Principles and models of expert-analytical methodology for adaptive organizational decisions forming under deep uncertainty

O. Illina, I. Sinitsyn, O.O. Slabospitska
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引用次数: 1

Abstract

The paper depicts and analyzes Expert-Analytical Methodology named EAM DMDU to support Proactive Anti-crisis Decisions within Organizational Systems under deep uncertainty. Complex tools are proposed for Decisions Domain Knowledge analytical operation. The Benefit is no essential resource demands while keeping the basic principles to deal with deep uncertainty (uncertainties and inconsisten- cies eliciting; Decision vulnerabilities searching instead prediction; threats resilience priority over effectiveness). EAM DMDU enables Deliberative multi-staged Process for Adaptive Decision forming aimed at expected future conflict situation solving. The Process stages are: Problem situation Analysis, Impact on Problem Situation Goal Proposition, Goal proposals Assessment; Efforts for Goal achieving Proposals; Effort Proposals Assessment; reference Proposal option Selection and Decision adaptations accordingly to Decision Frame changes Recommendation. Knowledge operation is enabled with the procedures such as: formal analysis, individual expert assessment, Decision elements deliberative forming. EAM DMDU common information space of is based on Domain Ontology and ensures equal participants’ awareness, expert judgments with their arguments constructive representation and knowledge reuse. Expert-analytical Selection of Proposals uses their Perspectivity Model. It is a sub-goals hierarchy to achieve the goal being formed over previous Process stages. Hierarchy knot is represented with ontologically formalized definition for State of the Art corresponding sub-goal achievement. Leaf node depicts State of the Art with explicit expert Estimates of Certainty factor (from the Stanford model) being provided concerning its implementation through Decision element Proposal being assessed. The Estimate’s arguments are elements of information space used by expert. Under incomplete certainty of element expert provides its boundary values and State of the Art estimates both pessimistic and optimistic. Perspectivity Model contains also conditions for goal achievement violation being caused with environmental threats. Procedures for Estimates formal integration up to Model provide extreme estimates of Proposals Perspectivity and Robustness regarding current uncertainty. Under unsatisfactory properties of integrated Estimates their deliberative adjustment is carried out using Uncertainty Map and arguments provided. The final reference Decision contains selected Goal-Means option and guides to adapt it when decision frame changes. Further research is carried out for EAM DMDU instrumental tools development and its usage for defense resource management.
深度不确定性下适应性组织决策的专家分析方法原理与模型
本文描述并分析了专家分析方法EAM DMDU,以支持组织系统在深度不确定性下的主动反危机决策。针对决策领域知识分析操作,提出了复杂的工具。在保持处理深层次不确定性(引起的不确定性和不一致性;决策漏洞搜索代替预测;威胁复原力优先于有效性)。EAM DMDU为自适应决策形成提供了多阶段审议过程,旨在解决预期的未来冲突情况。过程阶段为:问题情况分析、对问题情况的影响、目标提出、目标建议评估;实现目标建议的努力;工作建议评估;参考提案选项选择和决策适应相应的决策框架变化建议。通过形式分析、个别专家评估、决策要素协商形成等程序实现知识操作。EAM DMDU公共信息空间以领域本体为基础,保证了参与者的平等意识、专家判断和论证的建设性表达以及知识的重用。建议的专家分析选择使用他们的透视模型。它是一个子目标层次结构,用于实现在前一个Process阶段形成的目标。层次结是用本体形式化的定义来表示相应的子目标实现状态。叶子节点描述了通过评估决策元素提案提供的关于其实施的确定性因素(来自斯坦福模型)的明确专家估计的最新技术。评估的参数是专家使用的信息空间元素。在不完全确定性条件下,专家给出了其边界值和目前的悲观和乐观估计。透视模型还包含了环境威胁导致目标违背的条件。评估程序正式整合到模型提供提案的极端估计关于当前的不确定性的透视图和稳健性。在综合估计性质不理想的情况下,利用不确定性图和给出的论证对综合估计进行审慎调整。最终参考决策包含选定的目标-手段选项,并指导在决策框架更改时对其进行调整。进一步研究了EAM DMDU工具开发及其在国防资源管理中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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