Tag: evolutionary economics

The modules that evolve

This is the third post in this series about economic evolution. In this post, I want to focus more on the modules that the evolutionary algorithm act upon that I mentioned in the previous post.

Ideas are the most fluffy evolutionary material that spreads between people and organisations. These ideas spread via formal means (like education, formal communications, management decisions or regulations) but also via less formal means like the media, stories, people moving between places and social networks. We pick up ideas as we move around in society, and often we combine these ideas with our own to create completely new idea combinations. I always ask people I interview where they got their ideas for innovations from, and I have heard the most amazing stories of how they took an idea developed in a completely different context and made it work in their own situation.

Tracking how ideas spread in society is tricky, partially because we cannot always remember where we heard or saw something that triggered a thought.

In the first post, I explained that Nelson and Winter (1982:14) preferred to use “routines” instead of “ideas” in their theory as the material that the evolutionary algorithm act upon. Routines are relatively stable ways of arranging physical and social technologies to perform certain functions. Even if they can come about in an arbitrary form, organisations tend to replicate routines internally, and other organisations also copy and adapt routines that appear to work well.

Eric Beinhocker (see the 2nd post) argues for using “modules” as the primary material on which the evolutionary algorithm acts. He agrees with the co-evolution between physical and social technologies but adds that there is a third design space, namely business plans.

“A module is a component of a business plan that has provided in the past, or could provide in the future, a basis for differential selection between business in a competitive environment.” A module is made up of configurations of routines from the physical, social and business plan design spaces.

Beinhocker, 2007:283

Continuous decisions about the composition of these modules must be made in any organisation or company. These routines and modules can be pretty unstructured, but the theory holds that the better-performing modules will be replicated within and beyond the organisation. In contrast, the less-efficient modules or routines will become obsolete when replicated less. However, even inefficient or unproductive modules can persist due to stubbornness, sunk costs, pride or ignorance.

A challenge we face is that the many internal marketplaces (within organisations) where different concepts or module formulations are pitched against each other do not work effectively. In addition, companies relying on inefficient module configurations can still thrive in uncompetitive marketplaces because the markets cannot select better-performing alternatives.

Over the years, I responded to this issue through the instigating innovation series and my teaching at various business schools.

Those organisations that constantly develop or search for new modules or tinker with their existing modules because of anticipated changes in the broader environment can be described as having dynamic capabilities. Dynamic capabilities cannot be bought, it is built through the intentional behaviour of entrepreneurs, managers and employees. David Teece is one of my favourite scholars studying dynamic capabilities, and here is his definition:

Dynamic Capabilities are the firm’s ability to integrate, build, and reconfigure internal and external resources/competences to address and shape rapidly changing business environments. 

Teece et al., 1997, 1990

Teece must often repeat building dynamic capabilities is about achieving abnormal results over the long term. The capabilities part is about the investments in abilities, skills, routines and relationships that are enduring AND that are distinct from what competitors may be doing. The dynamic part refers to the ability to adapt and proactively create market conditions that favour the company. It is about an internal culture of change, experimentation and recognising what works (and what does not work). Why I took this detour is that we need to find ways to foster dynamic capabilities in developing countries in both the public and the private sectors. We have too few organisations actively trying to create new modules that are then adapted based on how they perform in the marketplace.

Back to the main thread. Investments in modules create path dependency, especially when all the investments are along a certain trajectory. In the software and services sectors, companies can change direction faster than in the manufacturing or public sectors. This is mainly due to the way fixed capital is invested in plant, equipment and infrastructure. Once committed to a specific configuration of modules, it may cost a lot of money to change direction.

Whereas routines can often be observed and replicated between companies or contexts, modules are often harder to observe and copy. For example, we can visit a factory and see some physical and social technologies arranged in routines. We can observe how raw material, processes, people and workflows are organised on the factory floor. However, the modular knowledge units combining physical, social and business plans are harder to observe or measure. The hidden parts include the internal incentive structure to innovate and resolve problems, the past learning about what did not work and should never be attempted again, etc., how the factory is connected to suppliers, clients and headquarters, and so on. Some parts of the modules are harder to observe because they weave together past decisions, strategies, funding mechanisms, and future expectations using combinations of physical and social technologies. The machine standing there or how people work together is just the visible tip of the module (or configuration of modules).

The irony is that even management may not realise how much tacit knowledge is involved in the logic that weaves the many different modules combined in a single unit of an enterprise in the modern economy.

