Definition · audit & governance
Reproducibility
Reproducibility is the ability to regenerate an identical result from the same inputs and method, a precondition for audit and verification. For reproducibility, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Also known as reproducible results, computational reproducibility
Why it matters
Understanding reproducibility matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Reproducibility means the same question returns the same number every time it's asked. Pluvo computes figures deterministically from source rather than generating them, so an answer can be rerun and will reconcile—unlike an estimate that drifts between runs.
In practice
Governance example
Teams use reproducibility when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.
Pluvo example
Reproducibility means the same question returns the same number every time it's asked. Pluvo computes figures deterministically from source rather than generating them, so an answer can be rerun and will reconcile—unlike an estimate that drifts between runs.
In practice, teams should define reproducibility with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding reproducibility matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Reproducibility means the same question returns the same number every time it's asked. Pluvo computes figures deterministically from source rather than generating them, so an answer can be rerun and will reconcile—unlike an estimate that drifts between runs.
A strong workflow for reproducibility separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Reproducibility means the same question returns the same number every time it's asked. Pluvo computes figures deterministically from source rather than generating them, so an answer can be rerun and will reconcile—unlike an estimate that drifts between runs.
FAQ
What does reproducibility mean for financial reporting?
Reproducibility is the ability to regenerate an identical result from the same inputs and method, a precondition for audit and verification. For reproducibility, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Why can't probabilistic AI guarantee reproducible numbers?
Understanding reproducibility matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Reproducibility means the same question returns the same number every time it's asked. Pluvo computes figures deterministically from source rather than generating them, so an answer can be rerun and will reconcile—unlike an estimate that drifts between runs.
Sources
- Costs and Benefits of Reproducibility in Finance and Economics Harvard Data Science Reviewhdsr.mitpress.mit.edu
- Reproducibility Wikipedia https://en.wikipedia.org › wiki › Reproducibilityen.wikipedia.org
- Summary - Reproducibility and Replicability in Science - NCBI - NIHNCBI - NIHhttps://www.ncbi.nlm.nih.gov › books ›ncbi.nlm.nih.gov