Module 3: Complexity theory

Module 3: Complexity theory

“introduction to Part B & module 3 … Complexity theory explained”
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Summaries

  • Module 3: Complexity theory > 3.2 Complexity theory explained > Web lecture: Characteristics of complex systems - Part 1: Systems Theory
  • Module 3: Complexity theory > 3.2 Complexity theory explained > Web lecture: Characteristics of complex systems - Part 2: Complexity Theory
  • Module 3: Complexity theory > 3.2 Complexity theory explained > Web lecture part 3 Complexity theory and the implications for management and governance

Module 3: Complexity theory > 3.2 Complexity theory explained > Web lecture: Characteristics of complex systems – Part 1: Systems Theory

  • What we will be looking at over the next two videos are the generic characteristics of complex systems.
  • In the first video, we will discuss the characteristics that we have come to understand through systems theory that are essential for us to build on to then develop a fuller understanding of complexity theory.
  • So the first video will look at the connectivity, inter-dependence, feedback and emergence.
  • Complex behavior arises from interaction, and complexity theory focuses on relationships.
  • The other key characteristics of complex theory is that complex systems can create new order.
  • Connectivity and interdependence, what I would like to emphasize is that there are degrees of connectivity.
  • Some of you would have gone through a process of actually hiring people, you look at their cv, you interview them, and you’ll point someone that you feel is going to make a very good contribution to the rest of the team.
  • The opposite of course will also be the case, and it could be that the person that may appear at the beginning to be shy or withdrawn, in the right environment, may then flower and actually make a very, very good contribution.
  • So the system becomes to be very dependent, or the different parts become very dependent on each other, and these dependences cannot be pulled apart.
  • The outcomes are often non-deterministic and the point here is that complexity does not argue for ever increasing connectivity.
  • Why? Because if we push the system too far into being connected it then becomes too inter-dependent and it becomes fragile.
  • What is happened is that finance linked to real estate, which is right at the center of it, which is the dark green, is at the very heart of the global financial system.
  • What that meant was that the system had become too interconnected, too interdependent and that had made the system fragile.
  • So it did not take much to actually topple the system.
  • You can see from this graph how the 5 sectors are actually moving together and this is just a period of just ten years.
  • The question is why? Why does this happen? And I hope that by understanding the characteristics of complex systems we will come to understand why these phenomena actually take place.
  • One of the first characteristics I want to explain when we move beyond connectivity and interdependence is feedback.
  • There are two types of feedback, and I want to actually explain the technical difference between positive feedback and negative feedback.
  • Now, counter intuitively, positive feedback tends to cause system instability, while negative feedback tends to cause system stability.
  • The first few sheep that started will then attract other sheep and more sheep will follow and you can then see what actually happens.
  • So positive feedback tends to cause system instability, and it can very quickly lead to a bank run or even a global financial crisis.
  • So positive feedback feeds on itself and makes the difference greater.
  • Negative feedback tends to make a system self regulating and it can produce stability.
  • So when you press the lever the water will empty, when the water empties, the valve opens and the water comes in and it refills the system.
  • As the water rises, the ballcock rises and then, when the water reaches the right level, the valve is closed and no more water comes into the system.
  • That is a very efficient, very simple system and it has a single equilibrium point.
  • Now what our mistake is, is that very often we actually make the assumption that what applies to a very simple mechanistic system will also apply to a complex system.
  • Now an economy, which is a complex system, may have both positive and negative feedback at the same time and it will have multiple equilibria.
  • The idea of emergence is that individual agents, this could be individual people, as I said, groups etcetera, interacting together create something which is both unpredictable and it has a bottom-up effect.
  • So we got individual agents interacting and creating the emergent at the macro level.
  • What comes out of that brainstorming session will be quite different then if you were to take exactly the same people, put them in separate rooms, give them exactly the same question and then compare the outcomes.
  • You cannot add the individual answers and come to the same outcome as you will get from the group working together in the brainstorming session.
  • So what that means is that emergence is a systemic property , it is a property of the system working together, interacting together, to create the emergent property.
  • Learning, culture, innovation are all emergent processes, but also new ways of organizing, and new organizational forms can also be emergent.
  • When we look at the challenges of managing complex systems, we will then focus on that much more.
  • What we have looked at are the four basic characteristics of complex systems.
  • These have already been articulated by systems theory, which are connectivity, interdependence, feedback and emergence.