From an innovation perspective, there is something else to note about how these modules are created, adapted and disseminated in economic system. The ability to identify, absorb, create and deploy these modules is cumulative. Think of lego bricks. The more modules a specific company has access to or has tried before, the more variations it can create, and the more creative solutions it can select to try in the marketplace.

When this happens over many companies in the economy, markets and users can select from more variety, and better ideas are replicated and amplified in the economy. We know from the research by Cesar Hidalgo, Ricardo Hausmann and the Centre for International Development at Harvard, that these winning knowledge modules can be mapped in the Productspace. (I have a hidden section on my blog site where I have documented some of my experiments and visualisations with this approach – you can access it here).

The experience of selecting combinations or transferring modules to other business areas is cumulative. Companies building modules drawing on different or technological knowledge are better positioned to create entirely new innovative architectures because they can draw on modules from divergent knowledge domains. In contrast, companies that can only manage knowledge modules drawing on narrow knowledge domains are more likely to become specialists. The narrower the range of modules, the higher the risk of being disrupted by other competitors that can build modules on a wider front.

It is not only companies and public organisations that build up these modules. Industries can build modules. Universities play a key role in introducing recent or reliable modules into society. Some modules are described in sufficient detail that can spread via blueprints, technical documentation or software code. Members of online communities can together develop knowledge modules in a distributed way. Just think of the power of Linux or other forms of open-source software that is developed in a distributed way.

Modules can also be accumulated in a society, like the knowledge of how to organise a certain kind of festival, or the modules accumulated through dealing with certain phenomena from the environment. When people change jobs or move from a workplace, they carry with them some understanding of the modules and their sub-routines with them. However, turning some of these modules into a business unit, product or process might require investment in developing the missing business plan areas.

In some locations, office parks or regions, knowledge about certain modules may be developed or may flow more easily over organisational boundaries. Of course, this process may be fostered by developing unique public (or private) infrastructure and through social networks.

I’ll go ahead and conclude. Entrepreneurs, managers, investors and public officials have to intentionally decide where to build modules in their organisations. These modules will typically combine elements from physical and social technology design spaces with business plans. Modules will evolve as they are selected by the organisation (management) and by the market. These modules are cumulative, those that have access to more can also create more variations. Those who draw their modules together from more divergent knowledge domains may have a long-term advantage over those who specialise in building capabilities in a narrower field. Dynamic capabilities are about intentionally investing in capabilities, and continuously evaluating, adjusting and refining existing modules with new ideas from beyond the organisation. Not all the modules originate in a firm, some are present in a community, or a network or are drawn from other companies or contexts. This is easier in some places than in others.

Some questions to explore in your own context:

  • In the industries or domains that you work in, what are the modules that you can identify?
  • How are they changing?
  • Who are the pioneers that are accumulating modules from divergent knowledge domains?
  • How do they compare to those mainly focused on developing modules in a narrower field?
  • Which public and private organisations have built up dynamic capabilities? In other words, they are constantly reflecting on their performance, and constantly working on building capabilities based on their understanding of their own situation in relation to changes in the broader context. How are these organisations managed, and how does their performance compare to more conventional organisations?
  • How are the accumulation and adaptation of modules fostered or promoted in certain areas, knowledge domains or industries?

Please share your thoughts in the comments, or drop me an email.

Image credit

The image at the top of this blog post was created by DALL-E of OpenAI. asked DALLE to create a picture of a topographical landscape with valleys and peaks.

PS.

Here in South Africa, we are all developing modules of how to cope with electricity blackouts (Our government calls it “load-shedding”, which is an excellent way of describing institutionalised incompetence).

Sources:

Beinhocker, E.D. 2007.  The origin of wealth. Evolution, complexity, and radical remaking of economics`. London: Random House.

Teece, D.J., Pisano, G. and Shuen, A. 1997.  Dynamic capabilities and strategic management. Strategic Management Journal,  pp. 509-533.

Teece, D.J. 2019.  A capability theory of the firm: An economics and (strategic) management perspective. New Zealand Economic Papers, 53.

Further reading:

If you have access to journal databases, you should also look up the fantastic work of Giovani Dosi, and also Richard R Nelson and B.A Lundvall.

Updated on 5 March 2023 with minor corrections

How technologies evolve

In my previous post, I introduced the general evolutionary algorithm of variety creation, selection and amplification.

I intentionally remained vague about what the evolutionary algorithm act upon by referring to “ideas”. In this post I will be more specific about what kind of ideas evolution acts on.

