[MUSIC] Hello, my name is Ligia Paina, and today I'm going to be presenting about system conceptualization and mapping, specifically to give you an introduction to causal loop diagrams. And I'd like to acknowledge professor David Bishai from Johns Hopkins University School Public Health, and also professor Agnes Rashana, from the Micro University College of Computing and Information Sciences for their collaboration on this lecture. So today the lecture will be divided into three parts. First, we'll be talking about various definitions and the purpose of system conceptualization and also causal loop diagrams. In the second part, we'll be talking about the basic components of cause loop diagrams. And we'll conclude by giving some examples of applications, and also by discussing the strengths and weaknesses of this tool. We'll start by talking about the definitions and the purpose of causal loop diagrams. You'll remember this diagram from an earlier lecture that describes the steps in the model building process. And conceptualizing the system is one of the first things that one might do in this iterative process. And the conceptualization is one of the very important areas, although it is one that is perhaps nebulas. John Sterman describes conceptualization at once the most important and least understood of all modeling activities. And often, this process is vaguely described in current articles. But the main elements of conceptualization mapping are establishing the focus of the study. Drawing boundaries around the problem definition. Discussing the initial time horizon. And really, as Randers put it, deciding what part of reality to study and how to describe it. For system conceptualization and mapping there are various tools that have been used in the field, and these include, stack and flow diagrams, causal loop diagrams, some people also use conceptual frameworks and flow charts. Today however, we will be talking about one of those tools, and that is the causal loop diagram. And it's often referred to as a systems thinking diagram or an influence diagram. There are several reasons why one would draw a causal loop diagram. And mainly because it's one of the system dynamics methods that can help you develop a qualitative conceptualization of a system. The main focus of the causal diagram is to really make explicit the assumptions that one has internally, and to describe through a visual representation a mental model that can help you document assumptions, and help share this mental model with collaborators and other stakeholders. As mentioned, it can bring together different perspectives, especially if it is done in a participatory way. And it can help synthesize data from different disciplines with a common language that we'll describe in later sections today. The process of developing a causal loop diagram can also facilitate consensus building, engaging various stakeholders, and brainstorming around a particular idea, debating assumptions, and components of a system. And it is a first representation of a structure where feedbacks exist. So it is often used for activities to answer research questions, where feedback is hypothesized. Often times causal loop diagrams are also used to identify data gaps that need to be filled. Especially in contacts where data might not be available to fully define the initial mental model. And what's important about the causal loop diagram process is to understand that it is a qualitative mental model, and a qualitative representation of the system, and therefore it's primary purpose is communication and definition and not simulation. And often times the diagram itself is less relevant than the process of developing it, as I mentioned because the participatory engagement is really important, and can really add value more so than the diagram itself. In the research process, if we think about research planning, which includes a problem definition, a research question, some sort of implementation of a study, and evaluation of eventual policy analysis, there are several points where the causal diagramming process can begin. In the initial research planning, for example, a causal loop diagram can help define the research question and draw boundaries around the system. As I mentioned before, it can also identify areas where more data is needed, or perhaps the relationships are not well defined. And it serves as a key opportunity to begin engaging stakeholders, and we'll see that, that is really a theme across all of the various parts. While monitoring and implementing an intervention, the casual loop diagram can serve as a platform to both document and revise assumptions about how when intervention is affecting the system and interacting, adapting. In evaluation, it can serve as a platform for interpreting results, and again, for engaging stakeholders to really understand how the system works. And finally, when it comes to policy analysis, it can serve as a communication tool, and also as a foundation for developing a quantitative system dynamic model, which will be described in subsequent lectures. This concludes our initial discussion about definitions and purposes of cause loop diagrams, and their role in system conceptualization and mapping. And in the next section, we'll learn more about the basic components of the causal loop diagram, and how to actually think about reading it and building one yourselves.