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AI & Foundations

Simulation Governance

The organizational practices, roles, and standards that decide when a simulation may be relied on, keeping trust a process rather than a habit.

Beyond the Individual Result

Simulation governance is the set of managerial and technical practices that control the quality of an organization's computational predictions: the standards a code must meet, the evidence required before a result is relied on, and who is accountable. It shifts trust from the confidence of individuals to a defined, auditable process, so reliability does not depend on who happened to run the model.

What Governance Covers

Kronos motion — when

Credibility Proportional to Stakes

Not every result needs the same rigor. Governance sets the level of evidence to the consequences: an exploratory sweep may need only sanity checks, while a result informing a safety-relevant decision needs full verification, validation, and uncertainty quantification. Matching effort to stakes, rather than treating all results alike, is central to governance.

Why Organizations Need It

Without governance, quality depends on the diligence of whoever ran the model, and standards drift as people come and go. A single unchecked result presented alongside rigorous ones erodes trust in all of them. Governance makes the required evidence explicit and consistent, so a claim's credibility can be judged by the process behind it, not the reputation in front of it.

At Kronos

Design results are produced under practices that keep verification, validation, and provenance as distinct, recorded ledgers, freeze results against specific code and inputs, and flag open reconciliations rather than burying them. This is simulation governance in practice: a defensible process behind every number, matched to what the number is used to claim.