I have been very lucky to work for an initiative termed ‘Systemic M&E’ (although we talk more about the ‘M’ than the ‘E’), focusing on ways to move away from a linear and static understanding of the systems we work in and develop tools and approaches that let us monitor changes in the complexity of the real world. Continue reading
How does systems thinking and complexity theory help us in designing a global knowledge management facility?
A global knowledge management facility has the goal to manage knowledge within a specific community of practice in order to improve the application of that knowledge in general or of a specific approach in particular. From a traditional point of view, important activities of such a facility would be to collect and codify knowledge, analyze good practices and there might even be a wish for standardization of the application of this particular knowledge or approach. In this sense, the facility can be seen as a custodian of the ‘right’ knowledge and oversee and certify the quality of its application. It would guard the rigor or ‘pureness’ of application of the specific approach with a view to preserve or improve its effectiveness and efficiency.
With this picture in mind I read a chapter in a knowledge management book that describes implications of systems thinking and complexity theory on knowledge management from an organizational perspectives (Bodhanya 2008). I would like to apply the conclusions of the article to the design of such a global knowledge management facility.
In his article, Bodhanya describes differences in characterizing knowledge that stem from a number of debates within the scientific community revolving around the interplay between ontology and epistemology or, to put it in simpler terms, between perspectives of knowledge as a thing and knowledge as a dynamic process. According to Bodhanya, the current debates in knowledge management, however, reduce this pluralism of the discussion around knowledge because the dominant discourse is based on what he calls a cognitive-possession perspective. He terms this ‘first order knowledge management’.
First order knowledge management is built on the following assumptions (Bodhanya 2008:7):
- Knowledge is reified.
- Knowledge is useful when it is objective and certain.
- Distinction between tacit and explicit knowledge.
- Knowledge may be managed through knowledge management.
- Knowledge identification is a search process.
- Knowledge construction is a process of configuration.
- Knowledge management comprises knowledge processes such as identification, generation, codification, and transfer.
- Business strategy may be formulated and implemented. This is a fundamental assumption across all strategic choice approaches to strategy and, at a minimum, will include the design, planning, positioning, and cultural schools of strategy.
- Knowledge management strategy may be formulated and implemented.
- Knowledge management strategy must be aligned to the business strategy.
The concept of first order knowledge management seems in line with the goals and traditional activities of a global knowledge management facility as described above: “Ultimately, first order KM relies on knowledge processes such as knowledge identification, generation (or more accurately configuration), codification, capture, and transfer in order to develop human and social capital, as these are considered as important in facilitating productive activity” (Bodhanya 2008:8).
In his paper, Bodhanya argues against the view of knowledge as a thing and criticizes current knowledge management of gross oversimplifications by relying on a view on knowledge as something that can be possessed. He suggests that knowledge is a much more dynamic phenomenon and suggests instead to shift the focus from knowledge to the act of knowing itself. “Knowledge is only generated in the act of knowing; everything else is information. In other words there is the perpetual potentiality for knowledge generation, but this is only transformed into actuality when information comes into contact with the human intellect. This happens in the act of knowing in the instant when there is sensemaking and interpretation. (…) [H]uman actors are constantly engaged in thought, and hence are engaged in sensemaking and interpretation at every instant, so knowledge is being regenerated afresh at every instant. This phenomenon of constant thought and action means that there is perpetual regenerating of knowledge” (Bodhanya 2008:10). Based on these insights, Bodhanya constructs a ‘second order knowledge management’ that takes into consideration the dynamic interplay between knowledge and the knower. In second order knowledge management, he points out, more attention needs to be paid to the social interactions between actors.
