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Concept Mapping

When a domain is described in prose or sketched as a diagram, the relationships between its concepts stay vague: a line is drawn between two boxes, or two ideas are called "related", and how they actually relate is never specified. Concept mapping refuses the unlabeled link. It builds a non-hierarchical semantic network in which every connection is a directed, labeled linking phrase, so each node-link-node triple reads as an explicit proposition ("free tier - increases - signup volume"), and clusters are joined by cross-links that connect concepts across different parts of the map. The load-bearing move is forcing every relationship to be named, which externalizes how the domain interrelates and makes gaps, missing links, and false propositions visible. The output is a concept map plus a list of surfaced gaps. It externalizes and inspects how concepts relate; it does not claim to improve learning, retention, or decisions.

When to Use

• A domain has many interrelated concepts and the goal is to make how they relate explicit and inspectable.
• You suspect hidden gaps or misconceptions in how a space is understood and want them surfaced as checkable propositions.
• Integration across sub-areas matters, so the cross-links between clusters carry the value (for example linking a pricing concept to a support-cost concept).

When NOT to Use

• To evaluate whether one argument or recommendation is sound - use argument-mapping. Both produce "maps" and this is the easiest confusion: argument mapping has a claim, reasons, co-premises, and objections and judges soundness; a concept map is a network of propositions and judges nothing.
• To decompose one big question top-down into MECE parts - use issue-tree. A concept map is a non-hierarchical network, not a decomposition tree, and does not aim for mutual exclusivity.
• To cluster many raw notes bottom-up with no named relationships - use affinity-mapping (the KJ method). Affinity mapping groups items into themes and deliberately does not name the relationship between them; if you only need themes, not propositions, use it.
• To move a problem down fixed event / pattern / structure / mental-model levels - use iceberg-model. The iceberg has prescribed causal levels; a concept map has none.
• If you drop the labeled-link / proposition constraint, you are doing free-association mind-mapping with unlabeled branches (the Buzan method), which this library excludes (X-tier; Farrand 2002). The named-relationship discipline is the skill; without it this collapses into the excluded method.

Instructions

When asked to map how the concepts in a domain relate, follow these steps:

1. List the concepts. Pull out the key concept terms in the domain (nouns / noun phrases). Aim for a focused set, not everything.
2. Connect with labeled, directed links. For each genuine relationship, draw a directed link and name it with a linking phrase (a verb or short phrase: "causes", "is a type of", "constrains", "increases", "depends on", "trades off against"). Never leave a link unlabeled. Each link must make source - link - target read as a true sentence.
3. Read every link back as a proposition. Write the node-link-node triple out as a sentence. If the sentence is vague, false, or one you cannot defend, fix the label or cut the link. This is where misconceptions surface.
4. Add cross-links across clusters. Find concepts in different clusters that should be connected and add labeled cross-links between them. These integrative links are the highest-value part of the map.
5. Surface gaps and missing links. Flag concepts with few or no links (under-connected), pairs of concepts that obviously relate but were never linked (missing links), and any propositions that look questionable.
6. Emit the concept map per references/TEMPLATE.md: the labeled-proposition network, the cross-links, and the explicit gaps / missing-links / questionable-propositions list.

Output Format

Use the template in references/TEMPLATE.md. The deliverable is the network of labeled propositions plus cross-links and the surfaced-gaps list, not prose.

Quality Checklist

Before finalizing, verify:

• Every link is a named, directed linking phrase; there are no unlabeled associations.
• Each node-link-node triple reads as a complete, defensible proposition (a sentence).
• At least one cross-link connects concepts across different clusters.
• Gaps (under-connected concepts), missing links, and questionable propositions are listed explicitly.
• The structure is a non-hierarchical network, not an argument tree or a MECE decomposition.
• No overclaim: it externalizes and inspects how concepts relate; it does not claim to improve learning, retention, or decisions (see evidence/dossier.md).
• The output is the concept-map artifact, not prose.

Evidence

Tier M/P, with a deliberate scope caveat. Concept mapping has a large human meta-analytic base (Nesbit & Adesope 2006: 55 studies, n=5,818; Schroeder et al. 2018: 142 effect sizes, n=11,814, overall g=0.58, constructing g=0.72 > studying g=0.43) - but those studies measure human knowledge retention, a memory-encoding outcome that does not transfer to an AI agent. That is why this skill is M/P and not S even though its base is larger than the S-graded argument-mapping: tier is set by whether the measured outcome transfers (reasoning quality does; retention does not), not by sample size. The transferable, practitioner-grade claim (Novak & Canas 2008; Davies 2011) is narrow: externalizing how concepts interrelate and forcing every relationship to be named surfaces gaps during construction. Evidence is transferred from human studies, not AI-validated. Full grading: evidence/dossier.md.

Examples

See references/EXAMPLE.md for a completed concept map.