So let's talk a little bit more about causal loop diagrams. Now, causal loop diagrams are very helpful in capturing and communicating sources and implications of feedback within a complex system for an individual or for a team. I've found them to be very useful initially in a project, when I'm trying to understand the scope and the possible interconnections and the feedback loops within a supply chain that I'm just learning. And they're great models to share that insight back and forth with the team members. Now, these are models. And so, you should try to keep them very simple because that's what models are for. We're trying to simplify reality to get to the critical elements that are important. So now, when I look at causal loops, they're really made up of causal links. And these causal links describe the structure of the system. And remember, the structure is what generates the overall behavior. Think back to our chickens, our eggs, and our roadway crossings, right. The chickens and the eggs-- that behavior was more of a dramatic increase, an exponential increase because there was nothing limiting it, it was all reinforcing. As opposed to the chickens and the roadway, it was more balancing. They were brought in check. So the interaction between these two causal links, this feedback between them-- this is what describes the overall behavior. Now, what these causal link diagrams do not do is, they don't describe what the values the variables will take. Instead, what they're describing is what happens if there's a change in one of them. Now, they have to have either positive or negative polarity. So let's do some quick examples. So here I have product quality and sales. And so we believe that there is some kind of causal link between product quality and sales. And all else being equal, if the product quality increases, then we expect the sales to increase above what it would have been normally. And vice versa-- if the product quality goes down, right, then we assume that sales will also go down. They move in the same direction. Now, this is assuming that all other things being equal. Nothing else is changing. We're freezing the rest of the world. This is ceteris paribus. Let's do another quick example. How about product price-- if we see product price increasing, we would expect the sales to decrease. So all else being equal, if product price increases, then sales will decrease below what it would have been normally. And vice versa-- if product price goes down, then we expect sales to go up. Again, all other things being equal. Now, these are called causal links because they should have some kind of causal connection, not just a correlation. Correlations might reflect some past behavior, and it's important to look at, but correlations do not capture the underlying structure. And that's what we're trying to get here. Links should also always be labeled for polarity unambiguously. They're either positive or they're negative. Now, if you look at some other system dynamics texts and other literature, sometimes they use slightly different notation. Instead of a positive, they'll use an s for same. It moves in the same direction. And for negative, they'll use an o for opposite. So it makes sense, but we're going to stick with the more standard nomenclature of the plus and the minus, but really says the same thing. OK. So those are causal links. Now let's go into loops because that's what we're really doing here. So we have these causal loops and they're going to be one of two types. They're either going to be reinforcing or they're going to be balancing. A reinforcing loop is a collection of links that form a loop that provides a positive feedback. And so we'll see a certain type of behavior over time or a dynamic behavior and that will be an increase. That exponential increase we saw with chickens and eggs. A balancing loop is a collection of links that form a loop that provides a negative feedback and brings things back into balance. And we saw that with chickens and the roadway crossings. Where they interact with each other and it brings things down to a balance. Now, in the example we had it would probably go to 0 unless I introduced new chickens. But if I had a certain rate of introducing chickens or some level I'm trying to go to, it would bring that down to that level. Now, we bring them as being either positive or negative. And so, how do we determine a loop's polarity? Well, I have two approaches here, but let's go through an example and then we'll come back and explain how we did what. So here's-- what I've got here is a simple causal diagram with two loops. Going to start here in the middle with the orders booked and we're going to go over the left-hand side loop first. So we assume the number of orders that I have booked, the number of sales I have booked, will increase the budget I can allocate for my sales team. Right. The more I sell, the more I can get people to help sell more. So that's a positive link. Now, as my budget for sales team increases, chances are my size of my sales team will increase, right. More budget, I can get more sales people. So the size of the team, hopefully, the bigger my sales team, the more orders I'll have booked. Right. Because if not, then I've got a problem with my sales guys. So I've got three links in this loop and they're all positive and they're all reinforcing. So I assume if this was left by itself the more orders will drive more budget, drive more team, drive more orders. And we see how that kind of connects in. It's a reinforcing loop. And we can see what the behavior would be. It would be increasing exponentially. But there's a loop on the right-hand side, too. So as my number of orders booked increases, that's also going to increase the number of orders that are backlogged. Right. The more I have the more probability I'll have of backlogged orders. Now, the more backlogged orders I have, the higher the number of delivery delays I'm going to have. So it's another positive link. The higher-- the more delivery delays I have, the lower customer satisfaction. So this is a negative link, now. And then if customer satisfaction-- if that increases, then the number of orders booked will increase. If it decreases, the number of order booked will decrease. So this is a balancing because it moves in the opposite direction. So the number of orders booked on this left-hand side was reinforcing and here it's more balancing because it'll bring it back in line. So it's a balancing loop. Now, I could also figure this out by counting the number of negative links. And so if it's odd then it's balancing. Think of it as a positive is a positive number. And a negative link is a negative. And when you multiply them, any positive time a negative will be a negative. However, two negatives turn into a positive. So let's give a quick example of that, something near and dear to my heart-- sleeping in class. So if you sleep in class, generally, that means your grades will go down. So that's kind of a negative link, right. More sleeping in class, generally, your grades will go down. As your grades go down, generally, you have more pressure in terms of what happens to staying up late at night. So as your grades go up, we expect the pressure to go down. As your grades go down, we expect the pressure to go up. So it's another negative link. And then the pressure-- as the pressure to stay up late stays up and gets high, then you're probably going to sleep more in class because you'll stay up later. If the pressure is low, you won't sleep as much in class. So what is this doing? Well, I have two negatives here so they cancel out. This is a reinforcing loop. As I sleep more in class, my grades go down, increases my pressure, I stay up later, I sleep more in class, my grades go down. So it's a reinforcing loop. And if we look at the amount of sleeping in class, that will increase dramatically because there's nothing holding it in balance. So hopefully this gives you a sense of how to identify for a causal loop, whether it's a reinforcing loop or a balancing loop. And again, the behavior of each of these loops and how they interact with each other is what drives the underlying behavior. It's that structure that drives the behavior. So some last tips-- these are just really quick-- for when you do these diagrams. Name and number of them. Whenever you do loops and have a larger, more realistic model, you're going to have a bunch of these. So name them balancing one, two, three. And give them names like sale cycle, order reduction cycle, sleeping cycle. So you can name them and understand and refer to them. You should indicate delays on your loops on the individual links. And we'll talk about this later in another video segment. The variable names should be nouns or noun phrases because remember, these are variables. So they're things. They're things that you can actually count. So you should also make sure they have a sense of direction so I know the number of orders, the satisfaction level. Don't just make it feedback because that's ambiguous what that means. Is it more? Is it less? So you want to make it something that you can actually measure or compare. And finally, you should keep them positive. You want to make sure it's driving profitability, not nonprofitability. Or driving satisfaction, not dissatisfaction. It just makes it clearer because remember, these are communication tools. These are meant to capture an idea and communicate it to others. So the simpler you can make it, the better. So for drawing them, along those same lines-- you want to use curved lines for these information feedbacks because that's what we're really capturing here. This will make more sense when we introduce stocks and flows because those are kind of straight lines. So it helps differentiate them. Minimize cross lines because that just adds to confusion. Avoid all this chart junk. The whole acronym of keep it simple, stupid. Don't put in fancy pictures or anything. Just try to keep them as simple as possible. And avoid putting all your loops in a single diagram if it gets really big because that is very impressive, but it doesn't really communicate as well. And the last thing is probably most important-- when you do these, iterate, iterate, iterate again. It's a communication tool. So use it. See how it works. Come back, do another version. So these are just some simple tips for how to use causal loop diagrams.