Business and complexity

Up to now, we have seen the world around us as systems that, we thought, could be described and understood by identifying rational causal links between things: if I choose X, then it will lead to Y. If, on the other hand, I choose A, it will lead to B. We are accustomed to drawing boxes and arrows between those boxes. We try to model the world as predictable processes based on knowing how things are and how they will be. We want to be certain, and we think we are.

Management thinking is based on the sciences of certainty. The whole system of strategic choice, goal setting and choosing actions to reach the given goals in a controlled way depends on predictability. The problem is that this familiar causal foundation cannot explain the reality we face. Almost daily, we experience the inability of leaders to choose what happens to them, to their organizations – or to their countries. Things may appear orderly over time, but are inherently unpredictable. We live in a complex world.

Complex systems are, as their name implies, hard to understand. Social systems, like organizations consisting of people, are accordingly complex and hard to understand. There is no linearity in the world of human beings. There are no arrows and people are not boxes, or fit inside of boxes. This is why our thinking needs to develop from the sciences of certainty to something more applicable, the sciences of uncertainty, the sciences of complexity.

Complexity refers to a pattern, a movement in time that is, at the same time, predictable and unpredictable, knowable and unknowable. Chaos theory explains how these patterns form. A parameter might be the flow of information in the system. At low rates, meaning no input or more of the same input, the system moves forward displaying a repetitive, stuck behavior. At higher rates and more diversity the pattern changes. At very high rates the system displays a totally random behavior. The pattern is highly unstable. However, there is a level between repetition/stability and randomness/instability. This level where simultaneous coherence and novelty are experienced is called the edge of chaos.

Classical physics took individual entities and their separate movement (trajectories) as the unit of analysis in the same way we have analyzed and rewarded individuals. Henri Poincaré was the first scientist to find that there are two distinct kinds of energy. The first was the kinetic energy in the movement of the particle itself. The second was the energy arising from the interaction between particles. When this second energy is not there, the system is in a state of non-dynamism. When there is interactive energy, the system is dynamic and capable of novelty and renewal.

Interaction creates resonance between the particles. Resonance is the result of coupling the frequencies of particles leading to an increase in the amplitude. Resonance makes it impossible to identify individual movement in interactive environments because the individual’s trajectory depends more on the resonance with others than on the kinetic energy contained by the individual itself.

We are the result of our interaction. We are our relations.

The conclusions are important for us: firstly, novelty always emerges in a radically unpredictable way. The smallest overlooked variable or the tiniest change can escalate by non-linear iterations into a major transformative change in the later life of the system.

Secondly, the patterns are not caused by competitive selection or independent choices made by independent agents. Instead, what is happening happens in interaction, not by chance or by choice, but as a result of the interaction itself.

The new social technologies have the potential to influence connectivity and interaction as much as the sciences of complexity are going to influence our thinking. The task today is to understand what both social business and complexity mean. The next management paradigm is going to be based on those two, at the same time.

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John Hagel on “harnessing the power of randomness” and “resilience“

High Performance Business

In our view of the world, we often think that competition creates and secures efficiency. But it may be that high performance is incorrectly attributed to competition and is more a result of diversity, self-organizing communication and non-competitive processes of cooperation. Competitive processes easily lead to a handicapping of the higher-level system that these processes are part of. This is because competitive selection leads to exclusion: somebody is left out. Leaving something out means a reduction of diversity. The resulting less diverse system can be efficient in the very short term, but always at the expense of longer-term agility and viability.

Our assumption has also been that by understanding the parts of a system in detail, we understand the whole. Classical physics took individual entities and their movement (trajectories) as the unit of analysis in the same way we have lately analyzed the competitiveness of individuals and firms. Henri Poincaré was the first scientist to find that there are two distinct kinds of energy. The first was the kinetic energy in the movement of the particle itself. The second was the energy arising from the interaction between particles. When this second energy is not there, the system is in a state of non-dynamism. When there is interactive energy, the system is dynamic and capable of novelty and renewal.

Interactive energy may be the single most important factor in (business) performance. Higher performance patterns may accordingly occur through the very simple combination of different experiences and enriching interaction. What happens in the interaction between the parts is thus much more important than the parts. The parts are born in the interaction and the whole is the emergent pattern of the interaction, not the sum of the parts.

The focus of the high performance organization should be on communicative interaction.

Interaction creates resonance between the particles. Resonance is the result of coupling the frequencies of particles leading to an increase in the amplitude of motion. Resonance makes it impossible to identify individual movement in interactive environments because the individual’s trajectory depends more on the resonance with others than on the kinetic energy contained by the individual itself. We are therefore the result of our interaction.

