Change CADE only when evidence justifies changing the system

GovernanceEvidenceCADE
Why this decision matters

It records how the NextGen Wargame system is governed.

This decision is part of the operating model behind CADE and its supporting workflows. It explains not just what was chosen, but how judgment, source truth, and execution pressure shaped the choice.

See the linked project Jump to the case study that applies this decision in practice. See the method Review the broader governed AI approach that these decisions refine. See the archive Compare this record against the other product and governance decisions.

Context

A reusable training framework needs stability. If every execution creates ad hoc changes, the core training effect erodes and future adopters cannot tell which parts are canonical.

Decision

Treat structural change as controlled evolution: update CADE only when live evidence, better training research, or stakeholder requirements justify a specific change.

Alternatives Considered

Continuously revise the framework after every preference or comment

Pros
  • Highly responsive
  • Captures many ideas quickly
Cons
  • Creates churn
  • Weakens the baseline
  • Makes the training effect harder to preserve

Freeze the framework completely

Pros
  • Maximum stability
  • Easy to teach and repeat
Cons
  • Ignores execution evidence
  • Prevents improvement when real failure modes appear

Reasoning

CADE needs deliberate stability, not stagnation. Evidence-driven change preserves the framework while allowing it to mature from what actually happens in execution.

AI Operator Skill Demonstrated

Using evidence to govern iteration instead of optimizing for polish

This decision keeps CADE from becoming a pile of one-off improvements. The point is to improve the system without losing the system.

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