Bodhanya then details out how this view on knowledge and knowing is in line with systems thinking and complexity theory. An interesting debate he touches upon is the question who, from an organizational point of view, are the actors in a complex adaptive system of knowledge. The most obvious choice would be the individuals in an organization but another possibility is, for example, to see narrative themes as the actors. Bodhanya argues for a more nuanced view that includes the individuals as well as other forms of agents such as groups of individuals, departments, and human artifacts. He defines the systems of knowledge as ‘knowledge ecologies’: “A knowledge ecology is a dynamic system of heterogeneous agents that interact with each other according to their schemata. The schemata are inextricably linked to each agent’s propensity for interpretation and sensemaking on an on-going basis. Since interpretation and sensemaking are related to knowing in action, every act of interpretation and every act of sensemaking is in effect an actor creating knowledge. There are therefore multiple cognitive feedback loops being generated which in turn refresh the schemata according to which agents then act” (Bodhanya 2008:14). But he goes even further and looks for evolutionary tendencies in knowledge ecologies to an effect that knowledge structures become the primary agents that survive, vary, mutate, and are subject to retention and selection. There are, thus, various layers of interacting and interconnected complex adaptive systems with various types of actors at play, which makes the description – or prediction – of the system impossible.
Whereas first order knowledge management is based on a strategic choice view of business strategy, considerations of complexity and systems thinking show that the knowledge environment is far to complex for any one person to fully understand and, hence, to make strategic choices. This does not only change the view on knowledge management, but on business strategy itself; it shows the need for a more dynamic approach that is much more process oriented. “Alignment between business and knowledge management strategies may therefore not simply be designed and imposed, but may only be stimulated through managing organisational context and the interactions between actors within an outside the organisation. We may therefore also refer to business and knowledge management strategies as undergoing a process of co-evolution” (Bodhanya 2008:15).
What does this mean for knowledge management? First of all we have to realize that knowledge management does not have something to do with control over knowledge and its use. No single agent in a complex adaptive system can stand outside the system and direct it. “[M]anagerial orientations must shift from a preoccupation with the ordered, rational, analytical, and the fixed towards a tolerance of ambiguity, subjectivity, flux, and the transient nature of organisational life” (Bodhanya 2008:17). But this also means that there is no formula, recipe, or easy prescription on how to implement knowledge management. Rather, the conditions for the emergence of knowledge ecologies need to be developed. “The best that we can do is to facilitate rich interconnections between agents, increase agent diversity, and provide an enabling context for sensemaking and interpretation” (Bodhanya 2008:17). Bodhanya introduced an approach to second order knowledge management he calls strategic conversation, but he also points out that there is still a lot of research needed to fundamentally transform knowledge management into a systemic process co-evolving with other strategies within an organization.
One important piece of wisdom Bodhanya gives us for that journey: “As human actors and managers, we are in a sense deluded by the extent to which we think we are in control. It calls for increased humility on the part of all of us as human actors. In a systemic world, we control less than we think, because the effects of our actions are subject to many feedback loops and nonlinear responses that are outside our sphere of influence and control. (…) Our plans are merely artifacts, and to the extent that they contribute to co-evolution, they do have a valuable role. However, this may call into question our criteria for what the value of a plan is, and what constitutes a good or a bad plan” (Bodhanya 2008:19).
What does this all mean for a global knowledge management facility? Obviously, such a facility does not follow the same rules as an organization, which can be seen as complex adaptive system with a fairly obvious, if also penetrable, boundary. Knowledge sources are much more widespread and part of diverse organizations with their own agendas. One of the first insights, thus, must be that such a facility cannot exist on its own, observing, collecting knowledge, codifying it, and defining best practices, which it will then disseminate again into the system. Rather than a centralized secretarial-type entity, the facility should rather be a hub of a network of actors in the knowledge ecology with the aim to stimulate knowledge creation and exchange. As pointed out by Bodhanya, an essential step thereby is to “facilitate rich interconnections between agents, increase agent diversity, and provide an enabling context for sensemaking and interpretation” (Bodhanya 2008:17). So rather than to see the facility as a kind of library and custodian of the right kind of reified knowledge or the pure way of implementing an approach, it should much more be a place where discussions are stimulated and the knowledge is created while it is used. Thereby, diversity plays a big role. It is important to see that there is not one right way to implement an approach, but that knowledge is created from the diversity of its application.
This is just the beginning of a possible discussion and many things still need to be touched upon and many critical points in the assessment above need to be made visible and discussed. I hope, however, that this post provides some food for thought.
