Слова со стрелками, входящими и выходящими, представляют переменные или величины, значения которых меняются с течением времени, а связи представляют причинно-следственную связь между двумя переменными (т. Е. Они не представляют материальный поток). Ссылка, отмеченная знаком +, указывает на положительную связь, где увеличение причинной переменной приводит, при прочих равных, к увеличению переменной эффекта, или уменьшение причинной переменной, при прочих равных условиях, приводит к уменьшению переменной эффекта. Отмеченная ссылка — указывает на отрицательную связь, где увеличение причинной переменной приводит, при прочих равных, к уменьшению переменной эффекта, или уменьшение причинной переменной приводит, при прочих равных, к увеличению переменной эффекта. Можно сказать, что положительная причинно-следственная связь ведет к изменению в том же направлении, а противоположная связь может вести к изменению в противоположном направлении, т.е. если переменная, с которой начинается связь, увеличивается, другая переменная уменьшается и наоборот. наоборот.
СОДЕРЖАНИЕ
История
Положительные и отрицательные причинно-следственные связи
Пример
Арматурные и уравновешивающие петли
Чтобы определить, усиливает ли причинный цикл или уравновешивает, можно начать с предположения, например, «Переменная 1 увеличивается», и проследить за циклом. Цикл:
Или, другими словами:
Если в системе есть задержки (часто обозначаемые короткой линией через причинно-следственную связь), система может колебаться.
What is Causal Loop Diagram? (With Examples)
Causal loops diagrams (also known as system thinking diagrams) are used to display the behavior of cause and effect from a system’s standpoint. Fishbone diagrams may elicit the categories of causes that impact a problem. Causal loops show the interrelation causes and their effects. When finished you have a diagram of the positive and negative reinforcements which describe the system of behavior. The neat thing about causal loops is it is depersonalizing. People can point at the arrows in the loop that are reinforcing the problem instead of pointing at people. It becomes a model of system behaviors that create the outcome of the system.
A causal loop diagram (CLD) is a causal diagram that aids in visualizing how different variables in a system are interrelated. The diagram consists of a set of nodes and edges. Nodes represent the variables and edges are the links that represent a connection or a relation between the two variables. A link marked positive indicates a positive relationship and a link marked negative indicates a negative relation. A positive causal link means the two nodes change in the same direction, i.e. if the node in which the link starts decreases, the other node also decreases. Similarly, if the node in which the link starts increases, the other node increases as well. A negative causal link means the two nodes change in opposite directions, i.e. if the node in which the link starts increases, the other node decreases, and vice versa.
Cause Loop Diagram Example – Growth and Investment
A Growth and Underinvestment structure is simply an elaborated Limits to Growth structure where the growth inhibitor is part of another Balancing Loop with an external standard and some delay. The nasty thing about this structure is that the two balancing loops form a single reinforcing loop that inhibits growth.
Cause Loop Diagram Example – Pizza House
A new home delivery-focused pizzeria opens up in the neighborhood. At first, the demand is low, but the pizza’s quality is excellent, as well as the delivery times. After a while, the pizzeria gets noticed and is featured in a local online food blog. As a result, the demand for pizza rises sharply. But the pizzeria owners are reluctant to purchase more delivery capacity (pizza delivery vehicles and personnel) along with higher pizza production capacity (additional pizza ovens). That results in higher delivery times and a larger percentage of undercooked pizzas, in turn lowering the number of returning customers. As a result, the pressure for additional investment in both delivery and production capacity is eliminated. The pizzeria owners are happy that they held off on the additional investment.
Learn to Read Causal Loop Diagrams
Most of our posts include causal loop diagrams because some things are better expressed with a visual model than in words alone. Systems thinking takes on complex, dynamic systems and how they behave over time, which calls for a different sort of language. This quick tutorial will teach you the basics about reading causal loop diagrams through a Population model.
There are 3 key reasons why causal loop diagrams are helpful:
Causal 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. Let’s hop into our example to make this more concrete.
Population Model
The two things that cause the Population to change are Births and Deaths, so we use arrows to represent these causal links. We know that more Births lead to a greater Population, and fewer Births will lead to a lower Population, all else equal. 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 indicate that a causal relationship has a positive polarity by placing a + sign next to the arrow head.
We also know that more deaths lead to a lower Population, and fewer deaths lead to a greater population. 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 represent this by labeling the arrow head with a – sign.
These causal links are true independently, and they are also both true at the same time. On their own, they don’t tell us what’s actually happening to the population. The direction of change in population is determined by whichever of these two relationships is dominant. As long as births exceed deaths population will grow, and whenever deaths exceed births, the population will shrink.
Now let’s introduce some Feedback into the model. 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). Therefore, we draw a positive causal link from Population back to Births.
Reinforcing and Balancing Loops impact Population
This link forms our first feedback loop, shown on the left side of the image above. 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. To read a feedback loop, you pick a variable to start with and arbitrarily pick a direction – either More or Less.
So let’s read this one starting with Population and More. More population leads to more births which leads to more population. 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. 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.
