Esko Kilpi on Interactive Value Creation

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

Tag: Ilya Prigogine

The Snapchat Economy

We inhabit a world of emergence, uncertainty and unforeseeable change. The greatest opportunities for advantage lie in the combination of fast-changing markets and emerging technologies. Because of this complex landscape, instead of preparing ourselves for a knowable future, we need to explore and probe for openings. We need to build on successful ventures and shift flexibly among opportunities as circumstances change.

The strategic logic is temporal rather than spatial. When following a spatial, foresight metaphor, there is a territory that can be mapped and understood, but here the territory is seen as being under continuous development and in formation by the exploration itself. It is impossible to map an area that changes with every step the explorer takes.”

The significant point is that no one can predict how long an advantage will last. It is a Snapchat economy. The responsible and resilient way to think is that it could all end tomorrow. The key insight is then that we should be where the flow of opportunities is the fastest and most promising.

Biologists explain the way social insects do this. If two ants would leave the nest at the same time and follow different routes to a new food source, they mark their exploration trail with pheromone. The ant that found the shorter route will return first. What happens is that this route will now be marked with twice as much pheromone as the other path taken by the second ant, who hasn’t yet returned.

The other ants will now be attracted to the shorter, more efficient path because of its concentration of pheromone. Individually, these ants have little intelligence. They don’t have managers or any supervision. Yet, collectively they create a thriving community.

For social insects, teamwork is organized and coordinated through the interactions of the members of the colony. Their collective intelligence emerges from the encounters, not from the insects. The success of the colony is a result of the collective activity of the individuals following very basic protocols: (1) make successful behavior visible to others, and (2) follow successful behavior.

The principle is basically the same, even if instead of ants and pheromone, we were to talk about human beings and blockchains. Today, the most valuable assets can be open. When success leaves tracks that others can follow, it can be beneficial not only to the follower, but also financially to the one who is followed with the help of post-blockchain smart contracts. Successful organizations always scale up learning. Now there is a financial model for it. Work itself is learning, meaning observing and simulating desirable practices. On the other hand, work is teaching, meaning doing one’s work in an openly reflective and transparent way, just the way the ants do it.

Every company and every individual is a particular combination of opportunities and (enabling) constraints. The biggest constraint, however, is that we are not used to thinking that we may have thousands of opportunities available every day. Thousands of potential trails to study and possibly follow. We just don’t know yet where to look for inspiration, but AI is going to change that.

The dominant business organization of the future may not be a permanent corporation but rather a dynamic network. Network knowledge can merge into temporary bundles whenever and wherever necessary to solve problems. The network makes it possible to pool the knowledge residing in millions of nodes into an ad hoc front end with massive problem-solving capacity. There is very little or no centralized control. The role of the manager changes dramatically and often disappears completely. There does not need to be any single point of oversight.

The Internet follows this same philosophy and logic. The things you have to obey are the communication protocols. Protocols make connecting possible. They are really the backbone of cooperation. Similarly, post-blockchain based protocols and smart contracts make the new, temporary economic spaces possible.

Protocols don’t need to take the form of technical specifications. They can also make human interaction possible, as we can see taking place in operating theaters. When surgeons, anesthesiologists, nurses and supporting staff gather to perform an emergency surgery they all know the protocols they follow and learn very fast how they’ll interact with one another, even if they never worked together before. A work role for many individuals in the future will be to take part in networks that neither they nor anyone else controls. The key metric is how long it takes for people to cooperate efficiently.

Modern science explains the theory behind this.

Quantum theory says that each quantum entity has both a wavelike and a particle like aspect. The particle like characteristic is fixed but the wavelike one is a set of potentialities that cannot be reduced to the existing (parts of the) entity. If two or more of these entities are brought together, their potentialities become entangled. Their wave aspects are interwoven to the extent that a change in the potentiality in one brings about a corresponding change in the potentiality of the other.

A new shared reality emerges that could not have been predicted by studying the properties or the actions of the two entities. The interconnected patterns in human interaction are the results of self-organizing processes across the particular network forming the temporal organization. Local interaction generates emergent outcomes that cannot be traced back to any specific action or actor.

Ilya Prigogine wrote in his book “The End of Certainty” that the future is not given, but under perpetual construction: “Life is about unpredictable novelty where the possible is always richer than the real.”


Credits Chris Meyer and Stephen Downes

The future under construction

The approaches of industrial management have given us remarkable material well-being over the last few centuries, but are increasingly being criticized for not being suited to handling the needs of today. Organizations need to excel in innovation. Companies also need to embrace rapid change and uncertainty. Some of the most creative ones have even gone so far as to take a “let’s just do cool things and see what happens” approach, trying to avoid traditional governance systems. Is this yet another sign that management is in crisis?

The industrial theory of management is based on top managers choosing the future of their organization and guiding its development in the right direction. The belief is that managers can make useful forecasts and set goals. Their daily responsibility is to monitor activities to identify gaps between the goals and actual outcomes so that the gaps can be closed. Uncertainty plays a minor role. Managers know what is going on.

Every business is a set of assumptions that are taken as given, thus reducing the perceived uncertainty. The whole plan–execute cycle is a process designed to prove those assumptions correct. But assumptions are never totally right most often not totally wrong, either. Accordingly, it is quite seldom that ideas are turned into a successful business in just the way described in the business plan. Things change.

In conditions of rapid change and uncertainty, there have to be systematic processes indicating progress and new opportunities as they emerge. This is much more important than forecasting or planning. It is about testing the assumptions continuously and signalling which assumptions are helpful and which are not. It is about finding out repeatedly which of the efforts are creating value and which are wasteful. Are we on the right track? Are we progressing? What new possibilities have become visible?

Lean thinking defines value as providing benefit to the customer. Anything else is waste. But what if we really don’t know?  Then the most important business process is to find out. We have to learn what creates value for different customers in different situations. “Anything that does not contribute to learning is waste”  as Eric Ries puts it. The business challenge for a creative company is to learn fast and cheaply!

Management theory needs to leave behind the industrial, mechanistic model of reality and the belief in linear if-then, causality. The sciences of complexity, non-linear dynamics, uncertainty and creative learning are the foundations of modern, human-centric management.

The task of managers is not the reduction of uncertainty but to develop the capacity to operate creatively within it. Ilya Prigogine wrote in his book “The End of Certainty” that the future is not given, but under perpetual construction:

“Life is about unpredictable novelty where the possible is always richer than the real.”


Thank you Eric Ries, Stu Kauffman and Ralph Stacey

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.


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

Complexity and management.