Reference: Shamim Bodhanya (2008): “Knowledge Management: From Management Fad to Systemic Change”. In: Abou-Zeid, El-Sayed: “Knowledge Management and Business Strategies: Theoretical Frameworks and Empirical Research.” Information Science Reference. Hershey, New York.
I want to pick some of the comments to my last post and reply to them. But instead of replying in the comment thread, I decided to write a new post.
First of all, I want to take up Shawn Cunningham’s point (who is actually the friend I was talking to who inspired the original post and he also writes a blog I like). He rightly points out the importance of the dampening feedback loops that often render our interventions toothless or return the system to its earlier stage after the project has phased out. I see this as one of the major shortcomings of current approaches in development that call themselves ‘systemic’. Just to take an example of such an approach I know fairly well: the ‘Making Markets Work for the Poor’ (M4P) approach, which is highly praised for being systemic. Although I think the approach is a valuable step towards a more systemic approach, I see many shortcomings from a systems thinking perspective. On the positive side, M4P promotes the notion of seeking change from within the system by introducing facilitation of system actors to change as the main intervention tool. Although the facilitation approach encourages practitioners to experiment with small interventions and learn from the system, the M4P approach does not include the analysis of feedback loops. Hence, many dynamics of the system, especially if they are outside the economic sphere, are not systematically assessed. They might be spotted if the implementation team is tuned to unintended effects of the interventions or effects of the system on the interventions, but that is probably the exception.
The second point I want to take up from the comments is Shawn’s point about the interest of donors and other interest groups. I think it is important to realize that the aid industry is a complex system in itself and interests are shaped by complex interactions within and between donor agencies, which usually are large bureaucratic organizations as well as by interactions between politics in and between donor countries and with ‘receiving’ countries. And there are not only the donors, there are also other interest groups that have a big influence on the aid system. So if Shawn talks about the interest of donors to have quick wins with their perceived beneficiary populations (‘the poor’, women, etc.), this is part of the dynamics of this particular system. What I mean to say is that we should not necessarily condemn the donors not to understand the need to use systemic approaches to effectively and sustainably improve the station of the poor (which is, however, probably also true to a large extend), but that they are trapped in the dynamics of their own systems.
This is a nice example, by the way, of the fractal nature of complex systems. You can always zoom further out and you will find another complex adaptive system of which the system you were looking at in the first place is only a part, i.e., the economy in which the poor participate – or also zoom further in for that matter, and you will look at the dynamics between and within poor households which are not less complex. Of course, they are in effect all part of the same system but we put some boundaries in order to delimit systems for our analysis.
The last point mentioned by Shawn about the change we want to see brings me to the topic of values that we have and the question of how far we can allow ourselves to impose our values on the system we are working with. I see this as a very delicate discussion and I am not very clear myself how to answer the question. I was discussing this question recently with another friend and he was pretty clear that we of course want for example to free women from oppression, from being stoned because their husband commits adultery, and of course we want to promote the universal declaration of human rights. But then again also in this case we have to find a way to make these changes happen from within the system and not impose these values on the system. To achieve social change is probably one of the most difficult things and the one where the systems are probably most averse to change.
An interesting aspect I want to take up from Alexis Morcrette’s comments is the problem of having multiple goals within economic development projects. He makes the following example:
(…) (1) you want the system to be more competitive as a whole (competitiveness of the system), (2) you want the participation of traditionally marginalised people in the system to be improved, in absolute numbers participating and in terms of the benefits they derive from the system (call this inclusion of the system), and finally (3) you want the participation of these traditionally marginalised people to be more self-determined/empowered (called this, for lack of a better word, equitability of the system).
Alexis identifies a need for trade-offs between these goals. This resonates with a point made by Shawn:
The third dimension is time, and it is dynamic. Here in South Africa, there is the fear that a particular group other than the intended beneficiaries would benefit in the short term, therefore paralysis ensues. Rather do nothing than tip the scale in favor of the wrong group. But this time dynamic also have a longer term dimension. Sometimes the change will happen, it will just take much longer. Or it may happen over time because some other conditions are met. Or maybe it almost happens, but because on (sic) or two factors are weak the system reverts to an earlier state. We have to remember that in most systems theories there is a recognition of the importance of the starting state of the system AND the timing of the change.
