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    面向复杂决策的OODA环:智能赋能与认知增强

    OODA Ring Theory for Complex Decision-making:Intelligent Empowerment and Cognitive Enhancement

    • 摘要: 传统OODA 环理论存在认知粒度低、单向循环、单一入口等缺点,无法满足未来以智能化、网络化、体系化为特征的多域联合作战需求,尚不具备在以跨域协同、体系聚优等为代表的复杂决策环境中应用的能力。通过梳理分析OODA 环理论发展历程及演进路线,确定了面向复杂决策的OODA 环理论的重点是突出认知环节与决策环节在整个OODA 环中的作用;通过分析智能技术的赋能方式,构建了智能态势认知与智能复杂决策框架,并将这两个框架嵌入认知粒度提高的OODA 环,形成面向复杂决策的智能CT-OODA 环理论;基于Cynefin 理论说明了复杂决策问题的环境分类方法,阐述了在不同复杂程度决策环境下智能CT-OODA 环的运行方式,以及如何通过OODA 的决策循环实现决策环境改变的动力学;提出了面向复杂决策的智能OODA及其具体结构和运行方式,并且提出了分类复杂决策环境的方法,为未来人机融合态势认知与复杂决策提供参考。

       

      Abstract: The traditional OODA(observation-orientation-decision-action)ring theory has such shortcomings as low cognitive granularity,one-way circulation, and single entrance, etc. and can't meet the multi-domain joint combat requirements with intelligent, networked,systematic features and isn't capable of applying in the complex decision-making environment represented by multi-domain coordination, systematic optimization, etc. Based on the sorting out and analysis of the development process and the evolution route of OODA ring theory, the focus of OODA ring theory for complex decision-making is determined to highlight the role of cognitive links and decision-making links in the whole OODA ring. By analyzing the empowerment mode of intelligent technology, the intelligent situation cognition and intelligent complex decision-making framework are constructed, and these two frameworks are embedded in the OODA ring with increased cognitive granularity to form an intelligent CT-OODA ring theory for complex decision-making. Based on Cynefin theory, the environmental classification method of complex decision-making problems is explained, and the operation mode of intelligent CT-OODA rings in decision-making environments of different complexity is elaborated, and how the dynamics of decision-making environment change are realized through OODA decision-making circulations. An intelligent OODA for complex decision-making and its specific structure and operation mode are proposed,and a method for classifying complex decision-making environments is proposed, to provide reference for future human-machine fusion situation cognition and complex decision-making.

       

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