Nelson and Winter (1982) argued that the material that evolution acts upon are formal and informal routines:

“ We use this term to include characteristics of firms that range from well-specified technical routines for producing things, through procedures for hiring and firing, ordering new inventory, or stepping up production of items in high demand, to policies regarding investment, research and development, advertising, business strategy about product diversification and overseas investment. In our evolutionary theory, these routines play the role that genes play in biological evolutionary theory

(Nelson and Winter, 1982:14)

These routines could emerge through serendipity or they could be the result of the creative efforts of individuals or teams. They could originate in isolation, but very often they are combinations of old and new, or combinations of internal and external ideas tried in a local context. These routines could be arranged around particular equipment or processes, or they could be norms about how people in a workplace treat each other.

These routines become part of the technology available to the firm. Good ideas that seem to work better than alternative arrangements typically spread between organisations and teams through imitation, social networks or the movement of people. Sometimes ideas are spread by word of mouth, other times through text or the media, and in some cases through mimicking or copying (or reverse engineering). Sometimes a confluence of factors makes the same ideas or routines emerge in many different organisations at the same time. Think back to how we all had to figure out how to get our work done during the Covid lockdowns.

Within organisations, the ideas and the routines that are selected are chosen by how they contribute to the objectives of the organisation. For most companies, the contribution of these routines to profitability is important, but that is not the only criteria. External and internal incentives will shape what is deemed the selection and retention criteria. I have visited enough companies to know that profitability is not the only criteria. Within companies, there are marketplaces for ideas. If somebody makes an suggestion about an improvement or adjustment management and peers might select those ideas, or they may be rejected. But let me not digress.

Ultimately, all the routines and ideas that a company create, adapt and combine (through its internal idea markets) result in products, services and offerings that are either selected or rejected by external markets.

But we know that organisations cannot only achieve their objectives with routines. I again turn to Nelson and Winter (1982) who argued that there is a co-evolution between two different kinds of technological spaces:

  • Physical technologies are what we often think of as technology. It is the artefacts, equipment, infrastructure and even code that are used to perform a function or to harness natural phenomena to achieve a specific outcome. These technologies are methods and processes for transforming matter, energy and information from one state into another in pursuit of a particular outcome. A physical technology is not only an artefact, it also includes the designs, instructions and code needed to make it, maintain and use it. Physical technologies are typically modular and often interdependent on other physical technologies. For instance, my smartphone depends on other technologies to function like a mobile network and the electricity distribution system.
  • Social technologies are methods, designs and arrangements for organising people in pursuit of a goal or objective. Social technologies smooth the way for information to be exchanged, priorities to be set, or unexpected situations to be dealt with. Social technologies enable (or disable) learning and adapting to changes in the environment. Organising people into a hierarchy, creating and enforcing regulations or arranging marketplaces are all social technologies. Like physical technologies, social technologies are modular and cumulative. Each new knowledge module or routine that is created opens up new further opportunities for a variety of new creations.

This co-evolution within an organisation also co-evolves with the physical and social technology spaces beyond the organisation. For instance, the social technologies beyond the firm like the regulatory frameworks, the incentives in the economy to innovate, or the tolerance of failure or uncertainty will influence the physical and social technologies available within the firm.

In the same way, the physical technologies beyond the firm, like the infrastructure, access to scarce equipment or expertise or access to required inputs will also shape the choices of physical and social technologies available to the leadership.

The point is that the social and physical technologies harnessed in any organisation is not isolated from the social and physical technologies in the environment around the firm. Ideas cross over the permeable boundaries of the organisation.

Of course, we expect innovative leaders to create internal environments that are much more conducive than external environments around their firms. However, we know how hard it is to get this right. In many developing countries, leaders of companies (but also government departments) have to contend with a wide range of challenges that make the deployment of more recent physical and social technologies very hard – largely because of a lack of diversity and depth in the social and physical technologies beyond their organisation. I will come back to this in a moment.

In 2006, Eric Beinhocker published The Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics. It is one of the books that have influenced my thinking the most. In his book, Beinhocker reframes the social and physical spaces introduced by Nelson and Winter as “design spaces” within which entrepreneurs and leaders have to make choices based on their resources, their capabilities, the external environment and the markets they are in.

Beinhocker also introduces a third design space that he calls the Business Plan design space. This is where entrepreneurs, leaders and innovators have to meld physical and social technologies together under a strategy, and then create routines that express the resulting design into the economic world. This strategy must include ways to raise finance, attract talent, set priorities, allocate resources and create an internal environment that is typically distinct from the external environment. From my experience of working with innovative teams, I know that this business plan space could also be described as an “entrepreneurial technology” because. Just like the other two design spaces, entrepreneurial technologies are created by combining knowledge modules, insights, talent, networks and competencies into new configurations.