Put in other words: in the short term, there might be a need for a trade-off to be made between the three goals if we want to see changes in all three of them – they might even be mutually exclusive because – as Shawn puts it – in order to achieve one goal the scale in the other aspect needs to be tipped in favor of the ‘wrong group’, for example bigger businesses. But if we take a longer term perspective, all of these goals might be reached to a satisfactory extend. The question is: do we have a long enough breath and the courage to take the necessary steps?
This blog post is about what I see as one of the most important papers linking the complexity sciences to development and humanitarian efforts – at least it is for me personally, but I think it also takes a very important position in the discussion in general.
The paper has the title ‘Exploring the science of complexity: Ideas and implications for development and humanitarian efforts’ and is authored by Ben Ramalingam (author of the blog Aid on the Edge of Chaos) and Harry Jones with Toussaint Reba and John Young. The paper can be downloaded here.
Why do I think is the paper so important? For me personally it was the first paper I read that explicitly linked the two domains (complexity science and international development) and it does that in a very comprehensive and systematic manner.
Ramalingam and colleagues go back to the origins of complexity sciences and put it into context by showing applications in the social, political and economic realms. They unpack the complexity sciences and present them in ten key concepts divided into three sets, i.e., complexity and systems, complexity and change, and complexity and agency. Here an overview taken from p 8. of their paper:
Complexity and systems: These first three concepts relate to the features of systems which can be described as complex:
- Systems characterised by interconnected and interdependent elements and dimensions are a key starting point for understanding complexity science.
- Feedback processes crucially shape how change happens within a complex system.
- Emergence describes how the behaviour of systems emerges – often unpredictably – from the interaction of the parts, such that the whole is different to the sum of the parts.
Complexity and change: The next four concepts relate to phenomena through which complexity manifests itself:
- Within complex systems, relationships between dimensions are frequently nonlinear, i.e., when change happens, it is frequently disproportionate and unpredictable.
- Sensitivity to initial conditions highlights how small differences in the initial state of a system can lead to massive differences later; butterfly effects and bifurcations are two ways in which complex systems can change drastically over time.
- Phase space helps to build a picture of the dimensions of a system, and how they change over time. This enables understanding of how systems move and evolve over time.
- Chaos and edge of chaos describe the order underlying the seemingly random behaviours exhibited by certain complex systems.
Complexity and agency: The final three concepts relate to the notion of adaptive agents, and how their behaviours are manifested in complex systems:
- Adaptive agents react to the system and to each other, leading to a number of phenomena.
- Self-organisation characterises a particular form of emergent property that can occur in systems of adaptive agents.
- Co-evolution describes how, within a system of adaptive agents, co-evolution occurs, such that the overall system and the agents within it evolve together, or co-evolve, over time.
In great detail they explain every concept, give examples and discuss the implications of the concepts for the development system.
I like the paper because it really brings together all those important concepts in an accessible way. Although the paper is pretty long (89 pages all in all), it is not at all a boring read. In the conclusion part of the paper, the authors also describe the difficulty of presenting such an intricate matter as complexity sciences, itself being not a unified scientific discipline:
[…] it is useful to note that scientific knowledge is usually characterised with reference to the metaphor of a building. The ease with which the terms ‘foundations’, ‘pillars’ and ‘structures’ of knowledge are used indicates the prevalence of this architectural metaphor. Our difficulty was in trying to represent complexity science concepts as though they were parts of a building. They are, in fact, more like a loose network of interconnected and interdependent ideas. A more detailed look highlights conceptual linkages and interconnections between the different ideas. The best way to see how they fit together in the development and humanitarian field would be to try to apply them to a specific challenge or problem. […] Based on our reading, however, a grand edifice may never be erected along the lines of, for example, neoclassical economics. If this is the case, it may be that we need to become better accustomed to a network-oriented model of how knowledge and ideas relate to each other.
