In broad terms, conceptual modelling is the process of developing a graphical representation (or model) from the real world. The conceptual modeller has to determine what aspects of the real world to include, and exclude, from the model, and at what level of detail to model each aspect. These decisions should generally be a joint agreement between the modeller and the problem owners i.e. the stakeholders who require the model to aid decision-making. The process of conceptual modelling requires decisions to be taken regarding the scope and level of detail of the model. It also requires assumptions to be made about the situation concerned. The conceptual models talked about below can also be thought of as non-software specific descriptions of the situation under inquiry – describing the objectives, inputs, outputs, content, assumptions and appropriate simplifications required. In this wider sense those making conceptual models may be facilitators helping groups of stakeholders better understand their situation.
The first few papers below deal more generally with problem structuring, and then move on to material that looks more specifically at conceptual modelling. The related Learning for Sustainability pages on Theory of Change and logic models provide more detail about how these methods can be used to help stakeholders structure and model problems, and support subsequent planning and evaluation.
Special issue on problem structuring research and practice. This 2014 paper by Fran Ackerman and colleagues introduces a special journal issue – EURO Journal on Decision Processes – bringing together a collection of papers concerned with problem structuring research and practice. It explores a range of challenges facing the field of problem structuring, and then introduces each of the specific papers in the special issue.
Problem structuring methods in action. This 2004 paper by John Mingers and Jonathan Rosenhead provides a review and evaluation of the use of problem structuring methods (PSMs) in practice. It outlines the origins of PSMs, the type of problem situation for which they are suitable, and the characteristics of some leading methods. A number of issues in the application of PSMs are discussed, in particular an account of the debate about evaluation of the success of PSMs; the selection of an appropriate method; multimethodology; and a variety of aspects of the maintenance of relationships with the client organisation(s).
Research on conceptual modelling : less known knowns and more unknown unknowns, please. This 2015 paper by Jan Recker argues that the field has become entrenched in some “bad habits” that usually emerge in evolved paradigms and that we need to proactively pursue a dual research strategy incorporating new and different avenues that lead us to novel and impactful research contexts of conceptual modelling. He provides a framework that can guide this exploration and finishes with some recommendations about how conceptual modelling research programs could proceed.
Conceptual modelling: Knowledge acquisistion and model abstraction This paper by Kathy Kotiadis and Stewart Robinson looks at the processes involved in arriving at a conceptual model. This paper contributes to this understanding by discussing the artifacts of conceptual modelling and two specific conceptual modelling processes: knowledge acquisition and model abstraction. Knowledge acquisition is the process of finding out about the problem situation and arriving at a system description. Model abstraction refers to the simplifications made in moving from a system description to a conceptual model. Soft Systems Methodology has tools that can help a modeller with knowledge acquisition and model abstraction, including the use of Rich Pictures. The use of these tools is discussed with respect to a case study in health care.
Choosing the right model: conceptual modeling for simulation This paper by Stewart Robinson looks at what to include in the simulation model and what to exclude. The process of determining what to model is known as conceptual modeling. He acknowledges a balance in getting the right level of detail – make it too complex and it may not be possible to complete the model with the time and knowledge available, make it too simple and the results may not be sufficiently accurate. In this paper we explore conceptual modeling first with an illustrative example from a healthcare setting. Conceptual modeling, its artefacts and requirements are then defined. Finally, a framework for helping a modeler to determine the conceptual model is briefly outlined.
From conceptual model to simulation: A participatory process for development of a GIS-based decision support system for environmental management This paper by Martin Bunch addresses the development of the Cooum DSS, and focusses particularly on the link between the conceptual understanding of the Cooum system, and (1) representation of the system in the GIS database and (2) the operationalization of critical processes of the system logically and algebraically, such that the representation of the system in the GIS/DSS modules can be used to parametize a water quality simulation model. In was found that the process of developing a conceptual model, and attempting to represent this in the Cooum DSS has made the a significant GIS/EM4 – From conceptual model to simulation contribution to understanding the Cooum system and provided a much needed forum for open debate and exchange of information, in addition to developing a potentially useful tool for planners, managers and researchers in Chennai.
Conceptual modelling: framework, principles, and future research The conceptual modelling stage in a simulation project is very important and yet is still generally regarded as more of an art than a science. In this paper Roger Brooks sets out some of his views on conceptual modelling. The meaning and nature of conceptual modelling are discussed and a framework set out. The overall aim should be to choose the best model for the project and conceptual modelling can be viewed as a difficult optimisation problem that can be tackled effectively using a creative search process that develops alternative models and predicts their performance throughout the project. Based on advice from the literature and personal experience 17 principles of conceptual modelling are suggested.
Rich pictures and causal loop diagrams
There are a number of ways in which we can begin to outline the relationships between the elements of a system, or between systems. These tools may be as simple as a mindmap or rich picture, they may begin to include influences and feedback loops such as causal loop diagrams (CLDs), or they may begin to move into more quantitative approaches that seek to quantify the influences and relationships.
Rich pictures. This page from BetterEvaluation highlights how a rich picture is a way to explore, acknowledge and define a situation and express it through diagrams to create a preliminary mental model. A rich picture helps to open discussion and come to a broad, shared understanding of a situation. As this Open University page points out, rich pictures were a key part of Peter Checkland’s Soft Systems Methodology for gathering information about a complex situation. The idea of using drawings or pictures to think about issues is common to several problem solving or creative thinking methods (including therapy) because our intuitive consciousness communicates more easily in impressions and symbols than in words. Guidelines are provided in this page on diagramming for development . Rich pictures are often drawn at the pre-analysis stage, before you know clearly which parts of the situation should best be regarded as process and which as structure.
Causal Loop Diagrams: Little Known Analytical Tool. This short post by William Rushing highlights the causal loop diagram’s unique ability to identify and visually display intricate processes and root causes. Every system behaves in a particular manner due to the influences on it. Some of these influences can be changed, some cannot, and some can minimized. Causal loop diagrams bring out the systematic feedback in processes by showing how variable X affects variable Y and, in turn, how variable Y affects variable Z through a chain of causes and effects. With a CLD, a practitioner no longer needs to focus only on one interaction between two variables, but can focus on the entire system, along with its many variables and its many causes and effects. This short CLD introductory powerpoint from a University of Massachusetts course helps explain things further, and this short 2-page summary from Daniel Kim provides some simple guidelines for drawing causal loop diagrams .
An Interpretive Approach to Drawing Causal Loop Diagrams. This 2008 paper by Mostafa Jafari and colleagues reminds us that the complex interpretive nature of management problems makes it difficult to recognize all the existing causal loop relations. In this paper, they develop an interpretive approach to drawing causal loop diagrams assuming that there are different perceptions about same concepts and the analyst is closely engaged with finding most agreed causal relationships.
Related topics and information
Conceptual models also provide a useful starting point for participatory or collaborative modeling efforts. They help different stakeholder groups establish a common language that facilitates more innovative planning and evaluation . Developing conceptual models is also a key step in developing indicators for sustainable and performance-based management. Another related page in this section provides a number of links outlining how to develop programme-based outcomes models , also called intervention logic models.