 How complex systems like businesses and economies change over time is studied within the domain of systems dynamics that came out of MIT during the 50s and 60s. Central to this area is the idea of feedback loops which we'll be exploring in this video. As we talk about positive and negative feedback, virtuous and vicious cycles, we'll touch upon the subject of control systems and finally talk about causal loop diagrams. This will be continuing on with our previous discussion on causal links, whereas causal links define relations between components within space, all at the same time. But with feedback loops, we're defining these relations over time. So here we're talking about the dynamics of the system and how it changes over some period of time. A feedback loop defines a relationship of interdependency between two or more components where the change in state of one element affects that of another, with this effect then in turn feeding back to alter the source element. This dynamic, captured by feedback loops, plays a fundamental role within the self-organization of elements within complex systems. They are also a key model for understanding how nonlinear systems like markets and economies evolve over time. An example of this might be a dialogue between two people. What you say now will affect what the other person will say and that will in turn feedback as the input to what you will say in the future. What feedback loops are saying is that the output to the system now will affect its environment in some way and that effect will feedback to be the input to the future state of the system. This is in contrast to linear models that describe linear cause and effect, meaning the output to the system now will not affect its future state. Can be of two kind, positive and negative. A negative feedback loop represents a relationship between two variables, say A and B, where more of A will result in more of B, which in turn feeds back to result in less of A. For example, the relationship between supply and demand is a negative feedback. The more a producer supplies, the lower the price for it, which will feed back to reduce the incentives to produce more in the future. As opposed to negative feedback where more begets less, a positive feedback loop is a relationship where more begets more. More of A will result in more of B, which will feed back to induce more of A. For example, asset pricing involves a positive feedback as the expectations of investors goes up, demand and prices go up, which then feeds back to increase expectations once again driving the price up. Negative feedback loops take place over time and thus create a certain dynamic pattern to the system's development. A negative feedback loop is one of constraint and balance. As different things are being balanced, it is always tending towards some equilibrium point. If there is some external shock to a system that is not too large, the negative feedback loop will bring it back into balance. As such, a negative feedback is a control mechanism. For example, governments try to control economies through automatic stabilizers, where income taxes and welfare spending are used to dampen fluctuations in real GDP. They act to stabilize and balance economic cycles. Negative feedback control of this kind results in an inherently static system. As it is designed to resist systemic change, stimulus packages and bank bailouts during financial crises are another example. They are using the control system of the government to try and bring the economy back into its previous equilibrium. Positive feedback, in contrast to negative feedback, is a destabilizing process because some change in the system's output now will be fed back in at the next iteration where that change will be increased. Thus, there is no balancing mechanism. The system will stay moving off in the same direction as the change gets compounded with each iteration. This compounding gives us exponential change. And if we had rapid iteration, this exponential change is a very powerful force driving the system away from its equilibrium. If it does not get balanced by some negative feedback loop, it is bound to take the system out of its current regime and into a whole new state. Positive feedback loops and the exponential change they give rise to can be best described as radical phenomena. When they operate in isolation, without negative feedback, the outcome will be extreme. This change may be either very positive or very negative. A change in a positive direction is called a virtuous cycle, where more of the positive thing begets more of it. For example, investment in economic infrastructure is a virtuous cycle, as companies can be more productive, rendering more tax, which can then be reinvested in better infrastructure leading to a more efficient economy and so on, creating a spiral that moves upwards in one direction until it hits some ceiling. Economics of scale is likewise a virtuous cycle that a startup company can ride. Greater scale of production leads to lower marginal cost and lower price to the consumer, which leads to more consumers, which leads to more revenue, which feeds back to enable greater scale of production and so on. The net result of this will be exponential growth in the company's early years, but this only lasts for so long. As the market becomes more mature, some limit will be met that will place negative feedback on the system to re-stabilize it again. A vicious cycle is a positive feedback loop that goes in the opposite direction, spiraling downwards like