Esko Kilpi on Interactive Value Creation

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

Category: New work

Lean interaction

In lean vocabulary, anything that does not create value experienced by the customer, anything that slows one down in serving the customer’s need, or does not contain potential for learning, is waste. Making something that does not solve the customer problem is waste. Waiting is waste. Any extra processing steps are waste.

The concept of lean has lately been transferred from manufacturing to other practices such as media services. People are used to lean thinking when it comes to technology and industrial processes, but it is still rare to understand what being lean means in communication-centric businesses. This is because many managers still trivialize the power of interaction.

We still don’t appreciate that work is communication: we live and work in a network of conversations. Being lean today means understanding that these conversations are never neutral. They always affect the quality and pace of the customer outcome. Communication either accelerates or slows down. Communication either creates value or creates waste. Communication can create energy and inspiration or can take energy away and reduce inspiration. Waste today means getting stuck or running in cognitive circles in the conversations we are having. Communication enables but also restricts

The sciences of uncertainty and complexity have helped us to understand that organizations can be seen as patterns of interaction between human beings. The interactions in the linear mass industries were very different from the interactions in the dynamic, unstable, Internet-based world. To cope with this, we need to learn to embrace unpredictability and complexity as inescapable constants.

Many managers still possess the skills that meet the challenges of static conditions. In a static, reductionist environment, you knew how each role fitted within the larger system. You knew how the repetitive process worked, and you didn’t want deviations. You knew what it took to make the products and you didn’t want people changing anything or inventing things . You wanted everyone to do their planned part and not get in each other’s way. When roles and organizational units are separated from other roles and units, communication is the task of the manager. You, as a manager, do the coordination and share the information necessary for each to make their planned contribution and nothing more.

In dynamic business conditions, the management practices described above are not only unhelpful, but cause damage and create waste rather than value. If you cannot predict you have to invest in real-time learning and iterations instead of predictions. Success is first and foremost based on the value of interaction, context awareness and responsiveness. What we still need to learn is that this responsiveness is not possible if we are many handshakes away from the customer context that we should respond to.

The agile manifesto points out that individuals in interaction are more important than processes and tools. Working prototypes are more important than documentation. Customer collaboration is more important than contracts and, most importantly, responding to change is more important than following a plan.

Knowledge is the act of interacting and new knowledge is created when ways of interaction, and therefore patterns of relationships, change. The creative assets of an organization are the patterns of interaction between its members. Assets are destroyed when relationships are missing or are dysfunctional.

Enabling new habits of communication and improving the quality of the conversation are today among the most important processes of lean development.

 

 

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.”

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Credits Chris Meyer and Stephen Downes

Sense making and protocols as the future of management

Management thinking is moving towards an understanding of human action as a process of sense making. What an organization becomes emerges from the sense-making relationships of its members, rather than being determined by the choices of few powerful individuals.

Management is historically seen as a collection of tasks involving planning, organizing, controlling and incentivizing. A competent manager is believed to be able to analyze organizational and task requirements plus the emotionally loaded human motivations. Successful management has then been able to remove conflict and uncertainty and accurately predict and plan the future.

The future is accordingly described as goals and performance targets. Following this logic, the role of management is to control the movement into a chosen future. But what management really is, is about reduction of anxiety. Anxiety levels in the individual experiences most often depend on the perceived level of control people have over themselves and their environment. This drives our need to believe that someone is, or should be, in control.

The opposites of being in control, such as responsiveness as opposed to planning, not knowing as opposed to knowing or differences as opposed to consensus should be removed by management. Success is equated with equilibrium.

However, the ability to do this in a complex world that is highly sensitive to the tiniest changes is questionable. Neither can rational causality be applied to humans because human action is not deterministic. The idealistic view of a manager as one who is in control is not consistent with our practical experience, or with modern science. From the point of view of the sciences of complexity, an organization is not even a system, but should be understood as a pattern, or as interconnected patterns in time.

These interconnected patterns are the results of self-organizing processes across the network forming the organization. Many events, local interactions generate emergent outcomes that cannot be traced back to any specific management action. Looking towards the future, we create what happens next, without knowing what will happen next.

