It takes time for an individual to be of-age to have a child which is why there is a delay between Population and Births. Hash marks represent a Delay, a situations where it takes time before the effect plays out. Notice in the image above that there are two hash marks, ||, on the causal links between Population and Births and between Population and Deaths. These two feedback loops can cause a few different behaviors based on the the birth rate and life expectancy – we will observe Population growing and growing ever faster as long as the reinforcing Births loop dominates, and leveling off if the Deaths balancing loop is dominant. We see a set of relationships that are always happening over and over again generating behavior that unfold over time. These types of loops are called Balancing Feedback Loops (marked with a B) since more leads to less or less leads to more – the original change is balanced by a change in the opposite direction.įeedback loops take on a life of their own. This is because more deaths today will cause the population to fall, which means less people will be around to die later. More Deaths today leads to fewer Deaths in the future. We see a different type of Feedback Loop when we examine Deaths. If this were the only Feedback Loop in the Population system and people did not die, then we would see exponential growth in the number of people. This is called a Reinforcing Feedback Loop (marked with an R) because more births today lead to more births in the future – births reinforce births. Similarly, less Births would lead to a lower Population which would lead to less births in the future the reinforcing process works in the opposite direction too. More population leads to more births which leads to more population. So let’s read this one starting with Population and More. To read a feedback loop, you pick a variable to start with and arbitrarily pick a direction – either More or Less. A feedback loop is what we call a set of relationships where one variable leads to a change in another variable that eventually leads to a change in the original variable. This link forms our first feedback loop, shown on the left side of the image above. Reinforcing and Balancing Loops impact Population Therefore, we draw a positive causal link from Population back to Births. While more Births lead to a greater Population, a greater Population also leads to more Births since more people make more babies, given a birth rate stays constant (this is why we say “all else equal”, because we only consider the two variables that we are linking when we think about polarity). Now let’s introduce some Feedback into the model. Population Growth is a function of Births and Deaths As long as births exceed deaths population will grow, and whenever deaths exceed births, the population will shrink. The direction of change in population is determined by whichever of these two relationships is dominant. On their own, they don’t tell us what’s actually happening to the population. These causal links are true independently, and they are also both true at the same time. More deaths cause the population to decrease. We represent this by labeling the arrow head with a – sign. The variables move in the opposite direction, more leads to less, or less leads to more, so we would say that this relationship has a negative polarity. We also know that more deaths lead to a lower Population, and fewer deaths lead to a greater population. More births causes the population to increase. We indicate that a causal relationship has a positive polarity by placing a + sign next to the arrow head. We would say this relationship has a positive polarity, meaning that the two variables move in the same direction: more leads to more, or less leads to less. We know that more Births lead to a greater Population, and fewer Births will lead to a lower Population, all else equal. The two things that cause the Population to change are Births and Deaths, so we use arrows to represent these causal links. Let’s hop into our example to make this more concrete. This quick tutorial will teach you the basics about reading causal loop diagrams through a Population model.Ĭausal loop diagrams consist of variables (things, actions or feelings) connected by causal links (arrows) with polarities (+ and – signs) and delays (||). Together, these create positive and negative feedback loops that describe the circles of cause and effect that take on a life of their own. Systems thinking takes on complex, dynamic systems and how they behave over time, which calls for a different sort of language. Most of our posts include causal loop diagrams because some things are better expressed with a visual model than in words alone.
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