Evolutionary Economics

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Evolutionary economics is a paradigm of economic development that is focused on the internal dynamics through which a macro economy generates novel phenomena and changes over time without the guidance of some centralized regulatory mechanism. Within this paradigm, evolution is seen as ultimately a search mechanism; the means through which a complex adaptive system searches for the appropriate solution to the challenges of operating within a given environment.[1] Through this process, the whole system develops to exhibit greater complexity and becomes capable of operating in a broader environment. This process of economic evolution is performed similar to that taking place within natural ecosystems through a process that involves a number of key stages, including: Firstly, the generation of variety through cross mixing and invention; secondly, adaptation, whereby products and services are exposed to their operating environment in order to reveal their functionality; and finally, selection whereby functional variants are selected to become more prevalent within the economy’s future life cycle. The result of this process of evolution acting on the economy is that the economic system as a whole develops to exhibit greater differentiation and integration over time.[2]

Development Approaches

Ever since the end of the colonial era and the birth of many new developing nations around the world, the question of how economies develop has been of major interest to both policy makers and economists. Since that time, we have seen some countries like Mali make little economic development while others such as South Korea have grown rapidly to become advanced economies. During such time we have also been through many different theories surrounding how economies grow and how best to manage their development. Today with the integration of our global economy this question of why some countries develop rapidly while others remain stagnant has moved to the forefront and it remains very much an open debate within economics

It is important to firstly recognize that the question of economic development is, of course, a very complex one in that it involves many factors that are traditionally outside of the domain of economics, socio-political, environmental, historical, cultural and so on. This is even more so than in any other area of economics. As we cover in a related article, there is really just two fundamentally different paradigms to economic management; top-down centralized regulatory systems and distributed adaptive networks. Each of these very different paradigms to economic management gives us a very different vision of what economic development is and how it should be conducted. We will firstly talk about our standard approach to make it explicit, before going on to talk about the evolutionary approach.

Linear Model

Within the centrally regulated paradigm to economic development, a model of the system and its future environment is created – we define a future desired state and directly coordinate the components within the system towards achieving this end typically through some set of incentive systems. The aim is to maximize throughput. Within a macroeconomy, this gross throughput is captured by the metric of gross domestic product, which is seen to be the primary metric to an economy’s state of development and the key parameter that we are trying to optimize for.[3] This paradigm to economic development found its most clear theoretical expression in the so-called linear-stages-of-growth model, which posits that there are a series of five consecutive stages of development which all countries must go through during the process of economic development.[4] The primary objective of developing nations was then seen to be in controlling and guiding nations through these successive stages. This model has had both its successes and failures, even within the same country. For example, China’s great leap forward project was one of its greatest disasters, but China has also used the same centralized regulatory model over the past 20 or 30 years to what might be described as a success.

This centralized model is, in its basic structure, a simple linear model to economic development, and linear models work best in simple environments, meaning it may be relevant to the earlier stages of economic development – that is to say when the economy is going through agrarianisation and industrialization – but whether it is an applicable model for the development of post-industrial, information and knowledge economies is an open question. The end of industrialization within advanced economies is witnessing the rise of a new form of networked IT enable economy. People and small organizations now have powerful tools for information processing in their hands. They are connected like never before into global networks that often bypass centralized regulatory systems either partially or fully, and they increasingly have unfiltered access to the world’s store of information and knowledge.

Evolutionary Development

It is only really within this context where we have an absence of top-down centralized regulation and agents with the capacity to interact and adapt locally that the process of evolution can play out. Without a centralized regulatory system, the components are not held in some predefined configuration. There is no one overseeing the whole system, no predefined trajectory and most importantly there is no goal to the whole enterprise; there is nowhere to get to, and this is a very radical idea because we are very much grounded in a linear conception of economic progress. Evolution is not like this at all though, it is very simply about adapting to the environment and with complex systems that are operating in volatile and uncertain environments, this is the best that we can hope for. The idea that we can know and control the whole system, know the environment, know the future state to that environment and align the whole system towards reaching some optimal state is a virtual impossibility. It is a legacy of having previously operated within a much simpler environment. It does not really work in complex systems, and evolution, although far from optimal is the appropriate paradigm.

Whereas adaptation is a micro level phenomenon – in that it describes how an individual agent can change in response to some change within its environment – evolution is essentially the same process of adaptation but it plays out on the macro level; describing how a whole population of agents manages to adapt to their environment.[5] It is not an immediate process. It plays out over the course of many life cycles to the system. These life cycles can be quickly iterated upon to get a rapid pace of development but it is more often associated with a long-term process of change within large complex adaptive systems such as ecosystems, technology infrastructure, cultures, all kinds of social institutions and of course, economics.

