 In this module we'll be continuing our discussion around self-organization and non-linearity within complex engineered systems. As we talk about path dependency and attractors, we'll look at how the process of self-organization and evolution results in systems that are sub-optimal and contingent on historical events. We'll talk about the network effect and negative externalities and how they lead to lock-in. The key takeaway from this module will be in understanding the process through which attractors are created and work as defining factors in the evolution of technology. Because we can fully control and design simple linear technologies like a refrigerator, we can take our design and by simply inputting enough resources, we can just produce the technology all in one go, rolling them off the production line. But complex engineered systems like cities and the internet don't just pop into existence like this. Instead, they start out simple and they go through a process of evolution to become complex. The important thing for us to note here is that they are the product of their evolutionary history and they carry this history with them in their DNA and overall makeup. In the same way that the fridge is the expression of the design that created it, these complex engineered systems are the expression of the evolutionary process that created them. We often think of engineering and technology as driven by efficiency and this may be the case on the micro level where we can fully control the design and production process for our refrigerator. But on the macro scale with self-organization, we often come to suboptimal solutions due to inertia, economic and socio-political dynamics. To illustrate this, we might think about the fact that these complex engineered systems typically involve large amounts of fixed capital and sunk costs. A city gets one chance to build its expressway out to the airport and that's it. It will be there for the next 50 years or more. The same with the internet. Now that we've built it, there is no option to build another one just because it is in some way suboptimal. History has played out to create these things and there is no going back. We have to live within and work within that context that it created and this is what we call path dependency. Path dependency describes how the set of decisions one faces currently are limited by the decisions one made in the past, even though these circumstances may no longer be relevant. Even though previous choices were made on chance or limited information with better options now being available, it is still easier to simply continue upon a pre-existing suboptimal path than to create an entirely new one. In other words, the present is never a clean slate where we are free to make any decision. It is in fact contingent on how we got to this point. In a very broad sense, it means that history matters. When we look around us, we can see that our systems of technology are very much a product of a path dependent process. Why do we still use the QWERTY keyboard that was designed for typewriters when it is not the most efficient for today's keyboards? Why do we still use the standard gauge train track designed two centuries ago for horse drawn coal carts to run today's powerful trains when it is far from optimal? Why is it so difficult for us to switch to renewable energy sources? Why do businesses all cluster in a particular area like Silicon Valley when there is nothing special about that particular location? All of these examples are because the choices we made in the past as to what technology we adopted influence the choices we make today. Path dependency is particularly acute in complex systems because of their high degree of connectivity and more importantly interdependency. Things don't happen in isolation. During the systems development, paths to the system interact with others and they co-evolve to become interdependent. Path dependency is particularly acute in these complex engineered systems because of their hierarchical structure. They are multi-tiered with end user technologies depending upon infrastructure technologies lower down the solution stack. They are what is called platform technologies. When a new technology platform is adopted like Microsoft's Windows operating system in the 90s, over time many new technologies are built on top of this and become dependent upon it. A whole ecosystem of new applications, new programming languages, new firmware, hardware, fenders, instructors, technicians and so on, meaning that small changes in the platform technology may result in a large effect across the ecosystem that has been specifically designed for it. And this is often the case for infrastructure systems like transportation networks and electrical power grids. They are deeply embedded within the socio-economic and technological fabric of the society with many deep interdependencies. The basic theory to path dependency is that it is a product of a self-organizing process where some small initial event that is often somewhat arbitrary in nature comes through positive feedback to create a lock-in effect. This lock-in effect leads to negative externalities, inertia and drives a particular course of events that are difficult to change in the future. So let's analyze this process a bit further to try and understand it better. Path dependency maintains that the starting point as well as feedback loops along the way affect and shape the end outcome to the technologies of today. In the language of chaos theory this is called sensitivity to initial conditions. More popularly it is known as the butterfly effect. Because of feedback loops some small possibly random event in the past can in fact turn out to have very significant consequences in the present or future and that we cannot predict this process a priori. We have to run or simulate the running of the system in order to understand its future state. An example of this might be the initiation of the First World War through a relatively small event in Bosnia. There was no way of knowing that this small event would lead to a world war and the reshaping of Europe's borders because this phenomena really emerged