Inspiration to write about something can sometimes be hard to find. That’s what’s happened to me this year. For whatever reason, writing on this blog didn’t happen at all. Fortunately inspiration is best found when you’re not looking for it, thanks to Chris Jones while mentioning his latest blogpost. Chris wrote about science and philosophy. He argues for a common ground called complexity. Interesting post, I would recommend anyone to read it fully. It was this post that made me think about the importance of philosophy in many fields. My reply on Chris’ post was the following:
Science is timely, philosophy is timeless. What’s true now in science can be false tomorrow. That’s a fact. In philosophy there is no true or false. What’s true in situation A, can be false in situation B. Differences in culture, beliefs, age, etc. defines what’s true or not in philosophy, and in general this diversity in thinking is considered a richness for many of us. It enables us to change perspective and rethink theories or ‘facts’ that can lead to other conclusions. In many cases it can even change the current state of science (think radical, for example the concepts of time or gravity). So science benefits from philosophy, like many fields of interest benefits from philosophy. Without philosophy, science would not progress. So therefore I would argue that science, like many other fields is a dependent of philosophy.
Because Chris put science and philosophy next to each other in a picture, like they represent two separate modes of thinking, that made me think. When you place philosophy on the right (like in the picture), then the left part is not only science. I rather would place philosophy in the center as it represents our ability to think (both left and right in the brain), and science as one of the many satellites around philosophy. Science is a product of our thinking, philosophy is the process of thinking. But what about art?
I use the term process because in philosophy, there is no common ground, no result. Only the topics are shared amongst them. Many philosophers disagree on the big questions in life. Religion, existence, free will, reason, ethics; these are the big topics that make philosophers think. The ambiguity in philosophy between many philosophers’ thinking is key to make progress here. The seeming inefficiency by disagreement is actually very effective. It’s the only way we can think from different perspectives, making it possible to advance in science, technology, political issues, human rights and so on. In that sense, philosophy is at the center of everything we can imagine. There would be no science without philosophy, neither would there be religion or ethics.
Philosophy is the process of thinking. Wisdom and knowledge (to name a few) the result. In that sense, you cannot argue that philosophy is in our right brain, or science on the right. I would compare it with the duality introduced by Wenger: “The negotiation of meaning involves the interaction of two processes, participation and reification, which form a duality“, where reification is the result of the process of participation, making the abstract more concrete.
A recent discussion on Twitter on complexity triggered me to write this post. Clearly, it is a subject that is being interpreted in many (3?) ways. Complex, chaos, simple, complicated, anarchy, all terms that are being compared in order to try to understand what they (should) mean. Some argue that you can use axes and create a spectrum, where all these phenomena can be plotted upon. Others disagree with the language used, or that these levels exist for complexity. And then there are other misunderstandings or misinterpretations. For example, complexity and Complex Adaptive Systems (CAS) are not exactly the same. We’re talking about the complexity of complexity.
Good for us humans, our thinking and behavior is quite complex as well. We are able to understand complex matter, albeit when looking back. We are used to think in linear ways, especially when we try to predict things to happen. In retrospective, we are capable of understanding things (events, behavior, etc.) that can be called complex. The most important attribute of complexity is non-linearity. Quite interesting finding, when looking back to understand phenomena it seems linear, looking ahead to the future, expect non-linear behavior. Is that complexity? No, it’s just uncertainty. Quite different things. And when looking back, uncertainty is gone, one outcome emerged in favor of many, at the time possible, outcomes.
Now I’ve almost lost myself in the above paragraph. Of course, complexity is related to uncertainty. However, the range certainty-uncertainty does not classify complexity, nor does predictability. In my view, complexity can not be classified, influenced or whatever. Complexity is an attribute of the behavior of a whole, where many actors are somehow involved and influence each other.
To me, complexity is not about systems. It’s about social phenomena. We can talk about the ‘problems’ of complexity and complex behavior, rather I’d talk about the opportunities. Dave Snowden understands this very well. Like I’ve said before regarding emergence, I’d like to say the same about complexity. It’s time to accept and embrace complexity, and to develop methods to get the most out of complex social phenomena or behavior. To be able to develop these methods it is important to understand complexity, however, I think we should not try to understand complexity fully. Our understanding will become better sooner or later, but we have to deal with it now. That’s inevitable.
In a world that changes increasingly faster and faster, the perceived complexity increases with it. It becomes harder to predict the status quo even on the short-term, perhaps even that of tomorrow. The attempts to make predictions become useless. An obsolete approach.
We need to stop acting like we have control over what will happen in the future. We just don’t know. Often we are not even close. What’s the point of making predictions of the future anyway, and then trying to control what happens?