So now we have three design spaces that co-evolve in a given organisation. There are physical technologies, social technologies and business plans/entrepreneurial technologies. At the same time, this co-evolution in the organisation also co-evolves with the design spaces beyond the firm. The inside of the organisation exchanges genetic material with the design spaces in the immediate and even more distant design spaces.

To tie this back to the evolutionary algorithm, within each of these three design spaces, variety is created, selections are made, and “good” ideas are amplified. A “good” idea meets the requirements of the internal idea markets within organisations and eventually must also meet the fitness criteria in the marketplace.

Innovation is not only about designing better physical technologies, we also need to foster innovation in the other design spaces within and beyond firms. It is very hard (I would go as far as claiming it is impossible) to successfully innovate in one design space without affecting the innovation potential in the other design spaces.

At the same time, development practitioners must pay attention to how the design spaces within organisations affect or interact with the design spaces beyond firms. This is where the co-evolution of companies, public organisations, universities and other social organisations and the physical infrastructure co-evolve.

Sources:

Beinhocker, E.D. 2007.  The origin of wealth. Evolution, complexity, and the radical remaking of economics. London: Random House.

Nelson, R.R. and Winter, S.G. 1982.  An Evolutionary Theory of Economic Change. Cambridge, MA: Belknap Press of Harvard University Press.

Evolution in the economy

Economic evolution is often a topic in conversations with the teams I am coaching and the leaders I advise. It is a simple idea to explain, yet it allows for a much deeper exploration of why and how economies and organisations change. Even leaders without a background in economics or innovation can see the role they play in promoting innovation and economic development.

Evolution is a general-purpose and potent algorithm for finding innovative solutions to complex problems. It describes a naturally unfolding process in the economy that plays out at different levels as people, individually and collectively, search for and try new ideas or modifications of what they already know. At its core, evolution is an iterative process of creating variety and selecting designs that are fit for purpose and then amplifying these by adapting resource flows. While in nature, fitness is determined by the environment, in economies, fitness can be intentionally influenced by human actors.

The idea that economies evolve continuously is not new. The term “evolutionary economics” was coined by Thorstein Veblen (1857-1929) already in 1898. Earlier, Karl Marx and Adam Smith raised issues that are now seen as part of the evolutionary economics school. Joseph Schumpeter’s theories of economic development had a strong evolutionary perspective. Possibly the Schumpetarian idea that is best known beyond economics is his suggestion that entrepreneurs introduce innovations that creatively destroy the equilibrium created by the predominant arrangements. Much later, Richard R Nelson and Sidney G Winter’s book An Evolutionary Theory of Economic Change (1982) was a seminal work that marked a renaissance of evolutionary economics.

Below is a simple illustration of the evolutionary algorithm.

The evolutionary algorithm works as follows:

  • An innovator or a team creates some variation based on existing ideas. The new idea is often created by recombining what is already known (knowledge and technology is cumulative) with some new insights. Or perhaps they figure out the idea based on their understanding of a given situation (experience is also cumulative). It may even be that the variation is created through serendipity. The critical point is that the variety of possible solutions or stock of ideas increases, irrespective of whether there is currently demand for any of these ideas. For the variation stage to be complete, the idea must either be recognised by the innovator as worthy of further pursuit or knowledge of the variation must spread to others.
  • A few pioneering buyers, managers, investors, or other innovators then select an idea because it can address a need, or solve a problem, or plugs a gap in a given context. Selection implies that resources, attention or access to complimentary knowledge or networks are made available, leading to the concept’s further development. Choosing an idea that is different and unproven is a risk, but the people making the selection somehow recognise the idea’s potential or the limitations of existing alternatives. Innovative ideas are often further developed because they are selected. My late business partner Jorg Meyer-Stamer constantly reminded me “that technologies become efficient because they are chosen; technologies are hardly chosen because they are already efficient”. As the idea attracts more resources, funds, interest, talent and pioneering buyers, it becomes easier for others to select it as the concept is refined. At this point, other innovators may enter and create more complimentary variety, which makes selection even more likely as the ecosystem of related and complimentary solutions becomes more established.
  • At a certain point, the idea becomes amplified in the economy or the system beyond the ideas or designs of the original innovators. Where those doing the selection in the previous phase were taking a risk, in the amplification stage the risks are much lower as the innovative idea is understood better, is more credible and has more support.