out of the nonlinear interactions during the system's development. Next positive feedback and negative externalities take hold to drive the system's development. Economics of scale is a good illustration of this. The more users there are of a particular technology the more we can leverage economics of scale to reduce its price which will in turn feedback to attract more users. This is a positive feedback and this is how some companies can get exponential growth as they ride this wave of positive feedback during the early stages of a new technologies life cycle. Added to this we have the network effect. The network effect is really due to the fact that the value of many technologies is in the capacity to interoperate with other users. Urban mass transit systems have the network effect. Every time we build a new station it adds value not just to the users of that particular station but to the entire network as everyone now has more possibilities in their destination. Both positive feedback and the network effect are powerful forces that once they take hold of a particular technology will amplify it but we can also add to this negative externalities meaning that when someone chooses to use a particular technology that choice decreases the value of another competing technology for all of its users. Once a particular industry or company adopts a particular standard this will crowd out others because the more a particular technology or standard grows the greater the cost to other people if they choose not to use it. This makes it very difficult to change some technology or standard once it's taken hold even if alternatives may be more efficient. And thus this particular pre-existing technology is essentially being subsidized by the network effect and the negative externality of not being able to interoperate with others if you change. This positive feedback and negative externality combine to create an attractor meaning once they have taken hold around a technology they work to subsidize that technology and make it an easier solution to any other of a number of different possible solutions as it becomes the default. Because the other options are now more costly or difficult this technology now has an attractor built around it. In non-mathematical terms an attractor is a set of states towards which a system will naturally gravitate from any given initial state and will remain within these set of states unless significantly perturbed. This is essentially the same thing as a default where default means a value or state to a system that is automatically selected if no other option is specified. New entrants to the industry or new adopters of the technology without specific reason to do otherwise will adopt this default technology because of the attractor around it. This subsidizing of a technology solution that comes with the network effect and negative externalities and the attractor space that it creates results in inertia the resistance to change. An example of this might be what is called carbon lock-in referring to the self-perpetuating inertia created by large fossil fuel based energy systems that inhibit the adoption of alternative energy technologies. Now that we've built up sophisticated machinery for extracting and processing petroleum and the combustion engine that has become a default technology the industry is being subsidized by economics of scale and the network effect meaning because of historical events we can produce a barrel of oil very cheaply and if you have a barrel of oil you can do almost anything with it from making raincoats to greasing your car's wheels to trading it on the futures market. It is interoperable across a wide set of technologies giving it the network effect and attractor and creating inertia. All of these positive feedback the network effect and negative externalities mean that once we decide to go down a particular path it is self-reinforcing and excludes other possibilities in the future creating the inertia of the lock-in effect. Breaking out of this will require either greatly more efficient technology coming along or very efficient organization for people to cooperate on changing to a better available solution. And cooperation is a very important aspect to this. It would take widespread cooperation for us to globally standardize the electrical plug or train track gauges and this is an example of how the sociopolitical domain influences the development of the technical domain. This inertia of the lock-in effect is not just a technical phenomena but also a sociocultural phenomena. Ways of doing things become embedded within a culture and there will be resistance to change. In the 19th century horses mattered. Today no one really cares about horses but instead cars matter and have significance to people today. Advertising companies create stories about them and they become part of our culture and way of doing life. People like to think that their lives have meaning and that things are the way they are for some reason. Most people don't like the idea that their lives and the world around them are in some way arbitrary. No matter how impersonal these technologies might seem they are part of our lives and we create stories around these things to give them meaning. Added to this is the uncertainty of change. Most people don't like uncertainty and they will remain with a particular pattern of organization, technology or solution because it is known and predictable. Again creating inertia due to sociocultural factors. In this module we've been talking about path dependency as we looked at how complex engineered systems are the product of a self-organizing evolutionary process. That this process may involve sensitivity to some relatively arbitrary initial condition but due to their highly interconnected and interdependent nature this initial event can become amplified as the network effect and negative externalities kick in to create an attractor with the technology becoming a default standard and the emergence of inertia. The net result of all of this being an outcome that is far from an equilibrium optimal solution but instead contingent on the system's history what we call path dependency.