Process & Methodology
Uncertainty Quantification
A number without an uncertainty is a guess in a lab coat. Kronos quantifies its own.
Kronos propagates input uncertainties through its models with Monte-Carlo sampling, producing distributions and feasibility fractions rather than single deterministic values. Sensitivity (Sobol) and tornado analyses rank which assumptions the result actually depends on.
Honest error barsReporting a feasibility fraction — the share of sampled cases that still close — is more honest than a single point, and it is deposited alongside the point estimate.
Common questions
What is uncertainty quantification?
Carrying the uncertainty in inputs and models through to the results, so a figure comes with a credible range rather than false precision.
Why does it matter for credibility?
Because honest ranges and named assumptions are more trustworthy than a single confident number — consistent with Kronos's honesty framework.