The organization, then, is no longer self-regulating in a cybernetic sense, but self-influencing in a complex sense. Self-influence as a concept is not necessarily positive, it can lead both to self-sustaining and self-destructive behaviors.

The key management capability is not being in control, but to participate and influence the formation of sense making and meaning. It is about creating a context that enables connectedness, interaction and trust between people.

Most people believe that the role of leaders is to choose strategic directions and then persuade others to follow them. A modern view of strategy is about exploration and experiments, a search process of trial and error. The openness to the possible through the search process leads to having to live with anxiety and not knowing. Work needs to equal learning.

Protocols

Almost all management practices we have from goal setting to budgeting are cybernetic in the sense that quantified targets are set at some point in the future and the path toward the goal is planned and then controlled. Variance is continually fed back to determine needed management adjustments to bring performance back to the target path. The still dominant ways of management thinking are based on Newtonian dynamics with the belief that a manager can find leverage points for interventions to initiate a known change. The manager’s role is with these “if-then” causal rules.

What (cybernetic) management used to be, is tomorrow done by algorithms and the new enabling/constraining protocols. It is about individuals acting with each other according to the fewest number of rules that can produce global, emergent patterns of coherent, interactive behavior.

Post-blockchain smart contracts make possible, in economically viable ways, that person A can be part in the work/learning of person B. B again plays part in the work/learning of person C, who plays part in the work/learning of A. Work is by default networked cognition. Value creation is event-based and contextually highly interdependent cooperation.

No one agent is choosing the number and strength of connections for other agents in the network. While no agent can be in control of a complex system, it is evolving in a controlled manner because of the conflicting constraints, the differences in the network. This is why the goal is not to reach consensus. What an organization becomes emerges from the relationships of its members rather than being chosen by some individuals.

The fundamental dynamic of evolution is not competitive selection, but interactive cooperation. Management in the new economic spaces is then about self-influencing cooperation.

The changing system of skills and responsibilities

We have so far followed a very crude pyramid-like classification in work: skilled work was what highly educated individuals would do. Semi-skilled work was possible for trained people. Unspecified labor was what almost everybody could do after onboarding. This classification of work led to the unintended consequence that the most economical design of mass-era organizations reduces the amount of skilled work and increases the amount of less-skilled work, thus reducing costs. A bigger problem than low-skilled people is the low-skilled occupations we have created.

Classification of work as different bundles of skills and responsibility has been very easy to grasp and easy to follow in compensation schemes. More skills and/or more responsibility — more pay. Managers, who are responsible people, are given responsibilities — and higher wages. Workers are given less demanding tasks, less responsibility — and lower wages. The argument behind is a circular, self-fulfilling prophecy. People who are not made responsible tend to avoid responsibilities and therefore never become responsible. Skilled people acquire more skills because of the higher cognitive demands of their work. Less-skilled work roles give less opportunities for learning, leading in fact to a slow but certain de-skilling. This is something we see in many industries today. The phenomena and the causes of the problem become the same.

The organizational system of skills and responsibilities has been made on the assumption that all that has to be done can be known and managed with efficiency and insight. In mass-production, work corresponds mainly with what has been planned and budgeted. But today, in more contextual problem solving, work corresponds mainly with complex engagements with the customers.

The focus changes from generic skills to contextual presence, empathy and interaction.

The most modern definition of work is “an exchange in which the participants benefit from the interaction”. Interestingly, cooperation is also described as “an exchange in which the participants benefit from the interaction”.

The technological environment of work has changed fundamentally, but we haven’t yet developed a new mode of economic space design, neither have we escaped the pull of the traditional industrial system. Our relations at work are still asymmetrical involving status differences based on systems of responsibility and systems of skills. This inbuilt systemic fault generates increasing social distance and inequality, as we have now seen.

Due to the variety of contexts people work in, work requires interpretation, exploration and negotiation. The interpreter with the best situational awareness is the worker, working together with the customer, not a manager. The relations are built on symmetry.

What defines most problems today is that they are not isolated and independent. To solve them, a person has to think not only about what he believes the right answer is, but also about what other people think the right answers might be. Work, then, is exploration both what comes to defining the problems and finding the solutions. Again, the relations need to be based on symmetry.

