Concept Taxonomy

This taxonomy groups the 528 concepts in concept-list.md into 13 categories for the learning graph viewer's legend and color-coding. Categories were formed by consolidating the 30 thematic sections of the concept list — each category is a contiguous ConceptID range, ordered roughly foundational to advanced. No category exceeds 19% of the total, so the graph viewer's legend stays readable and no single color dominates.

#	Category	TaxonomyID	Concepts	% of Total	ConceptID Range
1	Systems Thinking Foundations	FOUND	43	8.1%	1–43
2	System Dynamics & Feedback	DYNM	97	18.4%	44–140
3	Systems Archetypes	ARCH	36	6.8%	141–176
4	Archetype Case Studies & Named Laws	CASE	69	13.1%	177–245
5	Leverage Points & Emergence	LEVR	42	8.0%	246–287
6	Graph Theory & Graph Databases	GRPH	32	6.1%	288–319
7	Knowledge Representation & Data Management	KREP	39	7.4%	320–358
8	Enterprise Knowledge Graphs & Silos	ENTK	48	9.1%	359–406
9	Artificial Intelligence Systems	ARTI	38	7.2%	407–444
10	Knowledge Systems & Economic Complexity	KSEC	20	3.8%	445–464
11	Systems Design & Future Practice	DSGN	26	4.9%	465–490
12	Knowledge Graph Applications	KGAP	25	4.7%	491–515
13	Systems Thinking Across Disciplines	DISC	13	2.5%	516–528

Category Descriptions

Systems Thinking Foundations (FOUND)

The prerequisite vocabulary for everything else in the book: what a system is, its boundary, environment, inputs/outputs; core thinking moves (linear vs. systems thinking, reductionism vs. holism, root-cause analysis); and the basic language of models, mental models, and emergence.

System Dynamics & Feedback (DYNM)

How systems behave over time. Covers causal loop diagram notation (nodes, links, polarity, loop identification), stocks and flows, control-system concepts (set points, homeostasis), growth patterns and nonlinear behavior (exponential growth, S-curves, chaos, tipping points), and resilience/feedback-resistance concepts. The largest category, reflecting how central CLD mechanics are to the book.

Systems Archetypes (ARCH)

The cross-cutting vocabulary shared by every archetype (quick fix, unintended consequence, externality, misaligned incentive) plus the ten named archetype patterns themselves (Limits to Growth, Fixes That Fail, Shifting the Burden, Tragedy of the Commons, Success to the Successful, Drifting Goals, Escalation, Accidental Adversaries, Underinvestment, Seeking the Wrong Goal).

Archetype Case Studies & Named Laws (CASE)

Concepts that elaborate a specific archetype family — named laws and effects (Moore's Law, Campbell's Law, Goodhart's Law, the Matthew Effect, Metcalfe's Law), network-structure vocabulary (scale-free/small-world networks, network effects), and diffusion/agent-based modeling concepts that recur across multiple case studies.

Leverage Points & Emergence (LEVR)

Donella Meadows' leverage-points hierarchy (from low-leverage parameter tweaks to high-leverage paradigm shifts) plus complex adaptive systems and emergence — the most conceptually advanced "pure" systems-thinking material in the book.

Graph Theory & Graph Databases (GRPH)

Mathematical and technical graph foundations: vertices, edges, directed/weighted/property graphs, RDF, graph traversal algorithms, and native/distributed graph database architecture.

Knowledge Representation & Data Management (KREP)

Data modeling and governance vocabulary: entities, schemas, metadata, relational databases, normalization, data warehousing, ETL, data quality, and data governance — the data-engineering foundation the book's knowledge-graph thesis builds on.

Enterprise Knowledge Graphs & Silos (ENTK)

The book's organizational throughline: enterprise knowledge graphs, organizational silos and silo-busting, and the Capability Maturity Model for systems-thinking adoption.

Artificial Intelligence Systems (ARTI)

AI/ML foundations (algorithms, neural networks, LLMs, training data) and the AI-specific systems dynamics the book uses as running examples (the AI flywheel, algorithmic bias, recommendation platforms, AI governance).

Knowledge Systems & Economic Complexity (KSEC)

Chapter 12's material on tacit vs. explicit knowledge, knowledge decay and spillover, and economic-complexity concepts (product space, relatedness, learning curves).

Systems Design & Future Practice (DSGN)

Applied, forward-looking material: human-centered systems design, stakeholder analysis, emerging technology, and personal/organizational systems-thinking practice.

Knowledge Graph Applications (KGAP)

Applied knowledge-graph and graph-database patterns: master data management, semantic layers, graph algorithms and graph neural networks, graph analytics, and use cases like fraud detection and supply-chain graphs.

Systems Thinking Across Disciplines (DISC)

The book's closing survey of how systems thinking applies beyond IT — economic, ecological, social, urban, biological, political, market, and education systems.