Dealing with soft variables and data scarcity: lessons learnt from quantification in a participatory system dynamics modelling process

IF 1.7 3区 管理学 Q3 MANAGEMENT
Irene Pluchinotta, Ke Zhou, Nici Zimmermann
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引用次数: 0

Abstract

System dynamics (SD) models are commonly used for structuring complex problems to support decision‐making. They are used to investigate areas in which limited knowledge is available, describing nonlinear relationships and including intangible elements. Although this explorative nature is one of the key advantages, it also represents a challenge for quantifying the intangible, i.e. more qualitative aspects of an SD model, especially when it is not possible to apply conventional analytical methods due to data scarcity. Procedures to obtain and analyse information using participatory approaches are limited. First, this article outlines existing quantification methods and related open questions when dealing with soft variables and data scarcity. Secondly, it summarises the quantification process developed during a participatory SD process, describing how we dealt with data scarcity and soft variables. Lastly, we suggest a quantification framework in relation to data availability and level of stakeholder engagement. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.
处理软变量和数据稀缺问题:从参与式系统动力学建模过程中的量化工作中汲取的经验教训
系统动力学(SD)模型通常用于构建复杂问题的结构以支持决策。它们用于研究知识有限的领域,描述非线性关系,并包括无形要素。尽管这种探索性是其主要优势之一,但它也对量化 SD 模型的无形要素,即更多的定性要素提出了挑战,尤其是在由于数据匮乏而无法采用传统分析方法的情况下。利用参与式方法获取和分析信息的程序十分有限。首先,本文概述了处理软变量和数据稀缺问题时的现有量化方法和相关开放性问题。其次,本文总结了在参与式可持续发展过程中开发的量化程序,描述了我们如何处理数据稀缺和软变量问题。最后,我们提出了一个与数据可用性和利益相关者参与程度相关的量化框架。© 2024 作者。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学学会出版。
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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来源期刊
CiteScore
6.60
自引率
8.30%
发文量
22
期刊介绍: The System Dynamics Review exists to communicate to a wide audience advances in the application of the perspectives and methods of system dynamics to societal, technical, managerial, and environmental problems. The Review publishes: advances in mathematical modelling and computer simulation of dynamic feedback systems; advances in methods of policy analysis based on information feedback and circular causality; generic structures (dynamic feedback systems that support particular widely applicable behavioural insights); system dynamics contributions to theory building in the social and natural sciences; policy studies and debate emphasizing the role of feedback and circular causality in problem behaviour.
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