发表机构
German Institute for Adult Education – Leibniz Institute for Lifelong Learning（德国成人教育研究所——莱布尼兹终身学习研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出元理论多重宇宙分析（MMA），将理论编码为命题逻辑并生成多重宇宙，通过元分析度量不确定性成因，以提升研究可靠性，并用模拟和软件验证。
AI 中文摘要
本文提出了一种测量、分析和降低理论不确定性的方法，我们称之为元理论多重宇宙分析（MMA）。该方法之所以重要，是因为对某一现象进行科学观测和检验的可靠性与可重复性是理论不确定性的函数，因此降低理论不确定性是改进理论的一种方法。MMA方法要求将理论编码为变量（节点）及其相互关系（边）之间的命题逻辑。在我们的方法中，命题逻辑模型被视为因果路径模型，这使得能够对其因果命题背后的检验和识别进行数学性质分析。通过将理论作为数据（包括其未知成分）进行编码，可以生成一个由多种可能合理的理论模型组成的多重宇宙，然后利用（统计）多重宇宙分析中借鉴的技术对其进行元分析。我们引入了用于研究理论多重宇宙的度量指标，这些指标能够测量和识别元理论不确定性的成因。这为研究者指明了在何处投入理论发展以降低不确定性最为有效。我们通过三个模拟实验和一个专用软件包来演示该方法。
英文摘要
Here we present a method for measuring, analyzing and reducing theoretical uncertainty. We call it metatheoretical multiverse analysis (MMA). The method is important because the reliability and replicability of scientific observation and testing of a phenomenon are a function of theoretical uncertainty thus reducing it is a method for improving theory. The MMA method requires encoding theory as propositional logic between variables (nodes) and their relationships with one another (edges). Propositional logic models are treated as causal path models in our method, which enable mathematical properties of testing and identification underlying their causal propositions. By encoding theories-as-data including their unknown components, it is possible to generate a multiverse of alternatively plausible theoretical models which can then be meta-analyzed using techniques borrowed from (statistical) multiverse analysis. We introduce metrics for studying theoretical multiverses that enable measuring and identifying the causes of metatheoretical uncertainty. This guides researchers to where it is best to invest theoretical development to reduce uncertainty. We use three simulations and a dedicated software package to demonstrate this method.
Comments34 Pages, 3 Figures, 2 Tables plus an Appendix