For me, it is intriguing how the science of complexity not only defies scientific practices by diverting from the pure deductive and inductive approaches and combining them but also evaded characterizations in ‘traditional’ scientific schemes such as the building mentioned above. This reminds me of the book ‘Complexity and Postmodernism’ by Paul Cilliers, which I started reading but I got stuck somewhere in the middle, overwhelmed by his theory and language. I hope that I will finish it some day and report on that here.
The authors also try to answer a number of questions around the topic of the application of complexity to development and what it means for example for international donors. A few quotes from the concluding remarks:
In our view, the value of complexity concepts are at a meta-level, in that they suggest new ways to think about problems and new questions that should be posed and answered, rather than specific concrete steps that should be taken as a result.
As well as use by implementing agencies, an understanding of complexity must also be built into the frameworks of the donors and others who hold the power to determine the shape of development interventions. This may be easier said than done – complexity requires a shift in attitudes that would not necessarily be welcome to many working in Northern agencies. For example, such a shift may require adjusting away from the ‘mechanistic’ approach to policy, or being prepared to admit that most organisations are learning about development interventions as they go along, or being transparent about the fact that taxpayers’ money may be spent on a project that does not guarantee results. It may mean having smaller, but better programmes.
At the start of our exploration, our view was simply that complexity would be a very interesting place to visit. At the end, we are of the opinion that many of us in the aid world live with complexity daily. There is a real need to start to recognise this explicitly, and try and understand and deal with this better. The science of complexity provides some valuable ideas. While it may be impossible to apply the complexity concepts comprehensively throughout the aid system, it is certainly possible and potentially very valuable to start to explore and apply them in relevant situations.
To do this, agencies first need to work to develop collective intellectual openness to ask a new, potentially valuable, but challenging set of questions of their mission and their work. Second, they
need to work to develop collective intellectual and methodological restraint to accept the limitations of a new and potentially valuable set of ideas, and not misuse or abuse them or let them become part of the ever-swinging pendulum of aid approaches. Third, they need to be humble and honest about the scope of what can be achieved through ‘outsider’ interventions, about the kinds of mistakes that are so often made, and about the reasons why such mistakes are repeated. Fourth, and perhaps most importantly, they need to develop the individual, institutional and political courage to face up to the implications.
I’d recommend anyone who works in international development and is interested in complexity to read this paper. It is a perfect entry point also for people with no background in complexity science.
I recently stumbled over a blog called Complexity Finance by a company called Rational Investment. A series of three posts which I liked was called ‘What ants can teach us about the market’. In part one, the author writes about a phenomenon that the number of ants would, given two identical and steadily replenished food sources not be divided 50/50, but rather 80/20:
Alan Kirman found some interesting behavior in the foraging activities of ants. He starts his account by citing the results of an experiment by Deneuboug et al. (1987a) and Pasteels et al. (1987) where two identical food sources were offered to ants. They were replenished so that they remained identical. Ants, after a period of time, were found not to be split 50/50 as common sense would conclude, but rather 80/20. Kirman further noted that this 80/20 split would often reverse inexplicably. This phenomenon is mirrored in studies by Becker where only one of two similar restaurants on opposite sides of the street tend to attract long lines of customers.
Apparently, this behavior is also mirrored by investors in a market.
In part three, another interesting concept is introduced: herding. Herding was identified as a common behavior in markets, responsible for creating trends.
Described as “History’s Hidden Engine”, socionomics posits that large trends in society and the market are driven by social mood. If the society at large is feeling positive, constructive behavior ensues, e.g. cooperation between governments, a rising stock market, expanding economy, box-shaped cars and brighter fashion tones. A negative mood will cause society to go to war, the stock market to decline, a recession/depression, rounder-shaped cars and darker fashion tones.
Socionomics is counter-intuitive in that most people believe events cause social mood. The stock market goes up and investors feel happy. Socionomics believes that a society that feels happy, for whatever prior cause, will cause them to buy stocks. It is the mood that causes the event. This mood is generated and reinforced through the herding mechanism.