Most decision makers are still unaware of the implications of the complex, responsive properties of the world we live in. Enterprises are not organized to facilitate interactions, only the actions of parts taken separately. Even more, compensation structures normally reward improving the actions of parts, not their interactions. This is why conventional jobs are increasingly inhibiting flexibility and contextual responses to new problem definitions or new technological solutions to old problems.

To succeed in the new economic spaces we need symmetric relationships, open assets and very open organizations.

When customers are identified as individuals in different use contexts, also the sales process is in fact a joint process of solving problems. You and your customer necessarily then become cooperators. You are together trying to solve the customer’s problem in a way that both satisfies the customer and ensures a profit for you. The industrial make-and-sell model required (explicit) skills as we still know them. The decisive thing was your individual knowledge and individual education. Today, in new economic spaces you work more from your presence and network than your skills. Work is interaction.

The really big objective of digital transformation is to reconfigure agency in a way that brings these relationships into the center. Success today is increasingly a result of skillful presence: it is about empathy and interaction. Through new technologies and ubiquitous connectivity, we have totally new opportunities for participation and communication in the new economic spaces

These economic spaces are about interdependent individuals and groups defining and solving problems in shared contexts utilizing smart contracts.

Individuals competing on job markets may be one of the historic mistakes we have inherited from the early industrial era. It made sense a very long time ago but now we should think differently.

Interaction creates capability beyond individuals. Cooperative performance can be more than what could ever be predicted just by looking at the performance of the parties involved. It is not about individual skills any more. Skills, performance and resilience are emergent properties of cooperative interaction. They are not attributable to any individuals. Higher performance is more a result from the quality of interaction than the quantity of training and education.

Networks provide problem-solving capability that results directly from the richness of communication and the amount of connectivity. What happens in interaction between the parts creates a reality that cannot be seen in the parts or even seen in all of the parts.

This is why it does not make sense any more to talk about skill levels and just managers being responsible. Either you are present in a relevant way or not. Neither can responsibility be somewhere else. You can only be present and contribute if you are response able.

Credits Nick Hanauer and Katri Saarikivi

A pattern language of post-industrial work

At the core of the post-industrial era is the idea that people should design for themselves. This principle applies also our value creating entities. This may sound radical but comes from the observation that most of the value on global scale is not created by firms but by people. People, then, should learn to be better designers. When designing something we always rely on certain patterns. We are in the midst of a shift from the industrial pattern of supply and demand to social, interactive patterns.

The customer is now seen as being directly and actively involved in the key moments of value creation as opposed to passively consuming value. There are profound implications that result from this change of thinking. Products and services are not reproducible as such any more. Solutions are by default contextual, but they can be starting points for someone else to create value. Creative, connected learning is at the core of the post-industrial business.

The most important principle is to build the organization around three design patterns: (1) Relations, (2) Network effects and (3) Solving problems /Asking questions.

Relations

Cultural homogenization is a theme of our time. It is apparent in fashion, food, music, and many services with a unified user experience. Everything is made to be basically the same everywhere. According to some psychologists, the desire for this sameness arises from anxiety about differences. This is one of the reasons why Gregory Bateson argued that the history of our time can be perceived as the history of malfunctioning relationships. More homogenization leads to more anxiety (when experiencing differences) which leads to more homogenization and the “differences that make a difference”, as Bateson put it, are lost.

Human behavior is learned in relations. Our brains are wired to notice and imitate others. Computational social science has proved that behavior can be caught like a disease merely by being exposed to other people. Learning and also non-learning can be found in communication. It is not that people are intelligent and then socially aware. Social intelligence is not a separate type of intelligence. All intelligence emerges from the efforts of the community.

To succeed you need relationships and interaction. When customers are identified as individuals in different use contexts, the sales process is really a joint process of solving problems. You and your customer necessarily then become cooperators. You are together trying to solve the customer’s problem in a way that both satisfies the customer and ensures a profit for you.

The industrial make-and-sell model required expert skills. The decisive thing was your individual knowledge. Today you work more from your network than your skills. The decisive thing is your relations. The new structures and new designs are about communities continuously organizing themselves around shared contexts, meaning shared interests and shared practices. The focus of industrial management was on the division of labor and the design of vertical/horizontal communication channels. The focus should now be on cooperation and emergent interaction based on transparency, interdependence and responsiveness.