Organizations are the best example of future predictors. They keep trying to figure out the most likely scenario’s to occur based on what happened in the past. Organizations have difficulties in accepting the fact that these predictions are not only a waste of time, it’s even worse than that. They even try to understand what happened in the past based on the present situation. What happened in the past was just one of the possible outcomes. There are no parallel pasts that occurred at the same time and that have led to where we are now. Rationalizing what happened then, is like denying what could have occurred. Sometimes it helps to understand phenomena, but using that for future predictions means that the same mistakes are being made over and over again.
Again, we have to stop predicting, and start nurturing the current situation in a way that good outcomes will flourish, independent of what that outcome can be. It’s not the outcome that matters most, it’s the road to it. The road to it (where ever it will lead) is an emergent path. So many influences are on the lurk, so many that no one knows how many and what they are, that they should be dealt with along the way. They both can be positive or negative, both will have influence on the emergence.
Dealing with matter like I described above is so different then how we are used to, and not only different, but scary as well. To accept and be comfortable with uncertain paths is not suitable for most organizations nowadays. And it won’t be for the years to come probably. However, we see more and more organizations that operate in a networked environment, where many stakeholders play a role. In these situations, long-term strategies are being replaced by emergent strategies, where control does not have a place.
Coming back to the title of the post, maybe it is somewhat exaggerated at the moment, maybe it is more realistic to speak of a change from long-term goals to short-term goals. Dealing with short-term goals combined with iterative processes is a good first step towards completely letting go of control and accepting that everything is emergent. We are humans with brains that can think ahead in time, let’s not forget that important aspect of us.
In my last post I argued that crises are the result of complexity. While I still hold this argument, a crisis is probably a situation where complexity is at a maximum, if there is a maximum. The situation will probably not become more complex than that. A response I got on the previous post from John Marke was a reference to his paper ‘Why bad things happen to good policies‘. I will come back in more detail about his paper later, but one of the important statements is that all paradigmatic shifts are preceded by crises. That’s interesting, if complexity is followed by a crisis, and a crisis is followed by a paradigmatic shift, then complexity will be followed by a paradigmatic shift.
Complexity → Crisis → Paradigmatic shift
Complexity can be seen as a positive feedback loop towards complexity, while a paradigmatic shift is a negative feedback loop towards a ‘stable’ but new (and temporary) equilibrium. A new equilibrium in the sense that it was not predicted or a situation that was stable before. If we can speak of systems here (depends on your point of view on systems), at least we are talking about complex systems, or complex adaptive systems.
If a paradigmatic shift follows a crisis, then who or what sparks this shift to occur? It’s hard to say. In a complex environment, there is a huge network of resources that is ever-expanding. The value of a network is proportional to the square of the number of connected users of the system (Metcalfe). That makes it unpredictable where this shift is coming from, but chances are that it can come from a bottom-up, self-organized distributed sub-network within the system. A question that John Marke asks the reader in his paper is ‘how could we empower them’? First we have to identify the possible ‘we’ and ‘them’. Or shouldn’t ‘we’, and should it be more emergent? Marke poses a similar choice, adapt to the complex adaptive system, or harness complexity and have it work in your advantage.
I like his way of thinking, because either you just accept the fact that you can do anything except adapt, or understand some properties of the system (emergent, unexpected, self-organized, highly connected, adaptive). The latter has more interesting possibilities, and is more congruent with these characteristics. Remember, you are probably in this complex adaptive system as well, play a role, and have the same characteristics. It’s not something totally alien.
In this present situation, it is easy to understand that the situation is getting complex more quickly than it did in the past. That means that crises are about to occur more often, and the same is true for paradigmatic shifts. The thing we need to accept is that situations are not stable, and these ‘stable’ situations are volatile and temporary. Solutions are valid for a short period of time, almost by definition. And why do we want to reach a situation that worked in the past, while the environment around us keeps changing in a rapid pace?
This post is my answer to the paper of John Marke. He’s in the process of writing another, on resilience, the solution space of complexity as he puts it.
Crises are a result of complexity, or better, a result of environments that become more complex than they were for quite a while. We see it all the time. While more people are and become more connected with more people, complexity levels will rise. My thesis here is that when complexity levels rise, entering a crisis is very likely. It’s very likely that something will happen that is unexpected, and has never occurred before. There is no plan or prescription of how to deal with this situation. So what happens when such a situation occurs? It can happen that people panic. It’s their initial response to something unexpected and apparently undesired. After some time, or when more crises occur, many people will blame others. It’s just not their fault, so it must be someone others fault and these people should solve the problem. Of course, one of the characteristics of complexity and crises is that many actors play a role and many connections are present between those actors that it is not easy to blame the right people for a crisis. I think that a Complex Adaptive System (CAS) has similar characteristics.
