 Systems mapping is a type of modeling that is designed to reveal the underlying interrelationships and structure of an organization. This is beneficial as it helps us to create a shared overall model of the system. Likewise, it helps us to start to understand how a system structure creates the observable outcomes. When one looks at most systems, be that in education, finance, energy systems, or politics, one will observe that there is limited understanding or consensus about the overall system. Never mind an understanding of how the system's underlying structure creates the observable outcomes. Typically, when someone is asked to describe the system they operate in, they will create a list of the elements without an understanding of how they interrelate to form the whole. Healthcare management will describe the nation's healthcare as a system, but when asked to describe that system, they will simply give a list of the elements. Hospitals, doctors, policymakers, insurance, etc. Without any real consensus between members about that list. When asked what the problem with the system is, they will point to one of the parts, with different members in the overall system pointing to different parts. The inevitable outcome of this is incoherence and lack of capacity to take coordinated efforts for the benefit of the end user. If we want coordinated action, we need some shared understanding of what the system is. And how it is operating. And this is what systems mapping can give us. Put simply, system maps are powerful visualization tools that can help change agents, describe and diagnose the current state of a given system, create a shared vision of it, get consensus about the problems, and identify opportunities. Of course, if we want shared consensus about the state of the system, then the mapping has to be a collective action created by those different parties involved. We have published a whole book on system mapping that goes in depth on the topic, so we will just touch upon it here to get an idea for its benefits and its importance. A systems map first consists of defining the elements in the system. We start by asking what are the key components? In ecosystems, this may be the different creatures. In a financial system, the different institutions. Or, for example, in a supply chain, the different producers, suppliers, transporters, etc. We now map the relations between those elements. These are causal relations. So these relations might be that of influence, a financial exchange or a physical exchange, such as the exchange of some resources, like water. A cause and effect relationship in a systems map is represented by an arrow between two variables. By variable, we mean anything that can change over time. The arrow goes from the cause variable, the one causing the change, to the effect variable, the one being affected by the change. There are two types of cause and effect relationships in CLDs, positive and negative. A positive relationship is depicted by the plus symbol at the arrow's head, whereas a negative relationship is depicted by the minus symbol at the arrow's head. A positive relationship means that the cause and effect variable is moving in the same direction, such that an increase in the cause variable results in an increase in the effect variable, and a decrease in the cause variable results in a decrease in the effect variable, all else equal. An example of a positive relation is births to population. This is a positive relationship because an increase in births will mean that the population will be larger than it would be if the births had an increase. Additionally, a decrease in births will mean that the population will be smaller than what it would otherwise have been. Conversely, in a negative relationship, the effect variable moves in the opposite direction to the cause variable. So, in this case, an increase in the cause results in a decrease in the effect, and a decrease in the cause increases the effect, all else equal. For example, if the price of grapes increases, then demand of grapes will decrease. Due to some assumed price sensitivity of people who buy grapes, we are also saying that if the price of grapes decreases, then the demand for grapes will increase. This is a negative relationship. When we follow a chain of cause and effect, we might often end up back where we started. Our last variable feeds back to the original cause. This means that we have discovered a feedback loop in the system. A feedback loop is a circular chain of cause and effect. So when A affects B, B affects C, and C affects A again, we say that a feedback loop exists between these variables. Just as there are only two types of causal relationships, positive or negative, there are also only two types of feedback loops, reinforcing or balancing. An example of a reinforcing feedback loop is one of births and population. The positive relationship from births to population also works the other way around. The higher the population, the more births there will be, all else equal. Therefore, an increase in population will increase births, and so a further increase in population. The initial increase in population is reinforced by this feedback loop structure in the system. Another example of this would be the positive feedback loop driving climate change, which can be identified as such. When Arctic ice melts, land or open water takes its place. Both land and open water are on average less reflective than ice, and thus absorb more solar radiation. This causes more warming, which in turn causes more melting, and this cycle continues. The other type of feedback loop that exists is a balancing negative loop. Wills to reinforcing loops generally cause greater change within the system, balancing loops generally do the opposite. They prevent change and create greater stability. An example of a balancing feedback loop is that of deaths and population. When a population increases, the total number of deaths also increases, all else equal. With an increase in deaths, the population in turn decreases. An increase in population is thus balanced by an increase in deaths. Conversely, a decrease in population will be partially compensated for by a decrease in deaths. Negative feedback loops are used as control and regulatory mechanisms, like an athermostat. Whenever there is a desired state in a system, then there will almost always be a balancing loop that describes how that goal is achieved. For example, when the temperature in an apartment is lower than the desired temperature, then there is a temperature gap. The heating system will sense this via a thermostat and will generate heat, such that the temperature gap is eventually reduced to zero, i.e. such that the temperature equals the desired temperature. If the temperature drops below the desired temperature again, then more heat will be generated until they are again equal. This is a balancing loop. To perform a more detailed quantitative analysis, a causal loop diagram is transformed into a stock and flow diagram, which helps in studying and analyzing the system quantitatively, typically through the use of computer simulations. A stock is a term for any entity that accumulates or depletes over time. A flow, in the contrary, is the rate of change in a stock. An example of a stock might be a water reservoir. It is a store of water, and we can ascribe a value to the volume it contains. Now if we put an outlet on the side of our reservoir and started pouring water out of it, this would be an example of a flow. Whereas a stock variable is a measure of some quantity, a flow variable is measured as a rate of exchange over time. By using these tools of system dynamics, we may get a qualitative and or quantitative idea of how a system of interest is likely to develop over time. For example, if we create a simple two-dimensional graph with time on the horizontal axis, we'll see how the different feedback loops create different types of graphs. Graphs for positive feedback loops typically reveal an initial exponential growth as they shoot upwards rapidly, but then reach some environmental boundary conditions where they crash back down again. A financial bubble and ensuing crash would be an example of this. Whereas the net result of a negative feedback loop will be a wave-like graph that will likely be bounded within an upper and lower limit over a prolonged period. With relatively smooth fluctuations during the system's development that enable it to sustain an overall stable state in the long term. System mapping is a powerful tool for creating a shared vision of what the system we are dealing with looks like, but it has many side benefits such as enabling us to locate root causes to systemic problems, identify the system's constituent elements, assess patterns of interaction within a system, deepen our understanding of the complexity behind most systems' failures, serve as a visual guide to understand interaction and relationships, discuss how to identify critical levers for change within a system, generate collective intelligence, find patterns and pathways that lead towards action. System dynamics is essentially just a technique. However, the use of this technique can radically transform how one approaches problems and even how one understands the world.