The compute layer

Flags: --formulas, --decisions, --sensitivity (or --compute).

ThoughtML can compute over the numbers in a document — formulas, expected value, sensitivity. This is the most powerful part of the mirror, and the one to be most careful about framing:

The compute layer is a second reading of the author’s numbers, not a program the document runs. Every result is opt-in, lands in its own field, and never overwrites what you wrote.

Quantities recap

A focus can carry an authored quantity — a number with a unit, classified into a dimension and normalized to a base unit where convertible. Quantities are the inputs the rest of this layer reads.

Formulas

--formulas. A focus whose value is computed from other foci, written as a = <expr> line:

focus hosting
  quantity 1200 USD
focus bandwidth
  quantity 300 USD
focus monthly-cost
  = hosting + bandwidth

• The expression supports references to other foci, numbers, quantities, the arithmetic operators + - * / ( ), and functions like min/max/sum.
• Evaluation runs in dependency order, so a formula sees its inputs’ computed values. A dependency cycle is detected and reported (never computed).
• Full dimensional analysis: you can multiply USD/instance by instance, but not add dollars to milliseconds — a dimension clash is a warning.
• The result lands in computed_quantity, presented in a human-friendly unit (8 GB, not 8e9 B) and strictly separate from any authored quantity. A computed value has no provenance basis — it wasn’t authored.

The bundled cloud-bill.thml is a full worked example.

Decision expected value

--decisions. The capstone, composing quantities, formulas, and derived confidence. The model:

• An option focus has leads-to edges to outcome foci.
• Each leads-to edge carries a probability; if it doesn’t, the outcome’s derived confidence is used as a fallback.
• Each outcome carries a payoff — its computed_quantity if a formula produced one, else its authored quantity.

Then:

expected_value(option) = Σ  probability · payoff

with full dimensional checking (you can’t average dollars with milliseconds). A decision focus, named by option-of edges, gets its options ranked by expected value, highest first. Each option also reports its downside (the worst-case payoff) and probability_mass (Σ probability).

link harvard option-of where-to-go
link harvard leads-to harvard-thrive
  probability 0.7
link harvard leads-to harvard-coast
  probability 0.3

It ranks; it does not crown. There is deliberately no best option and no margin. The mirror reports the expected values, ordered, with each option’s downside — and leaves the choice to you. Decisions are about risk, not just the mean, and the call is yours. See ship-or-hold.thml and ship-or-hold.thml.

Diagnostics (never errors) flag the gaps: an outcome with no payoff, a leads-to with no probability and no derived confidence, mixed dimensions, or an authored probability mass over 1.

Sensitivity (leverage)

--sensitivity. How load-bearing is each piece of evidence? For each evidence edge e into target T:

leverage(e) = derived(T) − derived_without_e(T)

It recomputes the target’s derived confidence with that one edge removed (a target left with no evidence falls to the neutral 0.5), and records the difference. Positive leverage means e props the target up (a support); negative means it drags it down (an attack); the magnitude is how much the conclusion rests on that single edge.

leverage is set on each evidence link. The bundled ship-or-hold.thml ranks evidence by it. It is computed by re-deriving the graph with each edge ablated in turn — single-edge sensitivity, precomputed for every edge at once — so the CLI never perturbs the document; it reports it as authored.