Esko Kilpi on Interactive Value Creation

The art of interaction, the design of digital and the science of social complexity

Tag: Chaos

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.


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

Complexity – Numbers that fool us

One of the basic ideas of modern science is that the laws of the material universe can only be meaningfully understood by expressing quantified measurements. Numerical terms are needed, not just words and stories. The belief was that instead of ordinary sentences we must use mathematical equations.

The values of the measurements at a given starting time are called the initial conditions for that system. The Newtonian, deterministic claim is that for any given system, the same initial conditions will always produce an identical outcome. Life is like a film that can be run forwards or backwards in time.

One thing we have learned is that no real measurement is infinitely precise. All measurements necessarily include a degree of uncertainty. The uncertainty that is always present arises from the fact that all measuring devices can record measurements only with finite precision. To be able to reach infinite precision, the instrument we use should be able to display outputs with an infinite number of digits.

By using very accurate devices, the level of uncertainty can often be made acceptable for a particular purpose, but it can never be eliminated completely. It is important to note that the uncertainty in the outcome does not arise from randomness in the equations, but from the lack of infinite accuracy in the initial conditions.

It used to be assumed that it was theoretically possible to obtain nearly perfect predictions by getting more precise information. Better instruments would shrink the uncertainty in the initial conditions, leading to shrinking imprecision in predictions. The lack of infinite precision was thought to be a minor problem. Well, our belief systems are still mostly based on the idea that very small uncertainties don’t matter.

Possibly the first clear explanation of a very different kind of understanding was given in the late nineteenth century by the French mathematician Henri Poincaré. He was the founder of the modern dynamical systems theory. His claim was that there were systems that followed different laws: the tiniest imprecision in the initial conditions could grow in time. Two nearly indistinguishable sets of different initial conditions for the same system would then result in two developments that differed massively from one another. This is the reason why seemingly random behavior can emerge from deterministic systems with no external source of randomness.

Poincaré was way ahead of his time. His early thoughts gained evidence in 1963, when Edward Lorenz found, by accident, that even computer models of the weather were subject to very sensitive dependence on initial conditions.

Numbers fool us and quantified measurements are very rarely the whole picture. Stories matter more than we think.

More on the topic: The End of Certainty: Time, Chaos, and the New Laws of Nature by Iliya Prigogine. 1998. Chaos: Making a New Science by James Gleick. 1987