Module 3: Complexity theory > 3.2 Complexity theory explained > Web lecture: Characteristics of complex systems – Part 2: Complexity Theory

  • In part 2 we are going to look at the characteristics of complex systems that we have come to understand through the theories of complexity.
  • What we looked at in the first video were the group at the top, connectivity, interdependence, feedback and emergence.
  • Because they have all contributed to our deep understanding of complex systems.
  • So that understanding has come from chemistry and physics, through evolutionary biology, autopoiesis, biology and cognition and chaos theory.
  • From economics based very much, to begin with, on the work of Brain Arthur.
  • So what we will look at now is the contribution that these theories have made to help us develop and understand much further the four principles I discussed in the first video.
  • Because we cannot always take something from the natural sciences and apply them directly to a human system.
  • During the Arab Spring, there was a point when someone took a broom and went on to Tahrir square and started cleaning up the square, simply because the square needed cleaning.
  • No one actually told that person, and of course when one person started others joined him.
  • What that means is it is simply that the system explores new options, different ways of working and relating, because it has found a particular constraint that will not allow it to fulfill a particular objective the way that it may have been preplanned.
  • If you really want to see her, you will explore the space of possibilities and find a way of getting there to actually be with your grandmother.
  • That is what complex systems do, they are very good at finding new ways of doing things.
  • What it shows is that the very successful species has climbed to the very top of the highest peak.
  • It has that one very successful strategy that has put it to the very top and nothing else.
  • As the children jump up and down the bouncing castle it changes all the time, and that is how to imagine a fitness landscape.
  • So what do you do? And as I said, the answer here is while that one strategy is successful, you need to explore different micro-strategies, and then do different experiments in order to see what works.
  • So that is the principle of exploration of the space of possibilities, and that is something that complex systems do very well indeed.
  • In this case we have an example in biology and we look at bumblebees and the flowers that they pollinate.
  • If you change your decision or action in due course comes back and affects me to such an extent that I also have to change my behaviour, that’s co-evolution.
  • The definition is reciprocal influence which changes the behaviour of the interacting entities, and it is a very, very powerful dynamic.
  • Because what it means is that we cannot just think about the impact of the environment on individuals, on organizations, on societies, because the moment they start changing their behaviour, that behaviour will go back and influence the initiator of the change.
  • The original work, which was done on dissipative structures by Ilya Prigogine and with his co-workers Nicolis and Stengers, it won Ilya Prigogine the Nobel Prize, because he reinterpreted the second law of thermodynamics.
  • As you may recognize there, you will see the two screens and again it is a global financial system when it tumbled down.
  • When a system is pushed far-from-equilibrium it means that it can no longer carry on under its previous way of operating.
  • You will see that the system is dynamically moving within a certain limit.
  • What happens is when there is a disturbance outside the system, that means it can no longer continue to function in its old way.
  • This is called pushing the system far-from-equilibrium.
  • There is a point in the second part of the diagram, and that is called a point of bifurcation.
  • Because at that point, at that critical point, the system, the complex system, will explore its space of possibilities, will continually explore different options.
  • So what that simple bifurcation shows is it will either create new order, remember at the very beginning I said that the complex system has the capacity to create new order, and this is what I mean.
  • What happens at that point is very, very exciting.
  • So what happens is when a system is pushed far-from-equilibrium the following characteristics come into play to create the new order.
  • Especially when we’re looking at complex systems.
  • When we’re looking at complex systems that we actually want to design.
  • One thing I want to make very clear is to give you a distinction between complicated systems and complex systems.
  • Complicated systems we can design, we can predict their behaviour and we can control their behaviour.
  • Now these are systems for example like producing a glass.
  • We know exactly what we’re producing, but we cannot do these things with a complex system.
  • In our third video on the challenges of managing complex systems we will then look at what is it that we can actually do if we cannot design, predict and control a complex system.

