Economic Feedback Loops

Feedback loops describe relationships of interdependence between parts and they are at the foundations of all nonlinear processes of change within economies

How complex systems like businesses and economies change over time is studied within the domain of system dynamics that came out of MIT during the 50’s and 60’s. Central to this area is the idea of feedback loops, which we explore in this article, as we talk about positive and negative feedback, virtuous and vicious cycles, we will touch upon the subject of control systems and finally causal loop diagrams. Feedback loops are an extension of the concept of a causal link, whereas causal links defined relations between components within space at the same time, feedback loops define relations of interdependence between parts over time. Thus we are talking about the dynamics of the system and patterns that emerge out of these feedback loops over time.

Feedback

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.1 This dynamic captured by feedback loops plays a fundamental role in the self-organization of elements within complex systems.2 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 feed back 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 feed back 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 that the output to the system now will not affect its future state.
Feedback loops can be of two kinds – 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 incentive to produce more in the future. As opposed to negative feedback where more begets less, a positive feedback loop is a relation 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 expectoration of investors goes up, demand and prices go up, which then feed back to increase expectation, once again driving price up.

Dynamics

Feedback loops take place over time and thus creates 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 the 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 a financial crisis 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 have 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.                 

Virtuous & Vicious Cycles   

Positive feedback is behind the formation of stock market bubbles and financial crises and can be identified as the driver of all nonlinear processes that create extreme events

This change may be either very positive or very negative. A change in a positive direction is called a virtuous cycle, where more of a 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 up in one direction until it hits some ceiling. Economics of scale is likewise a virtuous cycle that a startup company can rise. Greater scale of production leads to lower marginal cost and low price to consumers, which leads to more consumers, which leads to more revenue, which feeds back to enable greater scale of production, and so the cycle goes on. The net result will be exponential growth in the company’s early years, but this only lasts for so long. As a market becomes mature, some limit will be met that will place negative feedback on the system to stabilize it.

A vicious cycle is a positive feedback loop that goes in the opposite direction, spiraling downwards like quicksand. It is what management and governments 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 homes dropped below the mortgage on it. This provided an incentive to walk away from the home, increasing defaults and foreclosures. This, in turn, lowers housing values further due to over-supply, reinforcing the cycle in the same downward direction.
This 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 future. The mechanism of a vicious cycle is here, dragging the 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 environment.

Economic Regulation        

These examples should also illustrate how positive and negative feedback loops are central to the whole idea of control. As such, they are the foundations of the science of control called cybernetics. Negative feedback is used within centralized control systems of all kinds and it represents goal-orientated behavior; that is to say, the control system has to define firstly what is the desired state of 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 are going to have to spend a lot of resources in trying to predict the future and 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 within 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 into our analytical models is one reason we are very poor at predicting major nonlinear changes such as economic crisis. Feedback loops are an inherent part of the development of complex systems like the economy and they mean that they are nonergodic. The future is not some simple transformation of the past. The faster the iterations to the system and the more the feedback, the lower the capacity to foresee the future, meaning any centralized, top-down negative feedback form of control has its limitation when dealing with complex organization. It is really best suited for very simple systems, operating in stable and predictable environments, like a thermostat.

Distributed Feedback

Centralized regulatory systems like governments work to try and maintain stability within an economy by managing negative feedback loops, such as the supply and demand of money

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 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 will 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 of its overall behavior. We can see the stocks and flows of resources within the system and can also begin to generate more quantitative model through computer simulation. These causal loop models have been used for modeling many economic phenomena such as product adoption, industry regulation, and environmental degradation. They are not designed to give us exact predictions to the future but more to understand the key drivers and dynamics behind the systems, thus allowing us to begin to think about what links need to be altered in order for the system to exhibit more of a desired behavior such as stability, robustness, sustainability or a change of some kind. 

1. (2017). Public.asu.edu. Retrieved 26/5/17, from http://www.public.asu.edu/~kirkwood/sysdyn/SDIntro/ch-1.pdf

2. (2017). How Self-organization Works. Retrieved 26 May 2017, from https://goo.gl/N0w3TN

2017-07-22T19:04:04+00:00