While the above introduction can be invalidated quite easily by the most of you, including myself, I think there’s something very true in it. Our environments are more complex than they were ten or fifteen years ago, or maybe even three years ago. Complex situations become more common and more normal every year. It would not be a good response to panic or blame others. It’ll probably be better to accept the fact that the world is quite complex, and that there is not a standard solution for everything. As crises become normal, deal with it normal.
Dealing with it as normal is not as easy done as it is said. There is so much we just can’t understand. The human brain is simply not capable of understanding all phenomena. That, and the fact that we are so dependent on so many other people from many countries and the more and more declining availability natural sources makes the world in the years to come even more complex. That is at least one good reason to change our behavior and attitude towards crises and complexity. It’s there and it will be there in the future waiting for us.
The question is, what do we have to change in our behavior and attitude to deal with crises and complexity like it is more normal? Not as business as usual, but because these situations will stay here and the world will become more and more complex. How do we not panic and not blame others for the new or changed situation? Crises are here to stay. I’m not sure what the right responses are, but I know that panicking will not help us so that’s at least one good response. The other ‘right’ responses probably depend on the particular situation, and sometimes responding will not help you at all. It will help to accept the crisis, accept that the situation is complex, and accept that you maybe can’t do anything about it. It’s a change in the mindset of people.
In the search for alternative modes of organization, I have come along a few already on this blog. In a comment on my earlier post on wirearchy, I already mentioned the concept of panarchy. Heterarchy, wierarchy and panarchy, all three are suggestions of how organization can be accomplished using alternative modes, in particular in situations where connections are easily accomplished by having online means of connecting. In this post I will try to unravel panarchy, according to Paul B. Hartzog this is a way governance can work in the network age. So what is panarchy? For the ones that have never heard of the concept before, it is the cumulative effect of the shift from hierarchies to networks is a system of overlapping spheres of authority and regimes of collective action, according to Hartzog. In short:
Complexity + Networks + Connectivity => Panarchy
The essay of Hartzog, which is a highly recommended read, explains the theoretical backgrounds and some real-world examples. I’m not an expert on this subject, but I do believe that we are reaching a point where other types of governance are better alternatives as opposed to hierarchical ones. I believe that global crises are about to occur much more often and that we should accept the fact that crises are a characteristic of our modern time. Instead of dealing with crises like we are doing today, that is fight them and trying to reach a stable equilibrium like we were used to do in the past, it is time to accept crises because of the properties of panarchy (such as complexity, networks and connectivity) are increasing and increasing, making the world more and more complex. This situation asks for systems that are complex as well, and not rigid, but rather flexible or fluid, like water that adapts to its environment. Water is a great metaphor here, it is strong, adaptive, and has some characteristics that always work within the same conditions. If we see situations we call crises now as reality and a logical result of increasing complexity, we don’t have to call these situation crises anymore.
So can panarchy be something like governance in the network age? That is a question which I find quite hard to answer. Is it a form of governance that encompasses all other forms? Or better, is there a form of governance that encompasses all other forms? Yes, you can call the shift panarchy if you like, but what’s the use of that? The paper I referred to does a great job in explaining what panarchy is, and Hartzog argues that it has the potential of becoming the dominant form of governance in the future. The importance of debates like this in my opinion is that many people still work and make big decisions that worked out well in earlier times, but not that good in the present time and not all in the future. The shift that we’re in, that the world is in, ultimately will lead to different modes of organization and governance. Power is more distributed, people are more connected and knowledge is created and transported in networks. Maybe one of the most important things that is happening, is that decision making is changing. It is changing in terms of who are able to make decisions because of where the knowledge is available, who can make the better decisions because of where the most accurate knowledge is available, and who are able to distribute the knowledge to let others make the decisions.
Ok, admitted, the end of the previous paragraph is nothing more than elaborating on the beginning of the previous paragraph and does not directly contribute to the main question here, but that is because (tacit) knowledge and decision-making are closely related to complexity, networks and connectivity, or panarchy if you like. And if the best decisions should be made, governance is important as well as organization. In addition to heterarchy and wirearchy, can panarchy help us as well?
Well, after some posts about systems thinking and complex adaptive systems, the discussions where fruitful, but many of us are still disagree quite strongly about certain statements I or others have made in these posts and discussions. One of the disagreements is whether an organization is a system or not, or if you can look at an organization like it is a system. For me, it’s not 100% clear what a system is. Neither is it clear for me whether an organization is a system or not. What helps me, is to look at an organization as if it were a system, like for example Carter MacNamara does.
