文章基本信息

标题：Operator Driven Policy: Deriving Action From Data Using The Quadrant Enabled Delphi (QED) Method
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作者：Lilian Alessa ; Sean Moon ; David Griffith 等
期刊名称：Homeland Security Affairs
印刷版ISSN：1558-643X
出版年度：2018
卷号：2018
页码：1-26
语种：English
出版社：Naval Postgraduate School Center for Homeland Defense and Security
摘要：To close the gap in operator-driven policy for the homeland security enterprise, we argue for a bottom-up policy process that acknowledges operator knowledge and opinions. We propose a practical approach to enable policy-makers to incorporate operator knowledge and experience, or operator driven policy (ODP), into policy through the Quadrant Enabled Delphi (QED) approach. We set out the theoretical requirements for QED, based on cognitive science. Using the EARTh-X QED workshop as a case-study, we demonstrate the application of QED focused on emerging Arctic security threats, and highlight key lessons for applying QED. Finally, we recommend an appropriate operator-driven policy-making process that incorporates the QED approach as a bottom-up policy process.