The term technology evolution can be used to describe how complex systems of technology are shaped by, and developed through the process of evolution over prolonged periods of time. Technology evolution may be defined in terms of rationalization, where rationalization is the making of a process or system more efficient. Here efficiency is understood as a ratio of inputted resources to the output of a solution; resources of both natural capital and human resources. We can then define a simple parameter spanning from low efficiency to high efficiency, with rationalization being the function that maps the system to a different value along this metric, corresponding to the process of technology evolution.
As an illustrate we could take a technology for grinding flour, what we call a flour mill. At the low end of this spectrum, we will have a technology that requires a high input of resources with limited throughput. We might have one of the original stone mills developed a few thousand years ago, driven by manual labor at a low throughput of flour. Through the process of rationalization we have made this system more efficient at grinding flour, thus at the high end of the spectrum we have a contemporary mill that is automated with a high throughput of flour to energy inputted. This process of technological rationalization has not only increased the rate of throughput to the system but also reduced the requirement for physical resources and human capital by automating it. At the beginning of this process of rationalization, the system was very inefficient and thus there was still a lot of value to be gained by rationalizing it. But by the time we get to the end of the process, the system may be very efficient and thus there is often very little value to be gained by increased rationalization.
In order to capture how efficient a technology is and its relation to other technologies, we can use the model of what is called a fitness landscape, which is a three-dimensional representation of the technology landscape. Like a rugged mountain range, it has points of different elevation with these different elevations representing how efficient that technology is. The higher you are up on one of these mountains the better or fitter the technology is at solving the problem at hand, but also similar technologies that interoperate are clustered together on the landscape. Through innovation, rationalization and evolution technologies try to climb to higher peaks on this landscape. But a technology doesn’t exist in isolation. It is part of a whole ecosystem of other technologies and its utility is also defined by how well it fits into that environment. Technologies today rarely stand alone, they more often form part of service networks that deliver functionality, and thus their effectiveness is also largely in their capacity to interoperate with other technologies and provide a required differentiated function within these service systems.
Because different technologies need to inter-operate, they are interdependent, meaning the fitness of one technology is dependent upon others. For example, there are over 25,000 companies developing technologies with Bluetooth capabilities. If the protocol was to be significantly altered or even discontinued, this would affect the entire ecosystem. Because of this interdependency, the landscape is not static but in fact dancing around in response to all the small and large changes that are being made to the individual technologies. But also due to the fact that the actual problem that they are trying solve is also changing, at different stages of technological development new possibilities and challenges emerge, fundamentally altering the landscape.
The model of a fitness landscape is a powerful model for understanding the evolution of complex adaptive systems. But it gives us a somewhat narrow vision to the evolution of technology, because new technologies and ideas can create whole new industries and landscapes. Technology is just one part in the broader technical framework of what is called STEM, which stands for Science, Technology, Engineering and Mathematics. As we know technological development is intimately interconnected with and dependent upon these other domains. This acronym should really read MSET in order to represent the process through which our technical body of knowledge develops and the set of dependencies between them. Technology is dependent upon engineering, which is dependent upon science, which is dependent upon the formal systems, primarily mathematics. There may be many nonlinear cross pollinations within the domain of technology and engineering to drive innovation, but ultimately new major technological paradigm shifts require breakthroughs in math and fundamental science.
This is most evident when we look at how the breakthrough of the modern scientific revolution gave birth to a new set of engineering methods and the industrial revolution. These major paradigm shifts result in the whole landscape changing. Not only is the set of solutions redefined, that is to say, the set of engineering methods and technologies, but also the actual problem space itself may be redefined, because that is what theory and science do. They redefined how we see the world, and thus what exactly the problem we are trying to solve is. We might call this thinking outside the box. We are not just trying to define what the solution is but actually redefining the problem. The two can co-evolve, because ultimately what we are trying to do here is solve problems, and we can do that by changing the problem or changing the solution. For example, the shift from a pre-modern to a modern view of the world based upon science redefined that problem space that we are trying to solve, and that’s a real paradigm shift and change in the whole landscape.
Evolution then, is a search over this landscape in order to find new and better solutions to the given environmental challenges. Evolution involves a number of key stages; Firstly, the production of a variety of solutions to the given problem, secondly, the application of these innovations to the problem to see which is best suited, and third selection, in order to remove those variants that were least effective and make the efficient solutions more prevalent in the next lifecycle of the system. Lastly, we need to be able to iterate on this process for a number of life cycles. Each iteration of the process should change the location of individual technologies on the landscape. The adaptive cycle gives a visual representation to the stages within the process of evolution. The adaptive cycle is a model used to capture the different stages that ecosystems go through during the course of their evolution, but it is equally applicable to all complex adaptive systems from social organizations to the development of new industries and technologies. It defines the macro-state to the process of evolution during four distinct stages of development, including growth, conservation, collapse, and reorganization.
In the growth phase, new scientific or fundamental engineering knowledge provides a new fertile ground on which innovation can happen. Without incumbents many new possible solutions can emerge. An example of this growth phase at the moment might be the era of 3D printing. Without an industry established enough to support any of the big players, it is full of small tinkerers and startups that are created out of garages. In the conservation stage some technologies have proven more effective, and by leveraging the positive feedback loop of economics of scale are able to outperform any newcomers to the industry. Economics of scale creates high barriers to entry as the industry becomes consolidated and mature. This is a period of maximum efficiency and minimum flexibility, with all available resources held within a productive configuration making the environment conservative towards change. During the release phase, some external environmental disturbance such as some disruptive innovation eventually triggers the collapse of the system as elements have become inflexible from over-exploiting a single niche. The relationships are broken with the elements and the resources they held becoming released. The elements that remain after the release stage will reorganize. In this stage, the connectedness of the system is low but the potential is very high. Therefore, novelty arises. Foreign elements that would in other stages be out-competed can establish at this point. The growth stage follows and a new cycle begins. The adaptive cycle is a very generalized model and we are far from fully understanding the dynamics behind it, but it does capture much of the macro scale stages that are characteristic to the development of adaptive systems, developing through an evolutionary process that is engendered in some dynamic between order and chaos.
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