The really big objective of the digital transformation is to reconfigure agency in a way that brings relationships into the center. Success today is increasingly a result of skillful participation: it is about how we are present and how we communicate. Through new technologies, applications and ubiquitous connectivity, we have totally new opportunities for participation and communication — potentially changing the way we work together.

Network effects

The new platforms can be a valuable, shared resource making value creation possible through organizing and simplifying participation. Sociologists have called such shared resources public goods. A private good is one that the owners can exclude others from using. Private was valuable and public without much value during the era of scarcity economics. This is now changing in a dramatic way, creating the intellectual confusion we are in the midst of today. The physical commons were, and still often are, over-exploited but the new commons follow a different logic. The more they are used, the more valuable they are for each participant.

The ongoing vogue of business design transforms asset-based firms to network-based platforms. The effects of Moore’s law on the growth of the ICT industry and computing are well known. A lesser-known but potentially more weighty law is starting to replace Moore’s law in strategic influence. Metcalfe’s law is named after Bob Metcalfe, the inventor of the Ethernet. The law states that the cost of a network expands linearly with increases in the size of the network, but the value of the network increases exponentially. When this is combined with Moore’s law, we are in a world where at the same time as the value of the network goes up with its size the average costs of technology are falling. This is one of the most important business drivers today.

The implication is that there is an ever-widening gap between network-economy companies and those driven by traditional asset leverage models. The industrial economy was based on supply-side economies of scale inside the corporation. The new focus is outside, in demand-side network economies.

The most important model is a network structure where the value of all interactions is raised by all interactions; where every interaction benefits from the total number of interactions. These are the new network businesses. In practice this means that digital services can attain the level of customer reach and network size, required to capture almost any market, even as the size of the company stays relatively small. This is why network-economy based start-ups have such a huge advantage over asset leverage based incumbents. The key understanding is that it is now the customers or members of the network who create value, not the network owner. The customer will be transformed from being an audience to an actor.

The central aggregator of enterprise value will no longer be a value chain. The Internet is a viable model for making sense of the value creating constellations of tomorrow. Perhaps the next evolutionary step in the life of the firms is a transformation from platforms to open commons with shared protocols. Perhaps Bitcoin/Blockchain is going to be part of the new stack, the TCP/IP of business.

Solving problems /Asking questions

Success in life has been seen governed by two concepts: skills and effort; how bright you are and how hard you work. Recently, researchers have claimed that there is a third and decisive concept. It is the practice of lifelong curiosity: “knowing what to do when you don’t know what to do” as Piaget put it.

The collective intelligence of our societies depends on the tools that augment human intelligence. We should welcome the fact that people today are smarter in large measure because they have invented and use smarter tools. Making tools is what human beings have always done. The interactions between tools and human minds are so complex that it is very hard to try to draw a line between humans and technology. Neither is it a zero-sum game where the human brain is losing to technological intelligence, but as technology changes, people and what people do, are necessarily changed.

Work starts from problems and learning starts from questions. Work is creating value and learning is creating knowledge. Both work and learning require the same things: interaction and engagement. With the help of modern tools, we can create ways for very large numbers of people to become learners. But learning itself has changed, it is not first acquiring skills and then utilizing those skills at work. Post-industrial work is learning. It is figuring out how to solve a particular problem and then scaling up the solution in a reflective and iterative way — both with technology and with other people.

The new design patterns create new opportunities. It is not about having a fixed job role as an employee or having tasks given to you as a contractor. The most inspiring and energizing future of work may be in solving problems and spotting opportunities in creative interaction with your customers.

The complex future of work

We live in an age of simplistic explanations. We build simple systemic models and crude abstractions. As a result, both our sense making and our decisions are built on an inadequate appreciation of the complex systems we are part of.

We have seen what it can lead to: industrial farming has caused a radical reduction of variety in nature in order to meet the goals of productivity. The simplification of crops was economically very efficient, allowing specialization in machinery and lowering the cost of learning, but it often damaged the local ecology in an irreversible way. The result was a fragile ecosystem, with a growing dependency on artificial fertilizers.

