STRUCTURAL EVALUATION FRAMEWORK

Decision Audit

Decision Audit is a structural evaluation framework for determining whether a decision is aligned, internally consistent, and convergent across multiple evidence layers prior to execution.

WHY DECISIONS FAIL

Data correctness does not equal decision readiness.

Most decision failures are not caused by bad intent or missing information. They are caused by structural problems — evidence layers that contradict each other, point toward incompatible outcomes, or fail to hold under pressure.

A decision can be built on accurate data and still be structurally compromised. When financial projections assume one timeline and operational constraints require another, the decision is broken at the structural level — regardless of how accurate each layer is in isolation.

Correct data + broken structure = failed execution.

WHY PREDICTION IS NOT DECISION

AI predicts outcomes. It does not audit structure.

Prediction models estimate what will happen. They optimize for accuracy against historical patterns. They do not evaluate whether the decision is structurally ready to be executed.

A prediction can tell you that a drug candidate has a 70% probability of Phase 3 success. It cannot tell you whether the evidence layers supporting the investment decision are internally consistent — whether the mechanistic rationale, the genetic population data, and the clinical trial design converge toward the same conclusion.

Prediction answers: what will happen?
Decision Audit answers: is this decision structurally ready?

HOW TAC SOLVES THIS

Target Alignment Criteria evaluates the structure, not the outcome.

TAC evaluates three structural dimensions across the evidence assembly of any decision:

A
Alignment
Do evidence layers support a coherent, unified objective?
T
Tension
Do contradictions exist across layers that undermine structural integrity?
C
Convergence
Does the structure resolve toward execution readiness across scenarios?