Social Feedback Loops
Social feedback loops describe a relationship within a social system where events feedback on themselves to create relationships of interdependence where different events work to balance each other or amplify each other. Feedback loops are central to the dynamics of nonlinear systems of all kind, from financial crisis to population growth, to ecosystem collapse to the outbreak of conflict; they are the engines of self-organization, being what drives the process as it develops over time. The are two types of feedback, positive feedback, and negative feedback. Positive feedback loops work to accelerate change while negative feedback, works to dampen down change, constraining the system towards a stable state. Positive feedback loops can be a powerful force that if left unchecked will take the system out of its current overall state and into a phase transition as it moves into a new regime. The interplay between periods of negative and positive feedback within a system create a pattern of development that is marked by prolonged periods of stability which are then punctuated by these rapid phase transitions.
Feedback loops describe a relationship of interdependence over time, meaning what happens now is going to affect what happens in the future and out of this feedback and interdependence of states over time we will get a certain pattern of development, so these feedback loops are not taking place between agents or groups at the same time but now represent relations over a period of time as the system changes and thus we are dealing with system dynamics. In the model of a linear system, there is an input to the system that generates some output, but this output does not affect its future input. Because of this, the homogeneity principle holds, that is that the input and output to the system remain constant over time so that things grow or decay in a linear fashion 2,4,6,8 etc. This type of linear development is really the product of independence between states over time, this model is like a business that never gets any better it simply stays doing the same thing year after year.
But as we know in the real world many social phenomena of change are not like this, they involve feedback loops over a period of time, what happens in the past feeds into effect what happens now and what happens now will feed into effect the future. Through this we can get a compounding effect as things build on top of themselves, our business can actually get better at what it does every year, so that the input-output ratio to the system will not stay constant.
Types of Feedback
Feedback systems define how an event may feedback on itself over time and what we are really interested in here is whether what feeds back will make the system do more of what it did in the past or less, because this will be definitive in its overall pattern of development as we will discuss. When what happened in the past feeds into make the system do more of what it did previously, then this is a positive feedback loop, everything is moving together in the same direction over time. With negative feedback loops the values move in the opposite direction, if we have more of something now we will have less of it in the future. Thus these negative feedback relations over a period of time will lead to stability and little change as what one does now is counterbalanced with what happens in the future.
Positive feedback loops are drivers of nonlinear exponential growth or decay within a system, the output to the system now is feedback in as the input to the future state of the system thus the system can build upon itself over a period of time, there is a compounding effect. The classical example of this being compound interest, the current output value of the account is fed into the future calculation where interest is added to it, this larger amount is then fed into the next cycle with the same rate of interest now acting on a larger figure thus producing a large growth rate. The thing to note here is that it is not just that the amount of money is growing it is more importantly that the amount that is being added each iteration is itself growing because of the compounding positive feedback relations over time.
Feedback loops are an example of the premise within complexity theory that complex phenomena can be the product of simple rules, almost all phenomena that you would consider not normal are nonlinear, positive feedback loops are behind very many processes of change within social systems. Just to make this more concrete we will go over a number of examples. A social riot would be an example of positive feedback, when a riot begins with few people these individuals are vulnerable but with every extra person that chooses to partake in the riot it makes it more likely that it will be successful and less like that any one individual will be reprimanded, thus more will beget more as this positive feedback cascades through the individuals aligning their states. Conflict escalation can involve positive feedback, given some act of aggression an opposing agent will be threatened, becoming less tolerant and more likely to react which will in turn feedback to effect the same action on the behalf of the other, an example of this would be an arms races between two nations, where the two sides continue to try and outcompete the other leading to all losing and growing potential for conflict.
Likewise, the phenomena of irrational exuberance is another example of a positive feedback, when the value of a trader’s stock goes up this feeds back to boost the trader’s self-confidence in their decision making and encourages them to make more investments that may be even riskier. Another good example would be what is called the Matthew effect within sociology, which describes the fact that advantage tends to beget further advantage, thus this phenomenon is also known as the rich get richer as these feedback loops tend to increase initial inequalities. We might think about the fact that bank managers are more likely to lend money to people who already have lots of money, likewise, those who are already well connected within society will have greater potential for making more influential connections. This accumulative effect is described within network science by the concept of preferential attachment, which explains that those nodes that initially acquire more connections than others will increase their connectivity at a higher rate, and thus an initial difference in the connectivity between two nodes will increase further as the network grows. We can see how this phenomenon of accumulative advantage may lead to greater polarization and of course a polarized social system means higher potential for conflict, this may be an example of what is called self-organizing criticality that we will discuss in a future section.