Every time we replace natural, complex systems with simplified mono-cultures we gain in short-term productivity, but at the cost of long-term resilience and viability. The less diverse a system is, the more vulnerable it is, and the more unsustainable it becomes.

Farming is now changing. New voices within agriculture say that “all farming takes place in a unique space and time”. These scholars claim that a mechanical application of generic rules and principles that ignore these contextual particularities is an invitation to catastrophic failure.

The principles of simplification still apply to the social systems of work: most of our firms can be described as mono-cultures. We also do our best to productize humans to fit the job markets. Many organizations are productive in the short term, but fragile in the long term. As long as the environment remains the same, simplified systems are very efficient, but they immediately become counterproductive when the environment changes even slightly. And it always will.

Our view of efficiency in firms still follows the line of thinking of efficiency on farms.

Job markets need standardized workers who are uniform in their skills and motivations. People are interchangeable labor. These people have no uniqueness. They have no original ideas to contribute to work. The focus is on the price of work; supply and demand.

In classical economic theory, markets are assumed to tend to a state of equilibrium. If there is an increase in demand, prices rise to encourage a reduction in demand and/or an increase in supply to match the demand. This is the principle behind Uber’s surge pricing. A market, then, is a simple cybernetic system: any significant change is self-regulating adaptation. There is no learning.

One-dimensional social designs have the same inbuilt risks as simplified natural designs. Simplified social systems can cause the same kind of damage to the human ecology as simplified farming systems have caused to the natural ecology. People become dependent on artificial motivation systems, the human equivalents of fertilizers. We call them incentives.

Just as all sustainable farming is now seen as taking place in a unique context, all human work takes place in a unique space and at a unique time. Human work is situated and context-dependent. It just hasn’t been understood that way. The digital architecture of this kind of work might resemble Amazon Dash buttons more than Uber.

Technological intelligence helps farmers to be more context-aware. Technological intelligence can do the same for human work. Mass systems were built on general knowledge and generic competences. Perhaps post-mass systems are going to be built more on situated knowledge and contextual competences.

An example of this might be the difference between the general knowledge of seamanship in open waters and the contextual knowledge of piloting. When a ship approaches land, the captain often hands over control to a local pilot, who then navigates the ship to the port. Pilots know well the dynamic peculiarities of the area, the winds and the currents. Much of this situated knowledge would be irrelevant somewhere else, at another harbor entrance.

A job market, as a concept, is a radical abstraction of human work. Every time we replace practical, local knowledge with general, standardized knowledge we gain in productivity, but at the cost of more environmental adaptation in the future. Learning debt is created and the whole system (of jobs) is less resilient and may even become dysfunctional. Short-term gains turn out to be extremely expensive in the long run!

The post-industrial era is too complicated to boil down into a single slogan describing work, but three scenarios seem to be emerging: (1) processes are automatized and robotized, leading to an algorithmic economy: (2) generic work is found through platforms, or turned into tasks circling the world, leading to a platform economy, and (3) context-specific value creation takes place in interaction between interdependent people, leading to an entrepreneurial economy.

I believe that the future of human work is contextual. Even after the captains are automated, the pilots may still be human beings. Even after the surgeons are robots, the nurses may still be human beings. Some people doubt this because there is some very advanced research going on that explores sensor technologies and responsive algorithms. The collaboration between sensors and actuators is getting better and better. Despite that, if you are a human being, it is better to be a tour guide than a travel agent.

It is a more profound change in work patterns than what the present platforms offer. It is not about employees becoming contractors. It is about generic, mass solutions becoming contextual and about interchangeable people who are now, perhaps for the first time, being seen as unique. The case for networked small units, such as human beings working together in responsive interaction, is stronger than ever. Local, contextual knowledge is needed not only for sustainability in farming but also at work.

What is most desperately needed is a deeper understanding of the complexity of life.

Farming is starting more and more with a true understanding of the particularities of the land. Work should also start with an understanding of the particularities of human beings.

Thank you Doug Griffin and James Scott

Digital Cultures

A friend who works with Artificial Intelligence told me: “It is possible that there are complex and conteaxtual things about humans, but in terms of intelligence it does not look that way. With the brain there is nothing that isn’t computable. The brain is a computer like any other.” I begged to differ and claimed, a bit flippantly, that our brains do much more than solve differential equations.

