DAIS-10: A Doctrine Aligned Framework for Safety (Dominant) Decision Making Under Uncertainty

DAIS‑10 is a decision system built for situations where things are risky, unclear, or constantly changing. Instead of relying on simple probability cutoffs, it looks at different possible scenarios, how risk builds up, and how confidence should shrink when uncertainty grows. It uses safety‑first rules inspired by advanced risk‑management methods. We define seven basic principles, prove seven supporting results, and test the system on public datasets from finance, medicine, and sensors. Across all tests, DAIS‑10 reduces the chance of missing dangerous events by about 77–90% compared to normal threshold methods, even when the data shifts or becomes adversarial. It is flexible, works across many domains, follows nine certification rules, and can be used in autonomous systems, medical diagnosis, financial risk tools, and multi‑agent safety settings.

This paper represents only the introductory portion of the broader DAIS‑10 continuum. The complete technical development, extended proofs, simulations, and implementation artifacts are maintained in the public repository. Readers seeking the full framework, ongoing updates, and supplementary materials are encouraged to visit: https://github.com/usman19zafar/DAIS-10-Continuum. 

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