Abstract
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.
Highlights
We regard many, if not most important biological and medical questions as complex, because they do not have single straightforward solutions
There have been some applications to medicine (Mark, 2006; Sturmberg and Martin, 2008; Van Beurden et al, 2013) but by and large, Cynefin has not yet had a measurable impact on life sciences. This might have cultural reasons and be partly due to the emphasis on decision making that Snowden’s original text had, but the tools from Cynefin to make sense of complexity appear to be highly useful for current biology and medicine as well
The reproducibility crisis, the conceptual and practical challenges of multi-omics and big data, the discussions around personalized medicine, the heritability gap in genome wide association studies, the failure of clinical trials for cancer and neurodegeneration after successful animal experiments and the tremendous difficulties in defining criteria for satisfying “mechanistic” explanations of a biological observation are all examples of struggles with complexity in biomedicine
Summary
If not most important biological and medical questions as complex, because they do not have single straightforward solutions. Life itself is complex and is more than the sum of its part: simple, direct causalities are only found within defined functional modules, such as chemical reactions and physical interactions (and even there can be challenged). Complex problems and questions in the life sciences have “emerging properties” that preclude that there are single true answers to questions of causalities. Neurodegeneration, consciousness, etc., cannot be comprehensively understood by adding up insights from partial aspects. They require a more holistic perspective, which, might be impossible to gain. While the large questions of life are the most obvious manifestations of a problem with complexity, the issue permeates biomedical research at every level
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