DAIS-10: A Doctrine Aligned Framework for Safety (Dominant) Decision Making Under Uncertainty
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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. Good News is that Now DIAS10 is can be practically experienced at \"https://zulfr.com/app/\". Any free service zulfr provides is specially design for young researchers. It a pleasure to bring concepts to Life! Contact: info@zulfr.com
DAIS-10是一款专为存在风险、信息模糊或环境持续变化的场景打造的决策系统。其并未依赖简单的概率截断阈值,而是考量各类潜在场景、风险的累积过程,以及不确定性加剧时信心应如何衰减。该系统采用源自先进风险管理方法的安全优先规则。 本文定义了七项核心原则,推导了七条辅助结论,并基于金融、医学与传感器领域的公开数据集对该系统开展测试。在所有测试场景中,即便遭遇数据分布偏移或对抗性数据,DAIS-10相较常规阈值方法仍可将危险事件的漏检率降低约77%至90%。该系统具备灵活性,可适配多领域场景,遵循九项认证准则,可应用于自主系统、医学诊断、金融风险工具以及多智能体安全场景。 本文仅为更完整的DAIS-10体系的介绍部分。完整的技术开发内容、拓展证明、仿真实验以及实现资源均托管于公开代码仓库中。如需获取完整框架、持续更新内容与补充材料,敬请访问:https://github.com/usman19zafar/DAIS-10-Continuum。 好消息是,现在可通过"https://zulfr.com/app/"实际体验DAIS-10。Zulfr提供的所有免费服务均专为青年研究人员打造。将概念落地实为乐事!联系方式:info@zulfr.com




