The five supervisory capacities of the Irreducibly Human framework

What this chart type is

A Brainstorm or Mind Map is a radial network diagram that externalises associative thinking. A central concept occupies the hub; related ideas radiate outward as branches; each branch can extend further into sub-concepts, creating a tree-like hierarchy that sprawls in two dimensions rather than descending top-to-bottom.

When rendered as a force-directed graph, the layout is computed by a physics simulation: nodes repel each other like charged particles while edges pull connected nodes together like springs. The simulation runs until kinetic energy dissipates and the graph reaches equilibrium. The result is not deterministic — the same data produces a different spatial arrangement on each run — but the connectivity structure is always preserved.

Node size and colour encode hierarchy level: the central node is largest and most saturated; primary branches are mid-sized; supporting concepts are smallest and most muted.

How to read this chart

Start at the central node (dark red, largest). Follow any edge outward to reach a primary branch (brass/gold). Each primary branch connects to two supporting concepts (grey-green, smallest). The spatial positions are physics-determined — proximity does not necessarily mean conceptual closeness; only edges encode actual relationships.

Drag any node to reposition it manually; the simulation will adjust surrounding nodes in response. Click restart simulation to re-randomise the starting positions and observe how the physics converges to a new (but structurally equivalent) layout.

The chart is best read locally — follow one branch at a time — rather than globally. The force-directed layout makes global pattern recognition difficult compared to a strictly hierarchical layout (tree diagram, sunburst), but it handles cross-branch connections more naturally if they exist.

Strengths of this form

Spatial freedom — nodes are not constrained to rows or columns, so the diagram can grow organically in any direction. Immediate overview — a viewer can see the full scope of a concept and all its associations in a single glance, even before reading individual labels. Natural for ideation — the radial structure mirrors how associative memory works: one idea triggers adjacent ideas, which trigger further associations.

Cross-connections — unlike a strict tree, a network mind map can show that a supporting concept connects to multiple branches (e.g. "Values" informing both Interpretive Judgment and Executive Integration), revealing shared underpinnings that a hierarchical layout would hide.

Limitations and when not to use it

Non-deterministic layout — the force-directed positions change on each run, so a reader cannot reliably memorise where a node will appear. This makes mind maps unsuitable for reference use cases where spatial memory matters.

Does not encode quantity — this form communicates structure and association, not size, frequency, or change over time. Adding quantitative data (e.g. node size = importance score) is possible but must be done carefully to avoid misleading area comparisons.

Clutters at scale — beyond ~40–50 nodes, edge crossings and label overlap make the chart unreadable without interactive filtering. Large knowledge graphs require hierarchical clustering, node grouping, or a dedicated network layout algorithm.