The lesson is that every interaction of all of the particles is thus potentially meaningful and can lead to the amplification of the slightest variation. Interactive systems with even the smallest variations take on a life of their own. The future form and direction of the system is not visible in the system at any given time. The future is not in the system and it cannot be chosen or planned by anyone.

The conclusions are important for us: Firstly, novelty always emerges in a radically unpredictable way. The smallest overlooked variable or the tiniest change can escalate by non-linear iterations into a major transformative change in the later life of the system.

Secondly, the patterns of healthy behaviour are not caused by reductionist, competitive selection or independent choices made by independent agents. Instead, what is happening happens in interaction, not by chance or by choice, but as a result of the competitive/collaborative interaction itself.

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Thank you Pekka Himanen and Doug Griffin

The new world between chance and choice

Nonlinear dynamics are concerned with messy systems. Examples for these systems are the human brain, the weather and the evolution of life itself. There is not a single science of non-linearity, but there are different streams of research such as chaos theory or the theory of complex adaptive systems. The latter strand takes up an agent- and rules of interaction-based approach to modeling what is going on. The first explains the behavior of systems that can be modeled by complex mathematical equations where the output of one calculation is taken as the input for the next.

Chaos theory explains how the parameters in the equations cause patterns in time. These patterns can also be described as phase diagrams. Then they are called attractors. A parameter might be the flow of information or the amount of energy in the system. At low rates the system moves forward displaying a repetitive, stuck behavior. This pattern, “spatially”, is called a point attractor. At higher rates the pattern changes. At very high rates of, for example information flow, the system displays a totally random behavior. The pattern is highly unstable. However, there is a level between repetition and stability or randomness and instability. This level is called the edge of chaos. The pattern in time is called a strange attractor when it is described as a phase diagram.

The strange thing with a strange attractor is that the ongoing movement is never the same but always recognizable. The pattern is paradoxically stable and unstable, predictable and unpredictable at the same time. Chaos theory describes a paradoxical dynamic that is not a synthesis of order and disorder. It is about orderly disorder or disorderly order. The very meaning of these words is new.

The weather is often used as an example of a system that displays this pattern. The overall weather patterns can be (almost) predicted over short periods of time. Over longer periods, the behavior cannot be predicted. The long-term behavior of a system like this is determined as much by the smallest changes in the smallest of parts of the system, as it is determined by the laws governing it. The conclusion is very clear. Predictability, if it is possible, is always short-term. Longer-term predictions would only be possible if absolutely all the variables in the system could be measured with absolutely infinite accuracy.

As it is impossible to know all the variables and as it is totally impossible to measure the variables with the infinite accuracy needed, the smallest overlooked variable or the most minute change can escalate up by non-linear iterations into a major transformative change in the later life of the system. Another conclusion is that from a chaos theory perspective, movement towards equilibrium is always movement towards death. If a system is healthy, successful and alive, it is “at the edge of chaos” where the long-term cannot be seen.

The scientists at the Santa Fe Institute developed another strand of research: the complex adaptive systems (CAS) approach. A CAS consists of a large number of agents. Each agent behaves according to its own intentions and rules for local interaction. Local here means that no agent can interact with the whole population of agents at the same time. No individual agent can determine or cause the pattern of behavior that the system as a whole displays. These adaptive systems display the same dynamics as the chaos theorists found: stable equilibrium at one end of the spectrum, random chaos at the other, and in-between the newly found complex dynamic of simultaneous stability and instability or predictability and unpredictability, paradoxically at the same time.

The conclusions are hugely important for us. Firstly, novelty always emerges in a radically unpredictable way. Secondly, the patterns of healthy behavior are not just caused by competitive selection or independent choices made by independent agents. Instead, what is happening, happens in interaction, not by chance or by choice, but as a result of the competitive/collaborative interaction itself.

The Internet changes the patterns of connectivity, transforms our understanding what “local” is, and makes possible new enriching variety in interaction. The changed dynamics we experience every day through social media may have the very characteristics of the edge of chaos.

The sciences of complexity change our perspective and thinking. Perhaps, as a result we should, especially in management, focus more attention on what we are doing than what we should be doing. The important question to answer is not what should happen in the future, what the goals are, but what is happening now that creates the continuously developing pattern that is our life.

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Thank you Stu Kauffman and W Brian Arthur. Special thanks to Ralph Stacey and Doug Griffin.

Complexity and management.