No prior knowledge needed. This page explains what you're looking at, who the thinkers are, and how to explore.
A knowledge graph is a way of mapping out ideas and showing how they connect to each other. Instead of reading about concepts in a book one page at a time, you can see the whole landscape of ideas at once — which ones lead to which, which ones build on each other, and which ones contrast.
In the diagram above, each circle is a concept (an idea), and each arrow is a relationship showing how the ideas connect. The colors tell you which thinker the concept comes from. That's all there is to it — a knowledge graph is a map of ideas and their connections.
This particular graph maps 408 concepts from six foundational thinkers in cybernetics and systems theory, with 283 relationships between them. It was built by carefully reading their original texts and extracting each concept and how it relates to the others.
A psychiatrist and pioneer of cybernetics. His books An Introduction to Cybernetics (1956) and Design for a Brain (1952) laid the mathematical foundations for understanding how systems regulate themselves. Key ideas include variety (the number of possible states), requisite variety (you need at least as much variety in your regulator as in the disturbance), and homeostasis (how systems maintain stability).
A cybernetician who developed Conversation Theory (1975, 1976), which frames learning as a structured dialogue rather than a transfer of information. Central ideas include entailment meshes (networks of how concepts depend on each other), teachback (proving you've learned something by teaching it back), and P-individuals (any entity capable of having a learning conversation).
A philosopher and mathematician whose Process and Reality (1929) proposed that reality is made of momentary events of experience, not static substances. Key ideas include actual occasions (the fundamental drops of experience that make up reality), prehension (how each event grasps and incorporates past events), and concrescence (the process by which an event becomes itself).
Founded computation theory and defined the limits of what machines can compute. His work on machine intelligence anticipated modern AI, and his morphogenesis research showed how mathematical processes create biological patterns. A member of the Ratio Club alongside Ashby. Key ideas include Turing machine (abstract model of computation), computability (what problems can be solved by algorithms), Imitation Game (the Turing Test for machine intelligence), and morphogenesis (how patterns emerge in biological systems).
Created the Viable System Model and pioneered management cybernetics, applying Ashby's principles to organisational design. His work on variety engineering, autonomy, and recursive structure showed how living systems maintain viability. A founding figure alongside Ashby in operational cybernetics. Key ideas include Viable System Model (recursive structure for organisational viability), variety engineering (managing complexity through requisite variety), autonomy (self-governance within viable systems), and algedonics (real-time signals of pleasure and pain for system alerting).
Author of Laws of Form (1969), which derives Boolean algebra from a single act of distinction. Key ideas include the mark (token of distinction), re-entry (self-referential forms), imaginary values (beyond binary states), and the derivation of time from self-reference.
These six thinkers are connected by a shared commitment to understanding the world in terms of process, relationship, and interaction rather than fixed things. Ashby gives us the mathematics of regulation, Pask gives us the theory of learning through conversation, Whitehead gives us the philosophical foundation that reality itself is processual, Turing gives us the theory of computation and information processing that underlies it all, Beer gives us the science of effective organisation through his Viable System Model, and Spencer-Brown gives us the formal foundation â from a single act of distinction, he derives Boolean algebra, self-reference, and time itself.
When mapping how ideas relate to each other, you need a precise vocabulary. Saying "these ideas are related" isn't enough — you need to say how. After careful analysis of the source texts, twelve types of relationship emerged as sufficient to capture the logical structure of these thinkers' work:
| Relationship | What it means |
|---|---|
|
Entails
cybernetics → functional approach
|
"If you accept A, then B necessarily follows." This is the strongest connection — a logical or definitional consequence. Cybernetics entails a functional approach because the very definition of cybernetics is about behaviour, not substance. This is the most common relationship type (104 uses), especially dominant in Pask's work where concepts form tight logical chains. |
|
Derives From
M-individual → entailment mesh
|
"B grows out of A" or "B depends on A for its meaning." An M-individual (a coherent chunk of knowledge) derives from the structure of the entailment mesh — you can't have one without the other. The most-used type in Ashby's work (82 uses), reflecting how he carefully builds each concept on top of previous ones. |
|
Generalizes
prehension → physical prehension
|
"A is the broader category that includes B." Prehension generalizes physical prehension — physical prehension is one specific kind of prehension. This captures the abstraction hierarchy in these thinkers' frameworks. |
|
Particularizes
geometry analogy → all possible machines
|
"B is a specific instance or case of A." The reverse of Generalizes. A geometry analogy particularizes the abstract concept of all possible machines by giving a concrete illustration. Together, Generalizes and Particularizes map the ladder of abstraction from specific to general. |
|
Constrains
law of requisite variety → regulation
|
"A limits or bounds what B can be." The law of requisite variety constrains what counts as effective regulation — your regulator must have enough variety to match the disturbance. This captures the important role that limitations and boundaries play in cybernetic thinking. |
|
Enables
concrescence → satisfaction
|
