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The AXIOM approach for probabilistic and causal modeling with expert elicited inputs

机译：AXIOM方法用于概率和因果建模，并由专家提供输入

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摘要

Expert informants can be used as the principal information source in the modeling of socio-techno-economic systems or problems to support planning, foresight and decision-making. Such modeling is theory-driven, grounded in expert judgment and understanding, and can be contrasted with data-driven modeling approaches. Several families of approaches exist to enable expert elicited systems modeling with varying input information requirements and analytical ambitions.This paper proposes a novel modeling language and computational process, which combines aspects from various other approaches in an attempt to create a flexible and practical systems modeling approach based on expert elicitation. It is intended to have high fitness in modeling of systems that lack statistical data and exhibit low quantifiability of important system characteristics. AXIOM is positioned against Bayesian networks, cross-impact analysis, structural analysis, and morphological analysis. The modeling language and computational process are illustrated with a small example model. A software implementation is also presented.
机译：专家线人可以用作社会技术经济系统或问题建模中的主要信息源，以支持计划，远见和决策。这种建模是理论驱动的，基于专家的判断和理解，并且可以与数据驱动的建模方法进行对比。存在多种方法系列，以使专家能够根据变化的输入信息要求和分析野心进行系统建模。本文提出了一种新颖的建模语言和计算过程，该方法结合了其他各种方法的各个方面，以试图创建一种灵活而实用的系统建模方法基于专家的启发。它旨在在缺乏统计数据的系统建模中具有很高的适应性，并且对重要系统特征的可量化性较低。 AXIOM针对贝叶斯网络，交叉影响分析，结构分析和形态分析进行定位。用一个小的示例模型来说明建模语言和计算过程。还介绍了一种软件实现。

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