Ontometrics

measuring whether an ontology is underdeveloped, calibrated, or overdetermined
current phase
Calibrated
4 categories · 3 axioms · 4 cases
quality Q
82%
MDL-inspired score
fit
93%
coverage + discrimination
structure load
46%
complexity + redundancy + inconsistency
Harman
balanced
index: 0.00
ClaudeApril 2026·Initial implementation
Coverage100%
Discrimination83%
Redundancy17%
Inconsistency0%
Complexity92%
Brittleness5%
avg labels/case
1.75
assignment density
44%
MDL decomposition: L(O) + L(D|O)
L(O) = description length of ontology. L(D|O) = residual surprise (unexplained phenomena). Good ontology minimizes the sum.
Harman undermining / overmining axis
underminingbalancedovermining

Angels on a pin

Medieval scholars are mocked for asking how many angels fit on a needle's tip. But the mockery misses the point: they were probing real problems — occupancy, individuation, locality, causation for non-material agents — with the only concepts available to them. “Angel questions” are what inquiry looks like when a culture has problem-sensitivity but lacks clean abstractions. The vocabulary may be wrong; the underlying pressure may be profound.

The CAD analogy

An ontology can fail in two opposite directions, mirroring CAD sketch constraints. An underdeveloped ontology has too few concepts to carve reality well — like an underconstrained sketch where many shapes satisfy the dimensions. An overdetermined ontology has too many categories, constraints, or distinctions relative to what the phenomena warrant — like an overconstrained sketch that locks up or becomes brittle. Good ontology sits in the middle: enough distinctions to track reality, not so many that it becomes baroque.

The quality function

The playground uses an MDL-inspired quality function that balances fit against structural cost:

Q(O;D)=FitλKμRedνIncρBritQ(O;D) = \text{Fit} - \lambda K - \mu \text{Red} - \nu \text{Inc} - \rho \text{Brit}

where Fit=0.55Coverage+0.45Discrimination\text{Fit} = 0.55 \cdot \text{Coverage} + 0.45 \cdot \text{Discrimination}, KK is structural complexity, Red\text{Red} is category redundancy (Jaccard overlap), Inc\text{Inc} is axiom inconsistency, and Brit\text{Brit} is revision brittleness. The penalty weights λ,μ,ν,ρ\lambda, \mu, \nu, \rho are adjustable.

Harman's axes

Graham Harman distinguishes two modes of ontological reduction. Undermining reduces a thing downward to its constituent parts. Overmining reduces a thing upward to its effects, appearances, or relations. These are orthogonal to the underdeveloped/overdetermined axis: an ontology can be underdeveloped AND overmining, or overdetermined AND undermining. The playground estimates a Harman index from axiom type distribution — subtype-heavy axioms lean toward overmining, dependsOn-heavy axioms lean toward undermining.

MDL interpretation

The Minimum Description Length principle says: the best model minimizes the sum of the model's description length L(O)L(O) and the residual surprise L(DO)L(D|O) — how much the phenomena remain unexplained. An underdeveloped ontology has low L(O)L(O) but high L(DO)L(D|O). An overdetermined ontology has bloated L(O)L(O) while L(DO)L(D|O) barely decreases. The playground visualizes this decomposition.

The strongest single test

Does the ontology introduce exactly the distinctions needed to explain stable differences in the world, without multiplying categories beyond independent necessity? That is probably the closest thing to an engineering criterion for ontology.

Model changelog

v1April 2026
  • Ontology editor with categories, relations, axioms (disjoint/subtype/dependsOn/identity), and case assignments
  • 4 domain presets: metaphysics, mind/consciousness, rights/law, particle physics
  • MDL-inspired quality function: Q = Fit − λ·Complexity − μ·Redundancy − ν·Inconsistency − ρ·Brittleness
  • 5-phase classification: Underdeveloped, Calibrated, Heavy but workable, Overdetermined, Brittle confusion
  • Harman undermining/overmining index from axiom type distribution
  • MDL decomposition: description length vs residual surprise
  • Phase map SVG with fit vs structure-load quadrants