[Music] hi I'm dr. Donna Gurule and this video will help you to better understand causal loop diagrams and their components let's get started causal loop diagrams are valuable because they help us to clarify our own mental models and make our thinking clearer identify common archetypes that drive systems behavior and share our mental models and modify them with others which potentially creates a rich dialogue causal loop diagrams consist of variables such as things actions or feelings connected by causal links or arrows with polarities which are positive or negative signs and delays listed as double yellow lines and delays indicated by double lines together these create positive and negative feedback loops that describe the circles of cause and effect let's break down the parts so that you can see the individual loops the two things that caused the population to change our births and deaths arrows are used to represent these causal links we know that more births lead to an increased population and fewer births will lead to a decrease population other factors being equal we would say this relationship has a positive polarity or is directly proportional this means 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 plus sign next to the arrow head we also know that more deaths lead to a decreased population and fewer deaths lead to an increased population the variables are inversely proportional 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 negative sign on their own they don't tell us what's actually happening to the population the direction of change and population is determined by whichever of these two relationships is dominant as long as births exceed deaths the 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 an increase in population a greater population also leads to more births since more people make more babies given a birth rate stays constant remember we're only considering the two variables that we are linking for polarity therefore we draw a positive causal link from population back to births this link forms our first feedback loop shown on the left side in a red circle 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 start with a variable and pick a direction 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 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 as well if this were the only feedback loop in the population system and people did not die then we would see an exponential growth in the number of people we've seen a different type of feedback loop when we examine deaths more deaths today leads to fewer deaths in the future why 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 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 are a set of relationships that are always happening over and over again generating behavior that unfolds over time these two feedback loops can cause a few different behaviors based on the birth rate and life expectancy we will observe population increasing as long as the reinforcing birth loop dominates and the leveling off of the death loop balance is dominant notice in this diagram that there are two hash marks on the causal links between population and births and between population and deaths hash marks represent a delay a situation where it takes time before the effect plays out in this population example it takes time for an individual to become of age to have a child which is why there is a delay between population and births this delay is longer in some countries like New Zealand where the average woman has children at age 29 compared to some developing countries the average woman has children at less than 20 years of age when creating a causal loop diagram delays must be included otherwise the reader will assume that the effect will be immediate let's go through through a few more examples when you add money to your bank account it earns interest based on the amount in the account the interest is reinvested to the money in the bank account so that the amount of interest will be greater in the future balancing loops known as negative loops are circles of cause and effect that counter a change with a push in the opposite direction the heart of the push the harder the system pushes back balancing feedback loops bring stability to a system depending on how it's perceived so they are essential to that system here's an example when the internal temperature of your body increases you sweat and as that sweat evaporates from the warm surface of your body you cool down balancing the initial increase when you are cool you sweat less there is less sweat to evaporate and it drains less heat allowing your body temperature to rise at the same time if your body temperature drops you may start shivering releasing more heat to warm your body and bridging the temperature gap balancing the initial decrease putting both of these loops together let's look at this concept as applied to technology when consumers adopt a hot new product like the latest iPhone more potential consumers encounter the product and are likely to purchase it themselves strengthening the word-of-mouth advertisement and leading to more adopters in the future this drives and increases sales and is represented on the left with the AR or reinforcing loop however if too many iPhones are released into the market creating saturation then less iPhones will be sold because the demand will go down this is represented on the right with the B or balancing loop this simple example of economics or supply and demand can be applied to any market now back to our original diagram let's assume we're discussing in developing countries population starting with a new balancing loop on the bottom right circled in red it shows those the population increases the number of resources per person Falls decreasing the average life expectancy 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 increase 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 also context is important in these situations these outcomes wouldn't apply in every population context but in some situations you can imagine how a mother expecting several of her children to die before they reach adulthood 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 a family in the future 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 US will eventually reach its limits one particular model and context of a model should always be clear models are used to frame problems and answer questions there are theories of what something acts in a particular way there are theories of why something acts in a particular way they should help to clarify what is being considered what is being excluded and present opportunities to suggest correction additions and improvements now let's go over some more examples to see how to read a causal loop diagram see if you can answer these questions correctly pop quiz what does this model say about the causal relationship in a traffic model more traffic density leads to a decrease in speed or less traffic density leads to an increase in speed in other words there is an inverse relationship between traffic density and speed how do you read this model about racial tension increased inequality leads to more conflict or less inequality leads to less conflict this relationship is directly proportional right this causal loop models how greenhouse gases labelled GHG interact with climate change what does it say and is it a balancing or a reinforcing loop let's walk through the feedback loop verbally start with a variable GHG greenhouse gas emissions and work your way around the loop more greenhouse gas emissions lead to more greenhouse gases in the atmosphere which lead to an increase in temperature which leads to more thawing organic matter there was trivia previously trapped in glaciers but there is a delay in this phase over time this increase in thawing organic matter leads to more greenhouse gas emissions that's not an immediate effect this is also a reinforcing loop because an initial increase in the starting variable led to a further increase in each variable this model shows the interaction between cows and the fields where they graze walk through each feedback loop and determine whether it is balancing or reinforcing loop for more graphs leads to more grass seeding which leads to increased new grass growth which becomes more grass or less grass leads to less grass seeding which leads to less new grass growth which leads to less grass in the future since more grass leads to more grass or less grass leads to less number four is a reinforcing loop loop number five more grass leads to less soil erosion which leads to more new grass growth which in turn leads to more grass or less grass leads to more soil erosion which leads to less new grass growth which in turn leads to less grass so number five is also a reinforcing loop loop number six more grass leads to a decrease in cattle death which leads to a larger herd size which leads to a higher grazing intensity which leads to less grass or less grass leads to more cattle death which leads to a smaller herd size which leads to a lower grazing intensity which leads to more grass number six is a balancing loop since more graphs leads to less grass over time or less grass leads to more my hope is that you have a better understanding of how causal loop diagrams are used to describe what's occurring in a system thanks for watching I'm doctor Donna Jean [Music]