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Assessment of influence productivity in cognitive models
Artificial Intelligence Review ( IF 10.7 ) Pub Date : 2020-03-07 , DOI: 10.1007/s10462-020-09823-8
Alexander Tselykh , Vladislav Vasilev , Larisa Tselykh

This article proposes a new influence productivity assessment methodology that is a cognitive intelligence system for the scenario planning of control impacts (generation and choice) for systems that are represented by directed weighted signed graphs based on the algorithm of effective controls. The algorithm implements a control model that expresses the direction of development (growth) of the system. The algorithm is based on the spectral properties of the adjacency matrix of a graph representing the model of a socioeconomic system and does not impose any constraints on the directions of the edges or the sign and weight range on the edges. Scenarios are assessed based on their compliance with tactical and strategic goals according to the codirectionality degree of the response vector with respect to the base vector of the model. The base vector is the effective control vector without constraints on the controls under the conditions of adequate model operation. The new methodology has three distinctive features: (1) the scenario approach is implemented with respect to a set of controls, (2) this approach is applicable for models with heterogeneous factors and does not require preliminary aggregation of the primary model elements of the system; and (3) this approach has a clear formalization metric for the selecting and generating of a set of control impacts. The process does not require the decision maker to have special mathematical training.

中文翻译:

评估认知模型中的影响生产力

本文提出了一种新的影响生产力评估方法,它是一种认知智能系统,用于基于有效控制算法的有向加权带符号图表示的系统的控制影响(生成和选择)的情景规划。该算法实现了一个表达系统发展(成长)方向的控制模型。该算法基于表示社会经济系统模型的图的邻接矩阵的谱特性,并且不对边的方向或边上的符号和权重范围施加任何约束。根据响应向量相对于模型基向量的共向度,基于其对战术和战略目标的遵守情况对场景进行评估。基向量是在模型充分运行的条件下对控制没有约束的有效控制向量。新方法具有三个显着特点:(1)情景法是针对一组控制实施的,(2)这种方法适用于具有异质因素的模型,不需要对系统的主要模型元素进行初步聚合; (3) 这种方法对于选择和产生一组控制影响有明确的形式化度量。该过程不需要决策者接受特殊的数学培训。(1) 情景法是针对一组控制实施的, (2) 该方法适用于具有异质因素的模型,不需要对系统的主要模型元素进行初步聚合;(3) 这种方法对于选择和产生一组控制影响有明确的形式化度量。该过程不需要决策者接受特殊的数学培训。(1) 情景法是针对一组控制实施的, (2) 该方法适用于具有异质因素的模型,不需要对系统的主要模型元素进行初步聚合;(3) 这种方法对于选择和产生一组控制影响有明确的形式化度量。该过程不需要决策者接受特殊的数学培训。
更新日期:2020-03-07
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