Causal Loop Diagrams

• Causal loop diagrams address the core principle of systems thinking: One cannot understand an issue or its constitutive parts (factors, actors, processes) in isolation. In a system, everything is related to everything else. The relationships (and not the parts themselves) drive the outcomes and behaviors we want to understand. Without understanding these relationships, and if necessary modifying them, we cannot possibly change outcomes/behaviors in a lasting manner. However, it is not easy to identify and account for these relationships.
• In complex systems, cause-effect relationships are often separated by time and space, making connections obscure. Additionally, the sheer number of connections between causes and effects as well as across causes themselves challenge the abilities of normal language and human mind, both of which are more suited to account for limited number of relationships at a time. Yet, while each relationship is individually important, it is the collective impact these relationships have on a system that shapes the outcomes/behaviors we want to understand.
• By providing a snapshot of all relationships that matter on a single sheet, CLDs allow us to gain a “big picture” perspective on a problem; that is, we can see the processes through which different parts (factors, actors, and processes) interact to generate a problem, or how a problem interacts with its broader environment. This is the first step in adapting a systems perspective and avoiding the common analytic tendency to see things in isolation. It is important to note that CLDs represent a tool for continued system analysis, and not the end product of the effort itself. Ideally, developing a CLD that accurately portrays the system being studied will yield insights that further the analysis and deepen the user’s understanding of the relevant causes and effects.

When you want to model a dynamic system in a holistic manner.

• CLDs are used to conceptually model dynamic systems in a holistic manner, mapping how variables (i.e., factors, issues, processes) influence one another. We tend to think of issues in terms of simple, linear and independent cause/effect statements. This is partly because of the limited ability of language and the human mind to process interdependent series of complex cause-effect chains. CLDs offer a language that can capture and convey this complexity.

When data are not available to provide a precise characterization of a complex system.

• There are various qualitative methods to analyze a CLD to obtain critical insights about how a system works. For example, we can examine a CLD to uncover a system’s underlying “feedback structures,” which arise from interactions of factors, actors, and processes in a system over time. These structures may otherwise be difficult to identify as its parts may be separated by time and space. However, understanding feedback structures is critical as behavior and outcome patterns in a system are shaped and conditioned by them. This understanding enables us to differentiate between symptoms and root causes of problems and identify high and low leverage intervention points in a system. With such insights, we are better equipped to design effective strategies to engage with a system and anticipate as well as preempt unintended consequences. CLDs also show the natural constraints within the system, helping us develop more realistic expectations regarding our ability to bring about change.

If and when data are available, CLDs can be transformed into stock and flow diagrams, in which each variable is represented by an appropriate mathematical equation, and various changes in variables of interest can be simulated to see the net effects in a system.