. A generative activity that creates more, diverging and including. A partner term with Attenuation. (See also ‘Attenuation’).
Attenuation – From Latin ‘to make thin’. In Cybernetics, it means to decrease, or constrain, a variable within a system. The variable could be the variety itself, or a volume of a signal, or any other aspect within or without a System. A convergent activity that selects and excludes. A partner term with Amplification. (See also ‘Amplification’).
Anastomotic – branching and reconnecting, like streams in a river delta. The entirety of the design process is Anastomotic by its nature, particularly within the Double-diamond approach.
Assembly – A part of the real world selected for observation. Every human field of endeavour might rightly be considered an ‘assembly’. Take for example, the study of biology, physics, chemistry, business, or design, which are all abstracted from absolute reality.
Artefact – Devices constructed to simulate some aspect of behaviour. When we describe the use of ‘Design Artefacts’, we are also creating a device to ‘stimulate’ some aspect of behaviour of our customers.
Controller – a natural or constructed assembly which interacts with its environment to bring about a specific stability called a ‘goal’, or ‘objective’.
Entelechy – the realisation of a potentiality in actuality.
Heuristic – a set of steps to reach an unknown goal. (see also partner term, ‘Algorithm’).
Multinode – a machine, brain, system or management-group made up individual decision-making elements and capable of reaching a corporate decision.
Paradigm – An exemplar or pattern; a basic way of doing something recognisable beneath many superficial variations.
Reticulum – (Latin: ‘a net’) a network of connections in which unique pathways may or may not be specifiable (see also ‘Anastomotic’).
Transducer – A machine, device, protocol or rule by which information is changed (edit: ‘transformed’) to an appropriate form and introduced into a system.
Ultra-stability – the capacity for a system to return to an equilibrial state after perturbation by unknown or unanalysed forces (against which the intervention of which the system was therefore not explicitly designed). See also ‘Homeostasis’.
Homeostasis – the capability of a system to hold its critical variables within physiological limits in the face of unexpected disturbance or perturbation, important to the survival or well-being of a system (such as a living system: ‘organism’). See also ‘Ultra-stability’.
Behaviour – An unchanging form of events due to the activity within the Assembly.
State – A recognisable condition of a system
Uncertainty – Any state of a system with a deficit of information. The reduction of uncertainty is directly correlated to the introduction of information.
System – a set of interrelated elements. An entity composed of at least 2 elements, and a relationship which holds those elements together. There are 5 types of System, per definitions by Cyberneticians over the last 100 years (Open/Closed, Abstract/Concrete, Simple/Complex, Static/Dynamic, Positive/Negative), which might all be considered lenses from which to view any System.
Open System – A category of System, by how information is shared. An open system is interdependent with and on information that comes from external sources, outside of itself.
Closed System – A category of System, by how information is shared. A closed system does not have any information about its environment, and is therefore closed to information from external sources. It depends strictly on information already within it.
Abstract System – A category of System, by the composition of the elements it contains. An abstract system is not defined by tangible or physical elements, but rather sets of concepts, theories, principles etc.
Concrete System – A category of Systems, by the composition of the elements it contains. A concrete system is defined by tangible or physical elements.
Adaptive System – An open system (see Open System), which adjusts its internal and external behaviour to fit with a state or states in the local environment.
Emergent System – A complex system (see Complex System), which cannot be split into parts and continue to achieve the common goal of the parts. A human or a car are examples of ‘Emergent Systems’.
Complex System – A categorisation of System, on the basis of the number of objects and relationships within it. Complex systems are juxtaposed with Simple systems, comprised of a large amount of variables and variety. There is no formal definition of the distinction of Simple vs Complex. (See also ‘Simple System’).
Simple System – A categorisation of System, on the basis of the number of objects and relationships within it. Simple systems are juxtaposed with Complex systems, comprised of few variables and variety. There is no formal definition of the distinction of Simple vs Complex. (See also ‘Complex System’).
Single-state System – A categorisation of a System based upon its change in properties over time. Also known as ‘Static’ system. An inanimate object with few alternative states. A table is a natural example, because of its structure it does not change its state. A partner term to ‘Multi-state System’. (See also ‘Multi-state system’).
Multi-state System – A categorisation of a System based upon its change in properties over time. Also known as ‘Dynamic’ system. An System whose state changes over time, due to changes in events. A Multi-state system may be Open or Closed. A partner term to ‘Single-state System’. (See also ‘Single-state System’).
Positive feedback loop – A type of system that a self-sustains behaviour on the basis of feedback. This tends to escalate, and lead to collapse in practice.
Negative feedback loop – A type of system that reduces the difference between a current state and a goal state, whilst there remains a deficit. An example of this is a thermostat (regulation of external temperature), or a refrigerator (regulation of internal temperature), which works to maintain a variable (the temperature). The less obvious Negative feedback loops are found in Businesses maintaining product-market fit. The activity of adjusting to fit is called ‘Adaptation’. (See also ‘Adaptation’).
State – A condition of an object, entity or element, consisting of particular properties at a given time. The state of a person could be their health, position, attitude, financial situation, number of romantic attachments, etc. There are a range and hierarchy of properties that define a state. For this reason, we can define 4 types of States (Current vs Goal, and Ideal vs Perceived).
Current State – Also known as ‘Actual’ State. The State of a System at the present time.
Goal State – Also known as ‘Outcome’ State, or ‘Target’ State. A variable which must be maintained, or reached by the System.
Ideal State – Also known as ‘Potential’ State. A special form of ‘Goal State’, whiccg/3:3,ch is possible, but not always known to the individual. This is representative of the Goal state without any constraints, physical, a lot perceived or otherwise.
Perceived State – A special form of ‘Current State’ in UX, wherein the user perceives their Current State. The distance between Current State and Perceived State might not always be the same.
Event – A change in a system, environment, or assembly state.
Space – A proxy notion to explain the variety within an Assembly. The breath, depth and width of a given ‘Space’, is defined by the variety and complexity within it, as well as the scope of the boundary. There are 4 types of Spaces: Problem-Solution Space, Knowledge and Phase Spaces. (see also, ‘Boundary’).
Problem Space – Also known as ‘Need’ Space. A specific type of ‘Assembly’, which includes factors defining the obstacles or blockers constraining the freedom of a System to reach an outcome state. A ‘problem’ could define a given customer behaviour(s), or challenge within or outside the team or business. From the Latin word for ‘obstacle’ (problema).
Phase Space – A set of events which comprise change in State, over time.
Knowledge Space – A state of knowledge of a System, specifically the degree of Certainty-Uncertainty within the System. Uncertainty demands more information, whereas Certainty has adequate information.