Some of us, myself included, thinks that it would help if we can agree on an operational definition of a system first. It would help in the dialogue, in discussing some topics that are strongly related to systems. It helps if the discussion would not be distracted by defining what a system is or is not. In this post I will try to accomplish to define a system. While this can seem as a useless try, because it seems so obvious to many, I think it can help. To start as blank as possible, let’s have a look what our friend Wikipedia says about systems:
A system is a set of interacting or interdependent entities forming an integrated whole. The concept of an ‘integrated whole’ can also be stated in terms of a system embodying a set of relationships which are differentiated from relationships of the set to other elements, and from relationships between an element of the set and elements not a part of the relational regime.
Quite abstract definition. But hold on, the definition of a system is further characterized by the following common characteristics:
- Systems have structure, defined by parts and their composition;
- Systems have behavior, which involves inputs, processing and outputs of material, energy or information;
- Systems have interconnectivity: the various parts of a system have functional as well as structural relationships between each other.
Let’s try to zoom in on some parts of this definition. Structure and interconnectivity is a rather common characteristic of many concepts. I think we can skip these here. The problematic characteristic is behavior. Apparently it involves input, processing and output, like a black box. What kind of behavior do we mean? Just systematic? Is it standard, predictable behavior? Or is complex and unpredictable allowed as well? Does the behavior show patterns or not? Are these causalities or not? Can a system always be optimized and made more efficient? Is there always a negative feedback loop in a system to control its behavior? Is there a desired state? All questions that are difficult to answer, but can be relevant when trying to zoom in on the behavior of a system. Another question is, which behavior makes it impossible to be a system? When can’t we speak of a system?
When thinking about systems and organizations, you immediately come across the differences between the two. People like to compare the two, because many people like to think that organizations can be controlled. However, unlike most natural systems, organizations are started and end in failure many times. Many times they fail because it can’t be controlled. It is more complex.
This comparison is clearly a problem we can not easily solve. It is quite philosophic, and it depends on what your worldview is how you look at it. However, a workable definition we can agree upon would be nice for the dialogue, so we can make the next steps. Unfortunately, if we look at systems like systems philosophy, it gets even more difficult.
According to systems philosophy, there are no “systems” in nature. The universe, the world and nature have no ability to describe themselves. That which is, is. With respect to nature, conceptual systems are merely models that humans create in an attempt to understand the environment in which they live. The system model is used because it more accurately describes the observations.
According to the above definition, there are no natural systems, only models. More on systems philosophy:
Systems are further expressed by listing the elements relationships, wholes, and rules associated with that system. Again, this is an arbitrary exercise true of all models humans create.
If it was difficult to define what a system is or is not, it sort of becomes impossible by now by using the word arbitrary. No wonder we cannot come to an agreement, and no wonder the discussion was taken over so often by the systems discussion. Can we say that everybody’s arguments are arbitrary? Does it all depend on the philosophical worldview (organic, mechanistic and process) you have that all compete with each other?
I started this post with two questions, but now I have many more questions instead of answering the first two. Not a problem at all, however, I hoped to come to a workable definition that would help structure the dialogue. Perhaps too much to ask for in a single try. I hope that you can add your view on the definition of a system, that will contribute to the understanding of systems thinking, complex adaptive systems and other concepts alike. Not to mention open and closed systems, or stochastic and deterministic systems.
Inspired by the many comments on previous posts and their deferring visions (myself included) about systems, systems thinking and systems theory, I thought it was time for a post about these subjects. For now I will focus on systems thinking. We talked about whether organizations are systems or not, what systems are and are not, and if it helps to compare organizations with systems. Very precarious matter, it seemed. To me, it is precarious as well. To compare two things with each other is always tricky. Do we share the same vocabulary? Are we referring to the same? Are we oversimplifying the subject matter? Talking about organizations makes it even more trickier, because no organization is the same. The forms of organizations can differ, let alone the people who make up the conversation of organization. Think Wittgenstein here…
Like many people, I like to understand certain phenomena. If we do not understand, we tend to compare these phenomena with ones we do understand, or think we understand. That comparison should help us with understanding the more complex phenomena. While this can be a strategy that helps us, it can distract us from the important aspects of these phenomena as well. This is always a pitfall when comparing apples and oranges. However, systems thinking is not just an apple or an orange, it can make sense to make use of systems thinking to try to understand tiny parts of a larger unit, in relation to other parts.
