Software Defined Corporation

Form 1 — Aggregate MFid (the canonical number)

MFidaggregate = (D × E × O × I)1/4. One number per system, vendor, process, or stack. This is the number that goes on the board deck. It is always a geometric mean of four [0,1] dimension scores. Weights are fixed at 1/4 each. There are no per-engagement weight choices.

Form 2 — Domain-projected fidelity (MFid_app, MFid_net, …)

When a system exposes domain-specific service-level indicators (latency, throughput, reliability for an app; bandwidth, jitter, loss for a network), we compute a domain projection:

MFidapp = wL·L + wT·T + wR·R

where L, T, R ∈ [0,1] are fidelity scores per SLI (claimed/observed clipped to 1.0) and weights w sum to 1. Weights are not chosen per case. They are derived from the system’s own published SLO portfolio — if the operator weights latency at 50% of their SLO budget, we use 0.5. If no SLO portfolio exists, we use the default Tier-1 weighting (0.5 / 0.3 / 0.2 in order of business impact: response time, throughput, reliability) and publish the choice in the finding.

A domain projection is not the aggregate MFid. It is one input into one of the four dimensions. Roll-up:

• Determinism (D) ← tail dispersion of each L/T/R signal (p99/p50 ratio inverted and clipped).
• Efficiency (E) ← resource-bounded form of L and T (claimed cost-per-unit ÷ observed cost-per-unit).
• Observability (O) ← coverage of L/T/R signals: fraction of customer journeys for which a measurement exists.
• Intentionality (I) ← scored separately; see rubric below.

Every published MFid_app in our case studies is annotated with both forms going forward: the projection that motivated the finding, and the aggregate it rolled into.

D — Determinism

Definition: Same input, under stated conditions, produces the same observable output within a stated tolerance.

Measurement: D = 1 − min(1, σ / (μ × τ)) where σ is the standard deviation of the observable, μ is its mean, and τ is the published tolerance band (e.g. 10%). Computed per SLI; aggregated by minimum across SLIs (a system is only as deterministic as its worst-behaved indicator).

Worst-case binding: If a single tail event in the measurement window exceeded 2σ on a critical SLI, D is capped at 0.9 regardless of the formula above. We refuse to let a calm hour hide a panic minute.

E — Efficiency

Definition: Resource cost per unit of useful output, relative to the published or contracted cost.

Measurement: E = min(1, claimed_cost_per_unit / observed_cost_per_unit), computed in the natural unit of the system (cycles/token, watts/inference, dollars/transaction, joules/request, bytes/query). One unit per system, declared up front. E is a one-sided ratio: under-cost (better than claim) is clipped at 1.0 — we report it but it does not inflate the score under standard scoring. The exception is documented, multi-dimensional under-reporting; see When MFid exceeds 1.0 below.

O — Observability

Definition: Fraction of the customer-relevant behavior surface for which a current, queryable, retained measurement exists.

Measurement: O = (covered_SLIs / required_SLIs) × retention_factor × freshness_factor. Required SLIs are enumerated up front for each system from its specification (not from what is currently instrumented — that would let absence become a credit). Retention factor is 1.0 if telemetry is retained ≥ 30 days, scaled down otherwise. Freshness factor is 1.0 if the dashboard is queryable in < 60 seconds, scaled down otherwise. A score you have to mine from log files is not observability; it is archaeology.

I — Intentionality

This is the dimension the 2026-05-26 review flagged as undefined. The review was correct. The rubric below is the answer.

Definition: The fraction of system activity that demonstrably serves the stated purpose under audit, with the remainder classified as out-of-spec drift (not necessarily harmful — but not what was contracted).

Important framing. Intentionality is not a property of the artifact in isolation; it is a property of the artifact relative to its specification. We measure the specification as carefully as we measure the system. An unclear spec produces a low Intentionality ceiling, not a low Intentionality score — we publish the ceiling and recommend the spec be tightened.