Module 3: Complexity theory > 3.2 Complexity theory explained > Web lecture part 3 Complexity theory and the implications for management and governance

  • You will remember that we looked at the generic characteristics of complex systems and we’re going to use the understanding from those characteristics to now address the challenge of how do we manage complex systems.
  • Now, if you remember we finished the earlier video by saying that we cannot manage complex systems in the sense of designing them and predicting and controlling their behavior.
  • So if we cannot manage them in that way, then what do we do? We need a different way of understanding what ‘management’ means by using our insights from complexity theory.
  • The World Economic Forum has set up a new global agenda council on complex systems, and for more than a year now we have been working with a catastrophic risks council to actually look at pandemics and I will explain this at the end.
  • First of all, we need to understand what does inadvertently constrain mean.
  • When we want to be innovative – and you remember last time I kept repeating innovation – and we do not facilitate self-­‐organization and exploration of the space-­‐of-­‐ possibilities, then we will not actually achieve our objective.
  • These are apparently intractable problems in the sense that they are very difficult, they do not have a single solution or a simple solution and they are very, very difficult to address.
  • Yet what we often do as humans is choose one of those dimensions and focus on that with exclusion of all others.
  • For example we might say it’s only a technical problem, or it’s only a financial problem, or it’s only a cultural problem, and we actually focus on that dimension and we do not address all the other dimensions.
  • These characteristics, these dimensions are inter-­‐related, inter-­‐ connected and they co-­‐evolve, they actually influence and change each other.
  • So how do we actually go about understanding the multiple dimensions and the multiple causalities? Let me give you an example, because what I have just said is theory.
  • What we did, we worked with that team last March, and we took them through a methodology that is based on the logic of complexity to actually identify the multiple dimensions of the problem space and the multiple interacting causalities.
  • Because the whole point is: How do they influence each other? How do they co-­‐evolve? Because if we don’t understand those co-­‐evolutionary dynamics within the multiple dimensions, we cannot address such a very complex problem.
  • What we need are multiple micro-­‐strategies, which are part of a coherent overarching framework, which is part of an enabling environment.
  • So the first one is the idea that the complex problem or challenge has multiple dimensions and multiple causalities and the way to address them is through an enabling environment, not a single solution.
  • All the dimensions in that cluster that we have identified need to be addressed at the same time.
  • We cannot say: ‘Today we will address culture, tomorrow we will address organizational structure, the day after we will address the financial’.
  • So we need to address, to set up the enabling environment by addressing the key dimensions.
  • Not everything, but the clusters we have identified, we need to address them all at the same time and at multiple scales.
  • What do I mean with multiple scales? What does an individual need to address them? What does a group need? What does the organization need? In other words, do we need new resources? Do we need different skills? What is it that we need in order to address this? So an enabling environment is something that is evolving and co-­‐evolving with this changing external environment and it is this condition that we have to set up to keep on and on addressing our complex problem.
  • Now remember, creating an enabling environment, which is conducive to chance and the emergence of new ways of organizing, we cannot predict, we cannot control what will emerge, we cannot even design it in detail.
  • We also need to facilitate self-­‐ organization and the exploration of the space of possibilities.
  • So an enabling environment needs clarity of vision, it needs clarity of identity and clarity of direction.
  • We really do need to rethink what governance means of a complex system.
  • There also needs to be coherence between the disparate parts of the organization, which is based on good communication and feedback.
  • So we need to actually enable, facilitate distributed leadership.
  • If a pandemic strikes, and you can see on your screen how many countries had been affected, and if it’s a pandemic that actually affects people and stops them working, the impact will be on water, on food, on transportation, on communications, on security, etcetera, etcetera.
  • We need to prepare, we need to understand what happens when the incident actually happens and the speed of the spread. Then we need to understand the impact of that pandemic and finally recovery.
  • We need to understand the complex problem space in all four stages and how they interact with each other.
  • So some concluding reflections: An organization needs to be seen as a whole, inter-­‐ connected and inter-­‐dependent system.
  • With multiple dimensions and issues that interact, co-­‐evolve and change each other.
  • Understanding our organizations as complex systems, enables us to work with their characteristics and to enable desirable behaviours which we cannot mandate or control.
  • So I think that brings us full circle from our earlier discussion in this module that complex systems cannot be designed and controlled and that behaviour cannot be predicted.
  • By understanding the problem space and co-­‐creating the enabling environment.

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