Thinking out loud: Is aid systematically unsystemic?

Originally published on 26 August 2013, edited and republished on 23 July 2019

I am taking a risk with this post, but I am sure my regular readers will bear with me as I think out loud. I have been fortunate in the past few months to have been able to focus my praxis almost completely on the topic of complexity and systems thinking in development, with a particular emphasis on innovation systems and the technological evolution of industries and regions. Shifting from advocating for a different approach to actually assisting with the shift has been a really exciting venture.

Disclaimer: These are my own opinions and do not reflect Mesopartner’s views. 

Systems and complexity thinking is now central to Mesopartner’s,  work (see our Systemic-Insight theme page). Not surprisingly, my main customers for working on systems and complexity are not those involved with development programmes per se, but meso level institutions tasked with supporting industries and sub-sectors to innovate, grow, compete and increase employment. It is no secret to most of you that I am very disillusioned with aid and development cooperation at the moment.

When you dive into the literature on complex adaptive systems and how they tend to function naturally, you cannot help but wonder whether aid and development cooperation is in a very systematic way trying to fight the very things that make economic systems so amazing. There are several universal phenomena about complex systems that seem to be ignored (I will highlight just a few that I found personally the most challenging to deal with in my work):

  • In complex systems, resources are allocated (or taken up) by those best able to make sense of the situation, using a combination of knowledge, resources and past experience. Marcus Jenal reminded me that people also exercise power and use their influence to take up these resources. It is a reinforcing loop referred to by systems thinkers such as Donnella Meadows as “strength to the strong”. It is even mentioned in the Bible and in other spiritual and philosophical texts. This presents us with the first dilemma as we can often sense that the way resources are distributed is not fair or just, and that many agents in the system will resist any attempt at redistribution.
  • Even when systems become unstable, they do tend to find a kind of equilibrium range. This means that systems will by their very nature resist our interventions as they strive towards some natural range.
  • When a system remains in a suboptimal state (in our judgement it is not delivering what we think it should deliver), we can almost be certain that somebody somewhere is using their power or influence to keep the system in a suboptimal state to extract rents from the situation. It is unnatural for systems to remain “broken” over an extended period without someone influential in the system benefitting from it.
  • Related to the previous point, the ability of an external agent to assess whether a system is broken is debatable and not questioned often enough. Even if the actors in the system believe it is broken, the system might just be behaving in a very natural way. Examples can be found in finance (who wants to lend money to potential losers when they can guarantee returns rather by lending to potential winners) or in agriculture (brokers overcome scale and uncertainty by aggregating volumes, and in return for their risk they earn good profits). There is a second dimension to this: if we ask the actors in the system what is bothering them, they will in most cases shift the blame for their decision or indecision somewhere else.
  • What is good for the system is not always good for the agents in the system and the other way around. An example is that strategies to promote exports or employment growth might actually have a negative effect on many small enterprises while having a positive effect on medium and large-sized enterprises. Or strengthening smaller enterprises might have a negative effect on the current account (through increased imports) while at the same increasing jobs in the service sector.
  • One of the weakest points of influence in a system is the actor, while one of the strongest leverage points is the purpose (often not articulated formally) and the rules (social institutions) of the system. Although it is much harder to change, the purpose of the system is the most important leverage point, followed by rules and the nature and frequency of interrelations. Yet most development programmes target particular actors, ignoring the implicit or unexpressed meta-purpose of the system. Furthermore, most measurement systems measure outputs at the level of the actor, instead of monitoring behaviour in the system. Behaviour (informed by social rules) is a better (yet harder to quantify) measure as it tells us something about the purpose, the meta culture and actors’ attitude towards his or her own health and the health of the systems. This is also a characteristic that I find personally challenging, as it takes time to change the purpose and the rules of the system. In many cases, people simply cannot even explain the purpose of their systems or the rules they follow, which are instinctively based on their values. Changing the purpose of a system requires people to change their priorities, their values, the way to judge success (or failure) and the way they perceive their role in a system or society.
  • There is a relationship between the actors and their artifacts (equipment, assets, machines, tools, etc,). Old, broken or outdated equipment is a symptom of old, broken and outdated business models implemented or executed by the actors. Somehow this is made possible by both the social context and the social as well as the economic technologies. Giving an actor a new artifact (equipment) does not necessarily change the system in the long term, as the actor is still the same. I have often witnessed how an old factory with outdated equipment but that is managed properly can outperform a modern factory run by incompetent management (management itself is an economic and social technology). Providing incentives for an existing business to buy new equipment is to address a symptom and not a cause of sustained underperformance. But an existing actor with a new attitude, better information or different behaviour is able to use existing artifacts more effectively. In the real world, this means that upgrading the equipment in a business is the wrong place to start. Rather promote new entrepreneurs or people who can better use the existing equipment. Physical technologies co-evolve with economic and social technologies; we are not dealing with an equipment problem only.