With an evolutionary process information is being received continuously by the agents and strategies are being updated in a distributed fashion all the time. During this process of change, we get coevolution and novel niches emerge. These niches might be new markets, new technologies, new behaviors or new institutions. The very act of filling a niche may provide new niches with the result being ongoing change and constant disequilibrium.6 Just as the process of adaptation requires a dynamic between order and chaos, so does evolution. As Schumpeter illustrated it is a process of creative destruction. We are trying to maintain the function and pattern to the overall system, but any component or set of components are equally composable and decomposable.[7]

Stages of Evolution

There are a number of key elements that are required to be present before the process of evolution can act on a system. Firstly, the system must be able to generate some form of variety between its constituent elements, which is typically done through some form of cross mixing between elements. Secondly, components must be exposed to the operating environment and capable of acting autonomously in order to express their distinct properties or functionality. Lastly, there must be some form of selection mechanism that is able to act objectively in evaluating the effectiveness of the different elements within that particular context; retaining those that have been effective while disposing of those that have not.[8] This process then has to be iterated upon over the course of a number of life cycles before change appears. But through this process of evolution, systems develop to become both more differentiated and integrated, which allows them to operate in a broader, more complex environment in a more sustainable fashion. By altering the mechanism through which this process of evolution operates on a system, we can speed it up or slow it down, perform it effectively or ineffectively. We will go over each of these stages to the process of evolution within economic systems as it acts both on the real economy of products and services but also on the institutional level of businesses, financial organizations etc.


The initial stage in the process is about producing variety. Within biology, this is achieved through the deformation of DNA and cross mixing of genes, illustrating that the creation of new variants can be both random as well as intentional. Of course, producing new products and services at random would give us a combinatory large number, that would be unfeasible. Through our capacity for imagination and design, we are able to reduce this number down to a small subset, and of course, most novel innovations are not novel at all, they are simply remixing pre-existing solutions. The paradigm of economic evolution is focused on the non-equilibrium processes that transform the economy from within as an ongoing continuous process; like with natural evolution, it is very difficult to make major leaps. Although whole paradigm shifts may occur, they are rare.[9] We are typically drawing upon some preexisting set of solutions and remixing them in new ways. Evolution is typically a slow step by step process.

Enabling this process means developing open platforms that enable diverse variants to interact and cross mix. For example, the rise of the Internet and YouTube has put all our music on the same platform right next to each other, the result being a great acceleration in the production of mash-up mixes between all types of very disparate music; many of which do not work but some do. Co-working and business incubators are based on the same idea of having an open space for the interaction between disparate activities, ideas, and expertise, to enable cross-pollination.

For this reason, it is important to maintain a stock of diversity within the system for the sake of innovation, but this is not the only factor.[10] We also need to create and foster weak links between disparate domains so that we can get greater diversity of cross-mixed variants, and the diversity between these variants will be less superficial. Inside the box innovation happens by getting lots of intelligent people to focus on a problem in a lab. Real outside the box innovation happens in the real world where there are lots of weak links between disparate domains. This is less the source of innovation and more the source of innovability, which refers to the system’s ability to generate disruptive, qualitative and fundamental improvements, which enables it to undergo transformational change.


Next, these variants need to be put into their operating environment. In order to adapt, they need to be fully exposed to that environment but given sufficient time and space to develop and exhibit all of their capabilities – this may take time. It takes time to develop a new product in a market or a new industry within an economy. If we think about evolution within ecosystems, new creatures are typically fostered for a prolonged period before being exposed to the full requirements of survival. A new agile approach to product development called MVP, standing for minimum viable product, is one approach to this process whereby new very basic solutions are rapidly developed and are deployed as soon as they have the minimum requirements to function within their operating environment.[11] In this way, we can start to receive immediate feedback as to the system’s viability and possible future trajectory without the need for significant investment or foresight.

But all variants ultimately must be exposed to the same environment in order to see how well they perform, although the traditional narrative surrounding evolution is one of competition, a vision of every one against everyone else, but this is only really true in very simple relatively isolated environments. Most products, services, business or individuals within advanced economies fail or succeed within that environment, based not only on their own capabilities but also on their capacity to interoperate and coordinate with other systems. Within environments of heightened interconnectivity, this capacity for interoperability is very important.


