Jayasinghe Saroj
Department of Clinical Medicine, Faculty of Medicine, University of Colombo, Kynsey Road, Colombo 8, Sri Lanka.
Emerg Themes Epidemiol. 2011 Jan 20;8(1):2. doi: 10.1186/1742-7622-8-2.
The mechanistic interpretation of reality can be traced to the influential work by René Descartes and Sir Isaac Newton. Their theories were able to accurately predict most physical phenomena relating to motion, optics and gravity. This paradigm had at least three principles and approaches: reductionism, linearity and hierarchy. These ideas appear to have influenced social scientists and the discourse on population health. In contrast, Complexity Science takes a more holistic view of systems. It views natural systems as being 'open', with fuzzy borders, constantly adapting to cope with pressures from the environment. These are called Complex Adaptive Systems (CAS). The sub-systems within it lack stable hierarchies, and the roles of agency keep changing. The interactions with the environment and among sub-systems are non-linear interactions and lead to self-organisation and emergent properties. Theoretical frameworks such as epi+demos+cracy and the ecosocial approach to health have implicitly used some of these concepts of interacting dynamic sub-systems. Using Complexity Science we can view population health outcomes as an emergent property of CAS, which has numerous dynamic non-linear interactions among its interconnected sub-systems or agents. In order to appreciate these sub-systems and determinants, one should acquire a basic knowledge of diverse disciplines and interact with experts from different disciplines. Strategies to improve health should be multi-pronged, and take into account the diversity of actors, determinants and contexts. The dynamic nature of the system requires that the interventions are constantly monitored to provide early feedback to a flexible system that takes quick corrections.
对现实的机械论解释可以追溯到勒内·笛卡尔和艾萨克·牛顿爵士的有影响力的著作。他们的理论能够准确预测大多数与运动、光学和引力相关的物理现象。这种范式至少有三个原则和方法：还原论、线性和层级结构。这些观点似乎影响了社会科学家以及关于人口健康的论述。相比之下，复杂性科学对系统采取更整体的观点。它将自然系统视为“开放的”，具有模糊的边界，不断适应以应对来自环境的压力。这些被称为复杂适应系统（CAS）。其中的子系统缺乏稳定的层级结构，且主体的角色不断变化。与环境以及子系统之间的相互作用是非线性相互作用，并导致自组织和涌现特性。诸如“epi + demos + cracy”以及健康的生态社会方法等理论框架已经隐含地使用了一些这些相互作用的动态子系统的概念。运用复杂性科学，我们可以将人口健康结果视为复杂适应系统的一种涌现特性，该系统在其相互关联的子系统或主体之间存在众多动态非线性相互作用。为了理解这些子系统和决定因素，人们应该掌握不同学科的基础知识，并与来自不同学科的专家进行互动。改善健康的策略应该是多方面的，并考虑到行为者、决定因素和背景的多样性。系统的动态性质要求不断监测干预措施，以便为一个能迅速做出调整的灵活系统提供早期反馈。