Can’t we think of organizations as systems at all? It depends on the vocabulary we use and have in common. I think it can help to deduct to some smaller pieces present in organizations. Carter McNamara shares his view, and it contributes to my understanding. His statement on what a system is, shows the complexity of a system:
A pile of sand is not a system. If one removes a sand particle, you’ve still got a pile of sand. However, a functioning car is a system. Remove the carburetor and you’ve no longer got a working car.
The statement above is a somewhat simple example, that illustrates the complexity of a system. When you remove a lot of particles, the pile will collapse or even disappear. Translated to an organization, it becomes apparent what the problem with the comparison between systems and organizations is. Like with systems, every particle in an organization plays a role. It influences other parts. Maybe some particles can easily be removed, because they have little or no influence on other parts. The organization still works as expected, but we call it more efficient. Some particles are more difficult to replace, it has more influence on other parts and the organization will change as a result. Unlike with systems, there are no two particles alike when humans are involved. Therefore, the statement above doesn’t help me that much. The comparison is still a problem. What helps, is the statement of the same Carter McNamera when he explains why it is important to look at organizations as systems.
The effect of this systems theory in management is that writers, educators, consultants, etc. are helping managers to look at organizations from a broader perspective. Systems theory has brought a new perspective for managers to interpret patterns and events in their organizations. In the past, managers typically took one part and focused on that. Then they moved all attention to another part. The problem was that an organization could, e.g., have wonderful departments that operate well by themselves but don’t integrate well together. Consequently, the organization suffers as a whole.
This is helpful. Organizations are not systems, but it helps to look at an organization as if it were a system. Changing something in the organization always has influence on other areas in the organization. The comparison refers to complexity, both organizations as well as systems are complex. It can help to deal with the complexity of an organization. But then again, by looking at it as a system you should not make it a system, the processes that occur in organizations are not comparable to systems at all.
Some commenters on previous posts on this blog referred to CAS or Complex Adaptive Systems. This term is somewhat fuzzy for me, as I’ve never read about CAS before. So now is the time to do so. A first lookup in Wikipedia is always a good start, so that’s what I did. I must say, the C in CAS already becomes apparent when you look at the definitions. One of the definitions that is mentioned is the following:
A Complex Adaptive System (CAS) is a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents.
So this definition says that a CAS is a network, where many actors act for themselves in a response to their (changing) environment. If I interpret this correctly, human behaviour is a CAS as well. Almost all humans are connected to each other via a number of other humans. Or the Internet is a CAS, where many endpoints are connected to the same network, they determine the network, they are the network. Or maybe the universe and evolution as well.
My interpretation is that we use the term CAS when we do not understand the behaviour of a system or phenomenon or when it can’t be controlled. Examples that are given are ant colonies, stock markets, the ecosystem, or political parties. All are difficult to understand, if they can be understood at all, and even the actors in it probably do not understand their system that they are part of, for example the politicians in a political party or the ants in the colony. These systems or phenomena can’t be controlled, their behaviour can seem unpredictable. And that’s a good thing, the urge to control is overrated very much. Maybe some influence can be desired sometimes, if possible.
The Wikipedia article also states that the principles of self-organization and emergence are very important in these systems. The relation between self-organization and CAS became apparent in the discussion on self-organization as well. But then we come to the differences between human beings with a mind of their own, and other players like ants or cells. Can self-organization occur in an organization where people are involved? Or is it just not possible because we can think for ourselves and can act by reason? However, the latter is a philosophical discussion. Do we act by reason or by drifts for power? The philosophers Immanuel Kant and Friedrich Nietzsche thought about that very differently. So maybe this discussion is always a philosophical one.
If we go back to the definition, the C in CAS is only true when you look at the phenomenon from a birds-eye perspective. All the actors deep down in the system are probably not aware (if they could) that they are part of the system, and just follow simple rules. So from their perspectives, there is not much complexity. They adapt to their environment, like a water drip just follows the easiest path. This drip is not aware of the ecosystem that it is part of, just like the system is not aware of the single drip. However, it is possible to influence the flow of the water, because we understand the characteristics of water. But it is not possible to influence the whole system where water is a part of, it’s just too complex.
Translated to organizations, complexity is there or not depending on the perspective you’re in. The higher in the hierarchy, the more complex the organization as a whole seems to function. If you are high in the organization, you’re aware of the size of the organization, and therefore aware of the variety of actors. How they all interact, is difficult to grasp. The lower in the hierarchy, the less you are aware of all the other players that exist in the organization, and the more focussed you are on your tasks which are relatively not complex at all. Well, that’s my understanding at this point.