Our present digital culture is oriented towards the objective and the quantifiable more than the subjective and the qualitative. The software we work with reflects the analytical minds of the people who built it, such as my friend. The downside of all this is a possible failure to understand and capture the paradoxical elements of life.

Traditional science was a project that aimed to get closer and closer to certainty. The new sciences of complexity are making it clear that this is not possible. Complexity sciences present paradoxes as being normal in everyday life. The dominant scientific way of thinking tries to eliminate paradox. An encounter with paradox, such as seeing the same thing differently from different points of view, has been understood as a sign of not thinking properly and thus has led to attempts to resolve or eliminate the paradox. What the new sciences are suggesting is that the dynamic patterns of knowing are inherently paradoxical and context-dependent.

A new language is appearing as scientists attempt to describe the complex dynamics in which phenomena are no longer perceived as certain. Things are both predictable and unpredictable, knowable and unknowable at the same time. To force this complexity into a reduced number of cognitive patterns would be enormously repressive.

The question of what technology dealing with Artificial Intelligence is doing to our cognitive patterns has been the subject of strong opinions but few robust studies. Some scholars claim that the brain has always been adapting to new tools. New neural patterns emerged when people began speaking, reading or writing. Digital tools and software code are just the next step, they say. Man is seen as his or her own maker — a maker of life through new tools and new practices created by those tools.

The real question here is whether modern society is in effect de-skilling people in the conduct of the practices of everyday life because of our tools. We have more machines than our ancestors, but less idea of how to use them well. We have more connections with people, but less understanding of people who are not like us. Our social tools have in a way helped to re-create tribalism: solidarity with others like yourself (in your own echo chamber) and aggression against those who differ. Tribalism involves thinking you know what other people are like without really knowing them. Lacking direct, time consuming face-to-face experiences, it is easy to fall back on fantasies and stereotypes.

Digital tools have increasingly become our senses, our eyes and ears. Digitalization has given us amazing access to the world. But there are things it does not capture. The more people have superficial information about the world, the less they actually put themselves in the shoes of others. The psychological problem is that when we don’t know the history and the context behind something, we project those ourselves. When the context is stripped away, we add it back. We fill in the gaps in information when they are not there. It is so easy for us to comment very negatively on Twitter posts without any understanding of the context of the discussion. We don’t know much about the refugee crises, but we think we know, as we project our beliefs, fears and worries onto what is going on.

I am one of the people who claim that the new social technologies can also be used to solve these problems.

The concept of social skills often means that people are good at telling stories or accomplished at party talk, but there are social capabilities of a very serious sort. The social capacity of cooperation is more the foundation of human intelligence than differential equations are.

The next digital tools dealing with intelligence need to be more “dialogic”. The concept of dialogue has a very precise meaning. It is a discussion which does not resolve itself by finding common ground. Though no shared agreements are reached, people often become more aware of their own views and learn through expanding their understanding of one another and the different contexts of different people. We become more intelligent if the paradoxes are kept alive.

Cultural homogenization is a theme of our time. It is apparent in fashion, food, music, and many services with a unified user experience. Everything is made to be basically the same everywhere. According to some psychologists, the desire for this sameness arises from anxiety about differences. This is one of the reasons why Gregory Bateson argued that the history of our time can be perceived as the history of malfunctioning relationships. More homogenization leads to more anxiety (when experiencing differences) which leads to more homogenization and the “differences that make a difference”, as Bateson put it, are lost.

Unless you genuinely value the perspectives of others, and not just the ones that conform to your own, you are not going to understand them. Truly intelligent thinking is not just a means to an end: it has to be rooted in what we see as ends in themselves, the values by which we live.

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The past and the future of work

The most modern definition of work is “an exchange in which the participants benefit from the interaction”. Interestingly, cooperation is also described as “an exchange in which the participants benefit from the interaction”.

The way we view work life is influenced by the way we view the world. This view rests on the most fundamental assumptions we make about reality. In the present competitive view of the world, we often think that the most capable are those who are the most competitive, and accordingly that competition creates and secures capability and long-term viability in the world (of work).