This evolutionary algorithm plays out in the marketplace and is fuelled by incentives that shapes each stage. It also plays out within organisations where different ideas compete for resources.

There are ways that we can shape the incentives in all three phases of the algorithm.

  • We can figure out what incentives dampen variety creation or which incentives can be amplified to encourage learning about and exploring possible alternative arrangements.
  • We can explore how we can tilt selection incentives away from innovations that are less desirable towards more desirable solutions.
  • We can explore how good ideas that have already been selected (and thus developed) can be amplified.

I know that the incentives are often understood to be financial. But remember that recognition, being able to contribute, using one’s talent or simply solving a puzzle are also important social incentives. In the same way, fear of failure or ridicule, not having the resources needed, or not having the time or the permission to solve a problem are incentives that hamper innovation.

I want to end this post with just a provocative question. The institutions in our economies and the rules/cultures in our organisations are already shaping the algorithm. What innovations and alternatives are the algorithms in your system incentivising, selecting and amplifying? I would love to hear your reflection on this question.

If you do not want to post it in the comments, then send me an email or reach me on twitter.

Credits: The ideas in this post are inspired by many conversations with Marcus Jenal over the last ten years. In 2015, we had the privilege of deep diving into evolutionary thinking and its applications to economic development in a project funded by the BEAM Exchange. The ideas we have explored together have shaped my view of organisations, markets and how societies evolve.

Local economic development as an evolutionary process

Modern evolutionary economics is about 20 years old now, and many research programmes continue to add to the content of the subject. I think that development practitioners have a lot to learn from this subject. When we work at the local level, with local stakeholders and local resources, we are often confronted by the failures of traditional economic models (for instance the obsession with supply and demand). For instance, traditional economics often focus on distribution or allocation of wealth, while in evolutionary economics the focus is more on wealth creation. Traditional economic models assume that you can use the data of the past to make reliable predictions about the future. Just this simple insight will already change many LED approaches that emphasize working with the youth and the marginalised (solving an allocation problem) towards understanding the systemic interaction of economic technologies, social technologies and physical technologies that co-evolve to create wealth.

To be more precise, an economy should be recognised as a complex adaptive system (Beinhocker, 2007; Ramalingham, Jones, Reba and Young, 2008). This means that the economy is a system of interacting agents that adapt to each other and their environment in a complex way. Complex adaptive systems are sub-systems of open systems. It recognises that change and advancement are forces within the system created by the agents, and that it takes energy to create and process information, and to create order.

Dosi and Nelson (1994) explains that “evolutionary” implies a class of theories that tries to explain the movement or change of something over time. It furthermore involves both random elements which generate or renew some variables, as well as mechanisms that systematically create variation. Central to these theories are the concepts of deductive and experimental learning and discovery.

Beinhocker explains a simple formula that is common to all evolutionary systems. Firstly, a system needs to create variety (for instance through many innovators trying new things), and then there must be some selection or fitness criteria (often this is provided by markets). Next there is a selection process, where the ‘best’ or rather most-suitable designs are selected, and thereafter these choices are amplified or repeated (also known as imitated).

So if you think of your local economy, then consider how certain businesses came about. The variety of businesses is a direct result of novelty or variety creation, and how they ‘fit’ to the criteria of local consumers,resulting in these business models being ‘chosen’. Every now and then, a business person with a new or different idea comes along, and this in many cases may even result in local consumers changing their fitness criteria. This describes a process where economic resources (as well as labour and technology) are continuously being allocated to those who are able to combine or create new ideas, new products, and new business models.

In the next few posts I will try to delve deeper into this topic, as I believe that it holds many important insights to why local economies grow in such an unpredictable and dynamic way, and why so few local governments or organised business in Southern Africa struggle to have any real positive and leveraged effect on local economies.

References and additional reading:

BEINHOCKER, E.D. 2007.  The origin of wealth. Evolution, complexity, and radical remaking of economics`. London: Random House.

DOSI, G. & NELSON, R.R. 1994.  An Introduction to Evolutionary Theories in Economics. Journal of Evolutionary Economics, Vol. 4(3).

NELSON, R.R. 1995.  Co-evolution of industry structure, technology and supporting Institutions, and the making of comparitive advantage. International Journal of the Economics of Busienss, Vol. 2(2) pp:171-184.

RAMALINGHAM, B., JONES, H., REBA, T. & YOUNG, J. 2008. Exploring the science of complexity. Ideas and implications for development and humanitarian efforts.  Working Paper 285, London: Overseas Development Institute.