We see a different type of Feedback Loop when we examine Deaths. More Deaths today leads to fewer Deaths in the future. This is because more deaths today will cause the population to fall, which means less people will be around to die later. 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.
Feedback loops take on a life of their own. We see a set of relationships that are always happening over and over again generating behavior that unfold over time. 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.
The delay in deaths is one where we see huge differences across various countries. In Japan, the life expectancy is over 80 years of age, while it’s only 49 years in Afghanistan. Delays have important implications, so whenever you run into one, think to your self, “how long is this delay?” If delays are relatively long, that could lead to a lag in responsiveness or inability to adapt (i.e. you simply can’t change the population instantly), whereas if delays are very short or non-existent, the system might be more sporadic.
The delay it takes for people to change their opinions is very short in a young child, and very long in adults (some adults never change their worldviews after a certain age).
A Resource-Constrained Poor Country
Now let’s take a look at a model that captures a resource-constrained, poor country below. Can you trace the two new Balancing and Reinforcing Loops and make sense of them? Let’s give it a try!
Population Model with Resource Constraints
We’ll begin with the new balancing loop on the bottom right. As Population increases, the number of Resources per Person falls, and when this happens, the average Life Expectancy will also fall since fewer resources means less food, a weaker economy, fewer doctors, and fewer jobs. As Life Expectancy falls, the rate of Deaths increases, which causes the Population to fall. This balancing loop makes sense, but it will only come into play given that resource constraints are a serious issue.
Another interesting thing plays out as related to Life Expectancy in the new reinforcing loop on the bottom left. When Life Expectancy falls and infant mortality rates increases, people may desire to have larger families. This ultimately leads to more children in each household, which increases the Population size, exacerbates resource constraints and decreases Life Expectancy further. This reinforcing loop represents a vicious cycle where people essentially get what they want in the present at the expense of the future. Does this mechanism make sense? It certainly wouldn’t apply in every context, but in some situations, you could imagine how a mother, expecting several of her children to die before they reach a ripe old age, would want to have more children in anticipation of early deaths. The model is true in the context of a prevailing set of factors (resource constraints matter) and beliefs (having many children is the best way to ensure that you have family in the future).
Context Is Key
Keep in mind that this is just one simplified population model of a hypothetical population. It may represent some countries more than others. For example, some would argue that the link between Resources and Life Expectancy is a weak one as long as technological progress and innovations allow us to support our consumption habits without extracting resources at too high of a rate. But others argue that technology can only do so much, and that even the U.S. will eventually reach its limits. Some believe that we are using oil as if we’re fetching water from a well – we have no idea how much is left, so we behave as if it’s bottomless.
The particular problem and context of a model should always be clear. Models are used to frame problems and answer questions. They are explicit theories of why something behaves the way it does. They should help to clarify what is being considered and what is being excluded and present opportunities to suggest correction, additions, and improvements.
Summary
So next time you see a causal loop diagram:
Now practice what you’ve learned in this short Causal Loop Diagram Quiz!
Practice Reading More Causal Loop Diagrams (or see full list of essays with CLDs):
Causal loop diagram
From Wikipedia, the free encyclopedia
A causal loop diagram (CLD) is a causal diagram that aids in visualizing how different variables in a system are causally interrelated. The diagram consists of a set of words and arrows. Causal loop diagrams are accompanied by a narrative which describes the causally closed situation the CLD describes. Closed loops, or causal feedback loops, in the diagram are very important features of CLDs.
The words with arrows coming in and out represent variables, or quantities whose value changes over time and the links represent a causal relationship between the two variables (i.e., they do not represent a material flow). A link marked + indicates a positive relation where an increase in the causal variable leads, all else equal, to an increase in the effect variable, or a decrease in the causal variable leads, all else equal, to a decrease in the effect variable. A link marked — indicates a negative relation where an increase in the causal variable leads, all else equal, to a decrease in the effect variable, or a decrease in the causal variable leads, all else equal, to an increase in the effect variable. A positive causal link can be said to lead to a change in the same direction, and an opposite link can be said to lead to change in the opposite direction, i.e. if the variable in which the link starts increases, the other variable decreases and vice versa.
The words without arrows are loop labels. As with the links, feedback loops have either positive (i.e., reinforcing) or negative (i.e., balancing) polarity. CLDs contain labels for these processes, often using numbering (e.g., B1 for the first balancing loop being described in a narrative, B2 for the second one, etc.), and phrases that describe the function of th loop (i.e., «haste makes waste»). A reinforcing loop is a cycle in which the effect of a variation in any variable propagates through the loop and returns to reinforce the initial deviation (i.e. if a variable increases in a reinforcing loop the effect through the cycle will return an increase to the same variable and vice versa). A balancing loop is the cycle in which the effect of a variation in any variable propagates through the loop and returns to the variable a deviation opposite to the initial one (i.e. if a variable increases in a balancing loop the effect through the cycle will return a decrease to the same variable and vice versa). Balancing loops are typically goal-seeking, or error-sensitive, processes and are presented with the variable indicating the goal of the loop. Reinforcing loops are typically vicious or virtuous cycles.