How do we get development programmes to embrace some of the strengths of complex systems? For example:

  • In complex systems, agents make decisions in a decentralised way. They make these decisions based on their perceptions of their environment and their perceptions of alternative futures, based on the information at their disposal. Thus flooding the system with valid information and promoting the view of how new knowledge or technologies can increase returns is much more valuable than physically trying to fix the actor. Education, technology demonstration and supporting experimentation are ways to provide entrepreneurs with better information to support decision making. It is often very difficult in developing countries to find this type of information which is needed for decision making support by entrepreneurs or firms.
  • A complex system exists of agents(or actors)and their artifacts, the interrelations between the agents and their common (system) purpose. Often in development, we describe only the actors, while ignoring the fact that a system has other elements that are important to include in our description. Already describing a target system as “small farmers using outdated equipment advocating for state protection” is already a much better description of a typical system, as opposed to “poor small farmers who are suffering”. We can also not just focus on “technology” without considering social and economic technologies.
  • In complex systems, the behaviour of the system emerges from the behaviour of all the elements in the systemThis emerging behaviour, in turn, has an effect on all the elements in the system, which gives rise to complexity. In complex systems there are strong feedback loops between the elements in the system and the overall observed behaviour or purpose of the system. As economies become more complex, and as inter-dependencies between different systems increase, so too it becomes more difficult to predict or anticipate how a system will change, adapt or respond to a change. This means that we have to be more sensitive to the feedback loops. Our greatest weakness is that we are outside the system, but this can also be turned into a strength if we use our distance to provide a neutral perspective on how the system seems to be behaving.
  • Even if we acknowledge that complex systems also frequently resist change, it is necessary to acknowledge that complex systems are very sensitive to small changes. Can we identify the areas where the system is least resistant to change? Can we support small safe-to-fail experiments in the areas where the risks seem high? Can we help the system to develop alternative paths that the actors can try out? Let us make sure that we increase the options available to the stakeholders and NOT fall in the trap of pushing stakeholders to adopt a “silver bullet” approach.
  • In complex systems, proximity (closeness) and connectivity (social networks) really matter. Every agent is connected to other agents and to the greater system in many different ways, and each agent typically affects every other agent through their effect on the system and their close neighbours in the system. The behaviour of any given actor affects the perspective and behaviour of other actors who are close to them. This means that we should also work with the existing structures and not create new artificial networks. If your first instinct is that the stakeholders are poorly coordinated and therefore that more coordination is needed, then you are pushing the system in the wrong direction.
  • The starting state really matters. And so does the development or evolutionary past of the system. The history of the system is still very evident in the system, and it affects how the system responds to choices, opportunities, failures and challenges. Thus the future of the system is affected by its past in unpredictable ways. This is why a solution that works in one place will almost certainly not work in another.
  • In complex systems, correlations may be strong, but causation is weak or hard to identify with certainty. In fact, what caused a particular response in the past might have no effect in the future, so perhaps trying to understand causation is in itself a future exercise. It is often impossible to figure out cause and effect and its direction in a complex system. We should not propose interventions with “guaranteed” linear outcomes. We should rather use our resources to assist developing countries to try different ideas by assisting in the creation of multiple viable paths. This is a task with very uncertain outcomes, which is exactly why our counterparts don’t pursue these risky options.

For me, development cooperation and aid need to embrace the principles of complex systems. Dave Snowden, in a Cynefin training arranged by Mesopartner in 2013, commented: “you absorb complexity, you don’t delude yourself into thinking you can eliminate it”. What I am really surprised at is how my clients in the public sector meso institutions and in the private sector find it easy to embrace the principles of complexity, while development programmes resist these same ideas. For so many of the private sector people with whom I am now working, these principles are useful as they help to explain why certain well-intentioned interventions did not yield the expected results.

My frustration is that when we discuss these same principles with aid partners or development cooperation programmes which support the very same institutions with which I am working, they find many excuses for not embracing these principles. For instance, last week I was told that “we need a clear project plan (a.k.a. an impact chain), based on a detailed assessment so that we can precisely measure our impact. We need to clearly state and then work towards our indicators so that we have evidence that we have achieved change”. Read that sentence again and count the number of times “we” and “our” are used. It seems to me that development is about delivery or applying resources and not about supporting the evolution and expansion of complex economic systems in developing countries. Never mind how sceptical development organisations are about the insights of local stakeholders as to what is really going on in the system, and how confident they are that they know exactly what the problem and the solution are.

I cannot help but wonder whether aid and development cooperation is systemically unsystemic.

23-07-2019 comment by Shawn. I decided to re-publish this post as I still find the same patterns in development. As a team, Mesopartner has moved deep into complexity thinking and have made profound changes to our consulting practice, research themes and capacity building events. Yet, much remains to be done. We are constantly also challenged by our clients in development who have to meet their funder’s requirements for predictable and quantifiable results, despite the people in these development organisations knowing that their interventions might not be addressing the real needs in a developing country.

Link to evolution for everyone

Thank you Michael Meadon for sending me the link to the Evolution for Everyone blogsite by David Sloan Wilson. In a series of blog articles David describes evolution in economics from different perspectives. Reading these articles made me realise why local stakeholder groups are so ineffective in promoting or driving local economic growth. Very often the social dynamics and social power plays are ignored. We have to find better ways of recognising local initiatives, but we also have to be aware of the social selection process that allows for this emergence.

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.

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