Lastly, selection has to be performed on the set of elements based on their functionality. Ultimately, all products, services, and economic institutions have to serve some function for someone who is prepared to pay for it. In that act of paying for it, they make a vote within the process of selection and that process really plays out on millions of different balance sheets across the economy. Every time we cast our vote for a product or service, we are tipping the balance in favor of its continuing to exist, while the balance of all the other products on the shelf that we did not select is very gradually tipping in the other direction. In that simple act of choosing, some product or business has lived on to become more prevalent within the next life cycle to the system while others have come closer to being discontinued and thus becoming less prevalent in the future. In such a way the whole system adapts to its environment, without anyone saying that we should switch from typewriters to computers because they believe there is an Information Age around the corner.[12] The whole system has still managed to figure that out in a distributed fashion without anyone predicting the future or guiding the whole process.

Economic Complexity

Although the evolutionary method of development may not have some fixed goal, that is not to say that it does not result in long-term systemic transformations – quite the contrary, in fact. It is through evolution that a system goes from being simple to becoming complex. Simple systems can just pop in and out of existence, but complex systems like our global economy are built through a prolonged process of evolution. During that process, they become both more differentiated and more integrated, and this is essentially what complexity is, a system that is both differentiated and integrated. This complexity then enables the system to operate in a more advanced, broader environment.[13] This process within economic development is best illustrated with reference to the theory of economic complexity.

The theory of economic complexity postulates that the key to prosperity is in both accumulating individual capabilities and the capacity to aggregate those capabilities through networks to create complex products within a diversity of industries.[14] It starts with a recognition that underdeveloped economies know how to make only a few things while developed economies know how to make many. “Know how” is defined as an individual’s competency to perform a task. Collective know how is the capacity to perform tasks that cannot be performed by an individual. They are team efforts. No individual can play a symphony, produce a laptop or make the trains run on time. In pre-agrarian societies, an individual knows almost as much as the whole organization knows. But in advanced economies, we are able to make more things that are more complex because we all have different knowledge and we are able to create networks for integrating them into coherent functional organizations. The more individual differentiated capabilities we have and the greater our institutional capabilities to integrate them, the more complex the products and services we can make and thus the more advanced our economy is.[14]


  1. (2017). Tuvalu.santafe.edu. Retrieved 27 May 2017, from http://tuvalu.santafe.edu/~wbarthur/Papers/Comp.Econ.SFI.pdf

  2. (2017). Umass.edu. Retrieved 27 May 2017, from http://www.umass.edu/preferen/Class%20Material/Readings%20in%20Market%20Dynamics/Complexity%20Economics.pdf

  3. (2017). Economist.com. Retrieved 27 May 2017, from http://www.economist.com/blogs/freeexchange/2011/10/building-blocks-economic-growth

  4. Linear growth theories. (2017). Economicsonline.co.uk. Retrieved 27 May 2017, from http://www.economicsonline.co.uk/Global_economics/Linear_growth_theories.html

  5. Concepts: Adaptive | NECSI. (2017). Necsi.edu. Retrieved 27 May 2017, from http://www.necsi.edu/guide/concepts/adaptive.html

  6. (2017). Tuvalu.santafe.edu. Retrieved 27 May 2017, from http://tuvalu.santafe.edu/~wbarthur/Papers/Comp.Econ.SFI.pdf

  7. (2017). Economics.mit.edu. Retrieved 27 May 2017, from https://economics.mit.edu/files/1785

  8. (2017). Umass.edu. Retrieved 27 May 2017, from http://www.umass.edu/preferen/Class%20Material/Readings%20in%20Market%20Dynamics/Complexity%20Economics.pdf

  9. (2017). Tuvalu.santafe.edu. Retrieved 27 May 2017, from http://tuvalu.santafe.edu/~wbarthur/Papers/Comp.Econ.SFI.pdf

  10. The Difference. (2017). Princeton University Press. Retrieved 27 May 2017, from http://press.princeton.edu/titles/8757.html

  11. (2017). Web-foundry.co.uk. Retrieved 27 May 2017, from https://www.web-foundry.co.uk/images/assets/WF_MVP_whitepaper.pdf

  12. Quant, A. (2017). The Computer and the Economy. The Atlantic. Retrieved 27 May 2017, from https://www.theatlantic.com/magazine/archive/1997/12/the-computer-and-the-economy/377019/

  13. Differentiation and Integration in Complex Organizations on JSTOR . (2017). Jstor.org. Retrieved 27 May 2017, from http://www.jstor.org/stable/2391211?seq=1#page_scan_tab_contents

  14. (2017). Atlas.cid.harvard.edu. Retrieved 27 May 2017, from http://atlas.cid.harvard.edu/media/atlas/pdf/HarvardMIT_AtlasOfEconomicComplexity_Part_I.pdf        

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