Conceptual modelling
Conceptual modelling involves creating a graphical representation (or model) of real-world systems. In collaborative problem-solving, it provides an accessible way for stakeholders to understand and engage with a system. Developing a conceptual model requires decisions about the model’s scope and level of detail, which should ideally be agreed upon by the modeller and stakeholders, who rely on the model for decision-making. Assumptions must also be made about the situation, determining which aspects to include or exclude and at what level of detail. Conceptual models are often non-software-specific descriptions, outlining objectives, inputs, outputs, content, assumptions, and simplifications. In this broader sense, facilitators may use conceptual models to help stakeholder groups better understand their context.
Conceptual models can also serve as a foundation for participatory or collaborative modeling efforts, fostering a shared language that supports innovative planning and evaluation.
Related approaches, such as systems thinking, systemic design and systems thinking tools enhance this process. Conceptual models have many uses in collaborative decision-making, including supporting the development of detailed computer models and decision-support systems. The resources below explore these applications.
Conceptual models – What are they and how can you use them?
This 2017 post by Andrew Powell-Morse provides a good introduction to these models. He explores what conceptual models are, how they are most commonly implemented, and touches on a few advantages and disadvantages of using conceptual models in the realm of software development.
Conceptual modelling: Knowledge acquisition and model abstraction
In this paper, Kathy Kotiadis and Stewart Robinson examine the processes of knowledge acquisition and model abstraction in conceptual modelling. Knowledge acquisition focuses on understanding the problem and creating a system description, while model abstraction simplifies this description into a conceptual model. The paper also references See also the related systems thinking tools page which provides more detail on the use of rich pictures and SSM.
Choosing the right model: conceptual modeling for simulation
Stewart Robinson explores the balance between complexity and simplicity in simulation models. Too much detail can make a model unmanageable, while too little can compromise its accuracy. Using an example from healthcare, Robinson defines conceptual modelling artefacts and outlines a framework to guide modellers in determining what to include or exclude.
Conceptual modelling: framework, principles, and future research
Roger Brooks discusses the challenges of conceptual modelling, often seen as more art than science. He provides a framework and suggests a creative optimisation process for developing models. The paper outlines 17 principles of conceptual modelling, offering practical advice for choosing the best model for a project and predicting its performance throughout.
Conceptual modelling is also integral to developing indicators for sustainable and performance-based management. For guidance on developing programme-based outcomes or intervention logic models, visit the related page on developing programme-based outcomes models. Additionally, see pages on systems thinking, systemic design and systems thinking tools for more resources on problem sructuring and framing.
