Kempermann Gerd
German Center for Neurodegenerative Diseases (DZNE) Dresden, Dresden, Germany.
Center for Regenerative Therapies Dresden (CRTD), Technische Universität Dresden, Dresden, Germany.
Front Neurosci. 2017 Nov 14;11:634. doi: 10.3389/fnins.2017.00634. eCollection 2017.
The Cynefin scheme is a concept of knowledge management, originally devised to support decision making in management, but more generally applicable to situations, in which complexity challenges the quality of insight, prediction, and decision. Despite the fact that life itself, and especially the brain and its diseases, are complex to the extent that complexity could be considered their cardinal feature, complex problems in biomedicine are often treated as if they were actually not more than the complicated sum of solvable sub-problems. Because of the emergent properties of complex contexts this is not correct. With a set of clear criteria Cynefin helps to set apart complex problems from "simple/obvious," "complicated," "chaotic," and "disordered" contexts in order to avoid misinterpreting the relevant causality structures. The distinction comes with the insight, which specific kind of knowledge is possible in each of these categories and what are the consequences for resulting decisions and actions. From student's theses over the publication and grant writing process to research politics, misinterpretation of complexity can have problematic or even dangerous consequences, especially in clinical contexts. Conceptualization of problems within a straightforward reference language like Cynefin improves clarity and stringency within projects and facilitates communication and decision-making about them.
辛芬框架是一种知识管理概念,最初旨在支持管理决策,但更广泛地适用于复杂性对洞察力、预测和决策质量构成挑战的情况。尽管生命本身,尤其是大脑及其疾病,复杂到可以将复杂性视为其主要特征,但生物医学中的复杂问题往往被当作实际上只不过是可解决子问题的复杂总和来处理。由于复杂情境的涌现特性,这种做法是不正确的。辛芬框架通过一套明确的标准,有助于将复杂问题与“简单/明显”、“复杂”、“混乱”和“无序”情境区分开来,以避免误解相关的因果结构。这种区分带来了这样一种见解,即在这些类别中的每一类中可能存在哪种特定类型的知识,以及对最终决策和行动会产生什么后果。从学生论文到出版和资助申请过程,再到研究政策,对复杂性的误解可能会产生问题甚至危险的后果,尤其是在临床环境中。在像辛芬框架这样直接的参考语言中对问题进行概念化,可提高项目中的清晰度和严谨性,并促进有关这些问题的沟通和决策。