A path-dependent process is one whose outcome evolves as a product of the process’ own history. The idea of path dependence is intended to capture the way in which small, historical contingent events can set off self-reinforcing mechanisms and processes that “lock-in” particular structures and pathways of development.1 As a result of this the set of decisions one faces currently are limited by the decisions one has made in the past, even though past 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 sub-optimal 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.2
When we look around us we can see that our systems of organization are very much a product of a path dependent process. Why do we still have the QWERTY keyboard that was designed for typewriters when it is not the most efficient for today’s keyboards? Why do we still used the stand 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 systems of organization we adopted influence and constrain the choices we make today.
Path dependence is particularly acute in complex systems because of their high degree of interconnectivity and more importantly interdependency. Things do not happen in isolation. During the system’s development parts to the system interact with others and they co-evolve to become interdependent. Path dependence is particularly acute in complex organizations because of their hierarchical structure. They are multi-tiered with the upper levels depending upon the lower down. For example, when a new platform technology is adopted like Microsoft’s Windows operating system in the 90’s, over time many new technologies are built on top of this and become dependent upon it, a whole ecosystem of new application, new programming languages, new firmware, hardware, vendors, 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 transport networks and electrical power grids. They are deeply embedded within the socio-economic and technological fabric of a society with many deep dependencies.
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.
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 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 phenomenon really emerged out of the nonlinear interactions during the system development.
Next, positive feedback and negative externalities take hold to drive the system’s development. Economies of scale is a good illustration of this. The more users there are of a particular technology the more we can leverage economies of scale to reduce its price, which will, in turn, feed back 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 state 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 their 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 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 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 has taken hold, even if alternatives may be more efficient, and thus, this particular preexisting technology is essentially being subsidized by the network effect and the negative extremities 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 of a system that is automatically selected if no other option is specified. New entrants to the industry or new adopters of the technology without a specific reason to do otherwise will adopt this default technology because of the attraction 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 have built up sophisticated machinery for extracting and processing petroleum and the combustion engine has become a default technology, the industry is being subsidized by economies 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 use it to do almost anything from making raincoats to greasing you car’s wheels, to trading it on the futures market. It is interoperable across a wide set of technologies giving it the network effect, an attractor and creating inertia.
All of these, positive feedback, the network effect and negative externalities mean that once you decided 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 a greatly more efficient technology coming along or a very effective organization for people to cooperate on changing to better available solutions. 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.
Thus, this inertia of the lock-in effect is not just a technical phenomenon but also a socio-cultural phenomenon. 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. Advertising companies create stories about them and they become part of our culture and way of life. People like to think that their lives have meaning and that things are the way they are for some reason. Most people do not 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 do not 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 socio-cultural factors.