"A makes B possible." Concrescence (the process of becoming) enables satisfaction (the completion of an actual occasion). Unlike Entails, this doesn't mean B necessarily follows — just that A opens the door for B. Particularly prominent in Whitehead's work. |
|
Contrasts
physical prehension → conceptual prehension
|
"A and B are importantly different." Physical prehension contrasts with conceptual prehension: one grasps actual occasions, the other grasps eternal objects. These aren't opposites — they're distinctions that matter for understanding. |
|
Analogous To
M-individual → chunk (cognitive psychology)
|
"A and B are structurally similar but from different contexts." An M-individual in Pask's theory is analogous to a chunk in cognitive psychology — they serve a similar role but in different theoretical frameworks. The rarest relationship (4 uses), reserved for genuine structural parallels across thinkers or disciplines. |
|
Corresponds
Turing's morphogenesis → Whitehead's concrescence
|
"A parallels B across thinkers." Cross-thinker structural parallel showing how different thinkers describe similar processes through their own frameworks. Turing's morphogenesis (how mathematical processes create biological patterns) corresponds to Whitehead's concrescence (the process by which an event becomes itself) — both describe emergence through process. |
|
Requires
Imitation Game → universal Turing machine
|
"A needs B to function." Functional dependency where A cannot operate without B. The Imitation Game (Turing test for machine intelligence) requires a universal Turing machine as its foundationâyou can't test intelligence without a computing substrate. |
|
Extends
Turing machine → reaction-diffusion systems
|
"A builds on and broadens B." Extension to new domain where an existing concept is applied in a new context. The Turing machine (abstract computation model) extends into reaction-diffusion systems (patterns in morphogenesis), showing how computational principles apply to biological pattern formation. |
|
Illustrates
Discrete state machine → Turing machine
|
"A is a concrete example of B." Physical realization or concrete instantiation of an abstract principle. A discrete state machine illustrates the Turing machine concept — it's a specific, buildable system that demonstrates the general principle of computation. |
These twelve types weren't chosen arbitrarily. They emerged from what the source texts actually needed. Together they capture five dimensions of how ideas relate:
Logical dependency: Entails and Derives From capture when one idea requires or follows from another.
Abstraction level: Generalizes and Particularizes map the hierarchy from abstract principles to concrete instances.
Enablement and limitation: Enables and Constrains capture how ideas open up or bound possibilities for each other.
Distinction and analogy: Contrasts and Analogous To capture important differences and cross-domain similarities.
Cross-thinker and functional relations: Corresponds, Requires, Extends, and Illustrates capture how ideas relate across thinkers and how concepts instantiate in different domains.
You could use fewer types, but you'd lose important distinctions. You could use more, but these twelve turn out to be sufficient for everything these six thinkers say. Think of them as a toolkit — just enough tools to build anything in this domain, with no redundancy.
When you first open the explorer, you'll see all 408 concepts arranged as a force-directed graph. Concepts that are more connected cluster together naturally. The colours show which thinker each concept belongs to: blue for Ashby, purple for Pask, amber for Whitehead, red for Turing, and green for Beer, and teal for Spencer-Brown. Bigger circles have more connections.
Click any circle to see its details in the right panel: its definition, intuition (a plain-language explanation), source, and all its incoming and outgoing relationships. The surrounding concepts will highlight while everything else dims, so you can see the local neighbourhood.
Use the left panel to show only one thinker at a time. Click "Pask" to see just the 35 Conversation Theory concepts and how they relate to each other. This is a great way to learn one thinker's framework before seeing how they connect to the others.
Want to see only the logical chains? Filter by "Entails." Want to see where thinkers disagree or make distinctions? Filter by "Contrasts." This lets you slice the graph by the kind of connection rather than the thinker.
Use the search bar at the top to find any concept by name or keyword. Type "variety" or "feedback" and click a result to jump straight to it in the graph.
The default "Force-Directed" layout clusters related concepts together. Try "Circle" to spread everything evenly, "By Degree" to put the most-connected concepts at the centre, or "Grid" for an orderly arrangement. Use the + and − buttons or scroll to zoom.
Tip: Hover over any concept to see a quick tooltip with its name and definition. Hover over any connecting line to see what type of relationship it is and the textual evidence from the original source.
Here are some starting points for exploration:
The largest circles are the most connected ideas — they're the hubs of their thinker's framework. For Ashby, look for variety, transformation, and homeostasis. For Pask, look for conversation and entailment mesh. For Whitehead, look for actual occasion and prehension.
Filter by "Entails" to see the logical skeleton of each thinker's work. These chains show you the order in which ideas must be understood — you can't understand requisite variety without first understanding variety, constraint, and regulation.
Look for the rare "Analogous To" relationships and any edges that connect concepts of different colours. These show where Ashby's cybernetics, Pask's conversation theory, and Whitehead's process philosophy speak to the same underlying ideas from different angles.
Filter by "Contrasts" to see the important distinctions each thinker makes. These aren't disagreements — they're the places where a thinker says "these two things might look similar, but the difference matters."
Open the interactive graph and start clicking around. There's no wrong way to explore.
Open the Knowledge Graph Explorer