Drift (D)

1. Canonical Definition

Drift (D) is the rate at which interpretive inconsistency accumulates in a meaning system when stabilizers cannot remain proportionate to operating demands. In Meaning System Science, drift increases when truth fidelity (T), signal alignment (P), or structural coherence (C) are insufficient for the volume, variability, or complexity of interpretation, and it is commonly accelerated by Constraint Failure and Closure Failure.

2. Featured Lineage

Claude Shannon — A Mathematical Theory of Communication (1948)
Showed that reliability decreases as noise and load increase. MSS extends this by treating drift as the rate of accumulating inconsistency under rising demand.

Charles Perrow — Normal Accidents (1984)
Showed that complex systems accumulate interacting inconsistencies. MSS adapts this by modeling drift as a system property rather than individual error.

3. Plainly

Drift is how fast inconsistencies build up and remain unresolved. When drift rate rises, different roles reach different interpretations from the same inputs and correction falls behind.

4. Scientific Role in Meaning System Science

D provides the destabilizing dimension of MSS. It models inconsistency accumulation as a rate condition, enabling analysis of when proportional stability will remain viable and when correction capacity will fall behind demand.

5. Relationship to the Variables (T, P, C, D, A)

• T: Uneven or unverifiable baselines increase inconsistency accumulation.

• P: Competing cues multiply incompatible interpretation.

• C: Routing inconsistency and ownership ambiguity increase recurrence.

• D: Measures the net rate at which unresolved inconsistency accumulates.

• A: Lower bandwidth reduces correction throughput and increases tolerance for unresolved items.

6. Relationship to the Physics of Becoming

L = (T × P × C) / D

D is the denominator of the law. As D increases, legitimacy decreases unless stabilizers increase proportionately. Drift rate is often the most direct indicator of proportional instability.

7. Application in Transformation Science

Transformation Science uses D to model instability trajectories, identify when inconsistency accumulation will exceed correction capacity, and explain compounding effects that follow from changes in T, P, C, and A.

8. Application in Transformation Management

Practitioners monitor D through recurring mismatches, exception volume, unresolved backlogs, and cross-role disagreement, then reduce drift by strengthening stabilizers, regulators, and interface governance.

9. Example Failure Modes

• Exceptions increase because constraints are not enforceable across pathways.

• Contradictions remain open, so the same issues reappear across cycles.

• Updates lag, creating multiple active versions of the baseline.

• Competing signals direct different actions for the same condition.

10. Canonical Cross References

Meaning-System • Interpretation • Meaning System Science • Physics of Becoming • First Law of Moral Proportion • Legitimacy (L) • Truth Fidelity (T) • Signal Alignment (P) • Structural Coherence (C) • Affective Regulation (A) • Thermodynamics (Meaning-System) • Interface • Coupling • Meaning Topology • Drift Catalysts (β₆) • Coherence Regulators (γ₆) • Constraint Failure • Closure Failure • Meaning-System Governance • Transformation Science • Transformation Management • LDP-1.0 • 3E Standard™