quicksand. It's what management and government fear greatly. A classical example would be hyperinflation, or for example there was a vicious cycle behind the previous financial crisis. As housing prices declined, more homeowners went underwater when the market value of their house dropped below the mortgage on it. This provided them with an incentive to walk away from the homes, increasing defaults and foreclosures, this in turn lowered housing value further due to oversupply, reinforcing the cycle in the same downward direction. The whole process of an economy going into recession is also an example. Significant job losses lead to reduced spending, harming additional firms, causing more job losses and causing prices to fall in a way that makes those who still have an income hold back on spending, because they expect things to be even cheaper in the future. The mechanism of a vicious cycle is here dragging the whole economy towards collapse. From these examples, we should be able to see how powerful these positive feedback loops are as they lead to runaway effects that are very difficult to break. Once put into motion, they often drive the system into a whole new regime and environmental state. These examples should also illustrate how positive and negative feedback loops are central to the whole idea of control. As such, they are foundational to the science of control called cybernetics. Negative feedback is used within centralized control systems of all kind and it represents goal-orientated behavior. That is to say, the control system has to define firstly, what is the desired state to the system. If we want the thermostat to work in our house, we have to set the desired temperature before it will operate. This may be simple when it comes to heating our house, but when we use a negative feedback loop to manage a whole corporation or economy, we're going to have to spend a lot of resources in trying to predict the future to figure out what are the possible future scenarios, which of those is optimal and then accordingly adjust the mechanisms we have for regulating the system such as taxation, grants, interest rates and so on. Feedback is often removed from standard models in science, engineering and economics because it adds significant complexity and limits our capacity to project out into the future, predicting events as all future states will be contingent upon many feedback loops along the way. This failure to properly incorporate feedback loops into our analytical models is one reason we are very poor at predicting major non-linear changes such as financial crisis. Feedback loops are an inherent part to the development of complex systems like economies and they mean that they are non-ergodic, the future is not a simple transformation of the past. The faster the iterations to the system and the more feedback, the lower our capacity to foresee the future, meaning any centralized top-down negative feedback form of control has its limitations when dealing with complex organizations. It's really best suited to very simple systems operating in stable and predictable environments like our thermostat. In complex, volatile and uncertain environments control needs to be distributed out so that components can readily adapt to local level information. Negative feedback has to be built into the system on the local level through the appropriate connections between components. Top-down centralized control systems are appropriate when there is a lack of connectivity and information but as we turn up the connectivity and availability of information on the local level we can create distributed negative feedback loops through peer-to-peer connections and the exchange of information which makes the system greatly more robust, flexible and adaptive. This is of course very much related to the subject of self-organization that we'll be discussing in the next module. A causal loop diagram is then a map of all the nodes, causal links and feedback loops within a system. Out of this we can begin to get an understanding for its overall behavior. We can see the stocks and flows of resources within the system and can also begin to generate some quantitative model through computer simulations. These causal loop models have been used for modeling many economic phenomena such as product adoption, industrial regulation and environmental degradation. They are not designed to give us exact predictions to the future but more understand the key drivers and dynamics behind the system, thus allowing us to begin to think about what links need to be altered in order for the system to exhibit more of the desired behavior such as stability, robustness, sustainability or change of some kind. In this module we've been looking at how feedback loops drive the dynamics behind complex organizations like economies, markets and businesses. We talked about the two basic types of positive and negative feedback, how positive feedback leads to self-reinforcing behavior over time. As with each iteration to the system, some phenomena gets compounded giving us exponential runaway behavior. In contrast, we looked at negative feedback that works as a balancing mechanism. As with each iteration, the system counterbalances its previous direction, continuously leading back to some equilibrium point of stability. We mentioned how this is used to design centralized control systems. Finally, we had a quick look at causal loop diagrams that tried to capture the full set of causal relations governing the overall dynamics behind the complex organization.