Herding behavior is simply acting the way others do. It is a type of sampling heuristic and, like cognitive biases, is triggered in times of uncertainty. When uncertain about what to do, most will default to following the actions of others. The socionomic model of herding describes it as “a model of unconscious, prerational herding behavior that posits endogenous dynamics that have evolved in homogenous groups of humans in contexts of uncertainty, while eschewing the traditional economic assumptions of equilibrium and utility-maximization.”
I wonder how this herding behavior could be used in the work of developing markets for the poor in developing countries. I do recognize one type of herding in these contexts that I often don’t see as particularly helpful, but a very understandable behavior: all people in a region, market, village, etc. do the same thing, regardless whether it is particularly beneficial or profitable. In general, diversification would not only lead to higher profits by tapping new markets, but also to a higher degree of resilience by not depend on only one product. A negative instance of herding?
Maybe the increasing interest of companies (and investors?) in social business can be seen as a positive type of herding that needs to be better exploited.
At the moment, I am reading and thinking a lot about complexity and how it could be applied to development and enrich the Systems Dynamics Analysis I am using in my work. Today, I read an article by David J. Snowden and Mary E. Boone titled “A Leader’s Framework for Decision Making” and published in the Harvard Business Review back in November 2007. Snowden and Boone added a box to their article in which they describe the main characteristics of complex systems. I found this to be a very comprehensive and yet understandable description an that’s why I want to share it here.
Here you go:
- It [a complex system] involves large numbers of interacting elements.
- The interactions are nonlinear, and minor changes can produce disproportionately major consequences.
- The system is dynamic, the whole is greater than the sum of its parts, and solutions can’t be imposed; rather, they arise from the circumstances. This is frequently referred to as emergence.
- The system has a history, and the past is integrated with the present; the elements evolve with one another and with the environment; and evolution is irreversible.
- Though a complex system may, in retrospect, appear to be ordered and predictable, hindsight does not lead to foresight because the external conditions and systems constantly change.
- Unlike in ordered systems (where the system constrains the agents), or chaotic systems (where there are no constraints), in a complex system the agents and the system constrain one another, especially over time. This means that we cannot forecast or predict what will happen.
Moreover, Snowden and Boon differentiate between two types of complex systems. In the first type, the individual actors or ‘agents’ in the system strictly follow predefined, simple rules, such as birds flying in a flock or ants in an ant colony. In the second type, however, the individual agents are not animals but humans and, hence, follow their own reasoning according to the relevant context and situation.
Consider the following ways in which humans are distinct from other animals:
- They have multiple identities and can fluidly switch between them without conscious thought. (For example, a person can be a respected member of the community as well as a terrorist.)
- They make decisions based on past patterns of success and failure, rather than on logical, definable rules.
- They can, in certain circumstances, purposefully change the systems in which they operate to equilibrium states (think of a Six Sigma project) in order to create predictable outcomes.
So where does this lead us in our everyday work? Snowden and Boone also offer a number of tools to manage complex situation out of which I want to pick two that I find are relevant for the work in development projects:
- Open up the discussion. Complex contexts require more interactive communication than any of the other domains. Large group methods (LGMs), for instance, are efficient approaches to initiating democratic, interactive, multidirectional discussion sessions. Here, people generate innovative ideas that help leaders with development and execution of complex decisions and strategies. (…)
- Stimulate attractors. Attractors are phenomena that arise when small stimuli and probes (whether from leaders or others) resonate with people. As attractors gain momentum, they provide structure and coherence. (…)
The first point clearly points out that participation still is a very important part of every development project that really wants to make a difference. In the end we have to be aware that it is not us that is changing the system, but we are merely working to enable the system to move itself towards a more favorable state (who defines whether this state is more favorable remains another point to discuss and influences a lot whether the system is actually moving in that direction).
The second point is important to recognize that we always have to look for things that work or try to start small pilots and see whether they work and amplify them. This is essentially the recognition that change to a system happens from within a system.
I will continue blogging about complexity, many things are going on in that field. So stay tuned.