Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate “what if” scenarios. We propose to realize this by deriving knowledge from experts’ mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM’s simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems.

中文翻译：

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细化因果循环图：最大化计算系统动力学建模领域专业知识贡献的教程。


复杂性科学和系统思维越来越被认为是研究生物学、心理学和社会环境因素相互作用的系统的相关范式。然而，系统思维的应用通常止于开发概念模型，该模型将系统内因果链接的映射可视化，例如因果循环图（CLD）。虽然这本身就是一个重要的贡献，但随后必须制定 CLD 的可计算版本，以便解释建模系统的动态并模拟“假设”场景。我们建议通过从生物心理社会领域专家的心理模型中获取知识来实现​​这一点。本文首先描述了在 CLD 中捕获专家知识所需的步骤，以便生成计算系统动力学模型 (SDM)。为此，我们向 CLD 引入了几个注释，以促进这种预期的转换。这种带注释的 CLD (aCLD) 包括证据来源、中间变量、因果关系的函数形式以及不确定和已知不存在的因果关系之间的区别。我们提出了一种用于开发包含这些注释的 aCLD 的算法。然后我们描述如何基于 aCLD 制定 SDM。所描述的此转换步骤有助于识别、量化和潜在地减少不确定性来源，并获得对 SDM 模拟结果的信心。我们利用一个运行示例来说明此转换过程的每个步骤。本文描述的系统方法促进和推进了计算科学方法在生物心理社会系统中的应用。