Skip to content →

The best principles of the systems science

The researchers from New York state questioned systems science not being able to solve the major challenges in public health. Promotion of public health and welfare often fails because of the complexity of the systems involved. According to Sterman, “What thwarts us is our lack of a meaningful systems thinking capability”.

However, there are principles that improve the modeling. The researchers successfully applied them to improve primary care screening in oral health. But they can be easily applied to other domains. Let’s consider four best principles proposed to enhance the modelling.

  1. Model problem, not the system

Modeling the system rather than the problem can cause confusion. For example, dental providers failed to screen their patients for hypertension and diabetes with evidence-based guidelines. But they have electronic devices at chairside to obtain web-based health information. There is a lack of coordination between dental hygienists and dentists. A clinical decision support system can improve the case. You can design an integrated system of oral care or focus on adhering dental hygienists to evidence-based hypertension and diabetes screening. The latter is more useful. So, it is better to define the problem and model it than to model a system.

  1. Mind importance, not countability

Numerical data are valuable, but they are not always available. That restricts the researchers. However, the qualitative statements can be used to derived the relationships. For example, conducting a survey about dental care you discovered that the peer network is important in recommending healthcare provider. The essence of this finding can be reflected on the circle diagram, where peer referral for health care provider turns into experience with this provider, trust and further peer referral. Such an approach opens multiple perspectives. Systems science is not limited to precisely measured constructs. Pay attention to what is important and not only quantifiable.

  1. Think about boundary objects

For the problem to be solved collaboratively the boundary objects should be paid special attention. These are diagrams, texts, or prototypes  that help different groups collaborate effectively.

The researches proposed a special framework. They took previously known Consolidated Framework for Implementation Research (CFIR). It is a generalized topology for multiple context. CFIR says what works, where and why. In current paper the framework was simplified. It includes the following sections:

“the intervention, the inner and outer settings, the individuals involved, and the process by which implementation is accomplished”.

The authors applied simplified CFIR for several models and found it to be effective. It facilitated team collaboration.

  1. Embrace portfolio approach

It is OK to explore several ‘flawed’ models than a perfect one. Let them be developed in parallel. The portfolio can incorporate different methods of systems science. These are system dynamics, agent-based modeling, geographic approaches, social network simulations, and others. Portfolio of models is especially conducive to collaboration. Different members of the research team can bring their models. It stimulates the flexibility of the modeling process and leverages its iterative nature.

Amount of information humans can process in their heads is limited. To enhance thought experiments we need to model them on computers. Thus we can explore alternative assumptions, find new possibilities for the structure. Finally we learn how to change the structure to improve its behavior.

Published in news


Leave a Reply

Your email address will not be published. Required fields are marked *