So whereas negative feedback within a linear system will give us linear incremental change, that is to say a simple quantitative change, positive feedback will give us a qualitative change. And this makes sense because control systems that use negative feedback are specifically designed to maintain a system within a certain set of parameters to enable its stable functioning, whereas nonlinearity is going to take the system outside of these parameters, thus discontinuing that state of functionality and requiring it to operate in a new fashion. This type of nonlinear growth is a very powerful force that is clearly unsustainable, it can not continue indefinitely, it will eventually drive the system out of its current regime.
Whereas linear development may maintain the system within its current attractor, exponential growth through positive feedback drives the system far-from-equilibrium and is a key characteristic of a system going through what is called a phase transition. Phase transitions represent periods of critical and rapid change within a system’s development either side of which the parameters with which we define the system change fundamentally, the melting of ice into water is an example of a phase transition, we get an abrupt transformation and a regime shift as water is a very different substance to solid ice.
Examples of phase transitions within social systems might include the fall of the Berlin Wall, before this rapid critical phase transition the global political environment was largely defined by a bipolar regime, before the fall this bipolar model was the parameter we used to define the system, after the event, the political environment was described with reference to a new set of parameter relating to globalization. The Arab Spring might be another example, The Arab Spring is widely believed to have been instigated by dissatisfaction with the rule of local governments. After many decades of the Middle East being held within a particular configuration or political regime, the Arab Spring was a punctuation of this equilibrium, that previous regime was a set of negative feedback loops that balanced the system into some equilibrium, we might say there was some balance of power, but this balance got broken through some small fluctuation, the self-sacrifice of a street vendor in Tunisia, this small event then got amplified by positive feedback into a large systemic transformation, through this positive feedback the balance of power was broken temporarily and the political system across the Middle East moved into a phase transition.
This process of self-organization involves what are called tipping points, within sociology, a tipping point is understood as a point in time when a group of agents—or a large number of group members—rapidly and dramatically changes its behavior by widely adopting a previously rare practice. The phrase was introduced to sociology by Morton Grodzins. Grodzins studied racial integration within American neighborhoods in the early 1960s. He discovered that most of the white families remained in the neighborhood as long as the comparative number of black families remained very small. But, at a certain point, when “one too many” black families arrived, the remaining white families would move out en masse in a process known as white flight. He called that moment the “tipping point”. Tipping points are the product of agents having thresholds that have to be met before they will act, and there is clearly a feedback loop at work here, every time an agent reaches a tipping point and acts this makes it more likely that another will also.
So for example if we take two groups of people and each individual has a propensity to adopt some new phenomena as seen on the screen 0-1-2-2-2 & 1-1-1-2-2 then we will notice that the first group will adopt the new phenomena as soon as it is introduced because the lowest threshold within that group is zero and once that person adopts then the next person at one will do likewise and then the rest, but the other group will not become adopters because they require one person before they will change.
The thing to note here is that both groups have the same average propensity for adoption, both groups as a whole are equally as receptive to this new phenomenon, we can then see that the average or normal is not important it is the outliers that matter. You have to know the distribution or variance and how they are connected, with more people in the tail you are more likely to get a collective action. And thus we can say that within these nonlinear systems it is the tail that wags the dog, meaning because of heightened connectivity and interdependence major new phenomena start at the fringes and then through positive feedback build up and make their way into effect the mainstream.
A combination of negative and positive feedback loops during the development of a system can lead to a model of development called punctuated equilibrium. Punctuated equilibrium is a model first derived from evolutionary biology but has also been applied to social theory as a method for understanding change in complex social systems. This model looks at the evolution of social change, suggesting that most social systems exist in an extended period of stasis, which are later punctuated by sudden shifts of radical change. Social systems are characterized by long periods of stability where negative feedback loops work to maintain an equilibrium holding them within a well-structured attractor state, this is then punctuated by large—though less frequent—societal shifts, driven by a positive feedback loop that drives the system far-from-equilibrium and out of its current attractor into a new one, during a phase transition that represents a new regime and new equilibrium under a new set of negative feedback loops.
This dynamic to nonlinear systems creates path dependency which explains how the set of states to a system now are limited and defined by the historical trajectory that led to this point in time, that is to say, complex social systems bare their history on their shoulders. Time reversibility only holds for some linear systems, but nonlinear systems are non-time reversible, the development of the system goes in one direction with respect to time, because of feedback loops, the system is within a particular attractor because of the choices made in the past. An example of this we could cite might be the clustering of businesses, similar businesses tend to congregate together geographically; opening nearby similar companies that attract workers with expertise in that business domain, this then draws in more businesses in search of experienced workers. This network effect is driven by positive feedback loops and negative externalities that have taken the system down a particular pathway into a particular basin of attraction from which it would be difficult to exit or alter.