Measurement (three-part, each scored [0,1], aggregated by geometric mean):

1. I_spec — Specification clarity. Does a written, dated, signed specification exist that enumerates required behaviors and forbidden behaviors? Scored by document analysis on a 7-point checklist (existence, dating, scope, behavior list, forbidden-list, change log, sign-off). Reproducible across reviewers with κ ≥ 0.7 on a 50-spec calibration set; calibration set published on request.
2. I_trace — Operational coverage. Of the operations the system performed during the measurement window, what fraction can be traced to a specified behavior? Computed as (traced_operations / total_operations) from logs, traces, or transaction records. Operations with no trace match are not assumed malicious — they are assumed unscored, and counted against I_trace.
3. I_drift — Forbidden-behavior detection. Of operations classifiable as “outside spec” (output schemas, data destinations, decision boundaries the spec excludes), what fraction were caught by automated guardrails before having effect? Computed as (blocked_violations / detected_violations). A system with no violations and no detection capability scores 0.5 (we cannot tell whether it is well-behaved or unmonitored).

I = (Ispec × Itrace × Idrift)1/3.

For ML and autonomous systems specifically: I_spec is scored against the model card and policy document. I_trace is computed from prompt/response logs against the policy classifier. I_drift measures jailbreak/red-team catch rate. A worked example on a public LLM endpoint is published in the MFid open-source repository as examples/intentionality_llm_worked.md.

What I is not. I is not a measure of whether the system is good. A perfectly-malicious system with a clear spec authorizing maliciousness scores I = 1.0. I measures fidelity to the spec, not the wisdom of the spec. That is by design. We score the gap between claim and reality; we do not score the claim itself. That is the customer’s job.

The canonical case: Porsche

Porsche publishes 0–60 times, quarter-mile figures, lap times, and power outputs that independent reviewers and owners consistently beat — not by a rounding error, but by a margin that is too regular to be noise. A 911 GT3 RS quoted at a Nürburgring time the factory already knows it can beat. A Taycan Turbo S rated at a 0–60 the launch-control software was tuned to exceed. The understatement is not accidental; it is policy. Porsche does not want a customer to discover their car underperforms the sticker. The sticker is a floor, not a target.

Applied to MFid: an audit of Porsche's published performance specifications against independently measured reality would find that E (efficiency — delivered performance per published rating), D (determinism — variance of the gap between claim and observation), and I (intentionality — systematic consistency of the understatement) all produce raw ratios above 1.0 before the clip is applied. Under standard scoring, all three are rounded down to 1.0 and the composite scores 1.0 — indistinguishable from a vendor who just barely met their word. That conflation is the problem.

The scoring rule for verified underreporters

The min(1, …) clip is lifted on any dimension where the following conditions are simultaneously met:

1. The raw ratio exceeds 1.0 on its own evidence. Observed performance is better than claimed, not merely equal.
2. The pattern is multi-instance. The gap is documented across at least three independent measurements or product lines — not a single favorable test.
3. The understatement is structural, not accidental. There is evidence the vendor knowingly set conservative specifications — internal communications, design posture, or a consistent historical record — rather than simply underestimating their own system.
4. No other dimension is below 0.9. A super-unity score on one axis cannot be used to redeem a deficiency on another. This rule preserves the core principle of the geometric mean: a chain is only as strong as its weakest link.

When all four conditions hold, each qualifying dimension's raw ratio is admitted without the clip. The aggregate geometric mean is then computed normally. A resulting score above 1.0 is published as-is, annotated with the designation Verified Underreporter and the specific dimensions that cleared the threshold.

What a score above 1.0 means — and does not mean

An MFid above 1.0 does not mean the system is perfect. It means the vendor's claims are structurally conservative relative to what they actually deliver. The organization tells you less than the truth in their favor. In a landscape where most published specifications are optimistic, aspirational, or outright wrong, a vendor that systematically underreports is a meaningful outlier. The score reflects that outlier status numerically rather than relegating it to a footnote.

The designation is rare by design. Verified Underreporter status is not awarded for beating a spec once, or for modest outperformance within normal manufacturing variance. It requires the kind of documented, intentional conservatism that an organization chooses as a brand posture and executes consistently. Most vendors will never qualify. The ones that do earn a score that cannot be confused with "barely passing."

The upper bound in practice is roughly 1.15 to 1.25 — constrained not by the formula but by how consistently any real-world organization can outperform its own stated claims across all four dimensions simultaneously. A published MFid above 1.25 would require extraordinary evidence and would be treated with the same scrutiny we apply to a vendor claiming 0.99 on a Tier-2 basis.