But what if 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 lead to the handicapping of the system that these processes are part of. This is because competitive selection leads to exclusion: something or somebody, the losers, are left outside. Leaving something out from an ecosystem always means a reduction of diversity. The resulting less diverse system is efficient in the short-term, competition seems to work, but always at the expense of long-term viability. Sustainability, agility and complex problem solving require more diversity, not less.

As losers are excluded from the game, they are not allowed to learn. The divide between winners and losers grows constantly. Losers multiply as winning behaviors are replicated in the smaller winners’ circles and losing behaviors are replicated in the bigger losers’ circles. This is why, in the end, the winners have to pay the price of winning in one-way or another. The bigger the divide of inequality, the bigger is the price that finally has to be paid. The winners end up having to take care of the losers, or two totally different cultures are formed, as is happening in many places today. Psychologically, competitive games create shadow games of losers competing at losing. These start-ups are trained in jails and the pitching takes place on streets very far away from the Sand Hill Road.

The games we play have been played under the assumption that the unit of survival is the player, meaning the individual or a company. However, at the time of the Anthropocene, the reality is that the unit of survival is the player in the game being played. Following Darwinian rhetoric, the unit of survival is the species in its environment. Who wins and who loses is of minor importance compared to the decay of the (game) environment as a result of the actions of the players.

In games that were paradoxically competitive and cooperative at the same time, losers would not be eliminated from the game, but would be invited to learn from the winners. What prevents losers learning from winners is our outdated zero-sum thinking and the winner-take-all philosophy.

In competitive games the players need to have the identical aim of winning the same thing. Unless all the players want the same thing, there cannot be a genuine contest. Human players and their contributions are, at best, too diverse to rank. They are, and should be, too qualitatively different to compare quantitatively. Zero-sum games were the offspring of scarcity economics. In the post-industrial era of abundant creativity and contextuality, new human-centric approaches are needed.

Before Adam Smith wrote “The Wealth of Nations” and came out with the idea of the invisible hand, he had already written something perhaps even more interesting for our time. In “The Theory of Moral Sentiments” he argued that a stable society was based on sympathy. He underlined the importance of a moral duty — to have regard for your fellow human beings

Cooperative processes are about interdependent individuals and groups defining and solving problems in a shared context. Individuals competing on job markets may be one of the historic mistakes we have inherited from the industrial age. It made sense a long time ago but now we should think differently.

Interaction creates capability beyond individuals. Cooperative performance can be more than what could ever be predicted just by looking at the performance of the parties involved in a competitive game. The higher performance and robustness are emergent properties of cooperative interaction. They are not attributable to any of the parts of the system or to functioning of the markets.

Networks provide problem-solving capability that results directly from the richness of communication and the amount of connectivity. What happens in interaction between the parts creates a reality that cannot be seen in the parts or even all of the parts. What we have called the “whole” is an emergent pattern of interaction, not the sum of the parts.

The same principle explains why we have financial crises that no one planned and wars that no one wants. On the other hand, the great societal promise is that interaction in wide-area networks, with enough diversity, can solve problems beyond the awareness of the individuals involved.

What defines most problems today is that they are not isolated and independent but connected and systemic. To solve them, a person has to think not only about what he believes the right answer is, but also about what other people think the right answers might be. Following the rhetoric of game theory: what each person does affects and depends on what everyone else will do and vice versa.

Most managers and decision makers are still unaware of the implications of the complex, responsive properties of the world we live in. Enterprises are not organized to facilitate management of interactions, only the actions of parts taken separately. Even more, compensation structures normally rewards improving the actions of parts, not their interactions.

Work that humans do used to be a role, now it is a task, but it is going to be a relationship: work is interaction between interdependent people. The really big idea of 2016 is to reconfigure agency in a way that brings relationships into the center. The mission is to see action within relationships.

Amyarta Sen has written that wealth should not be measured by what we have but what we can do. As we engage in new relationships and connect with thinking that is different from ours, we are always creating new potentials for action. In competitive/cooperative games the winners would be all those whose participation, comments and contributions were incorporated in the development of the game.

Work is solving problems and learning is answering questions

Studies predict that nearly half of all jobs and over 70% of low-skill jobs could be susceptible to computerization over the next two decades. Our chances of creating work for human beings in this new, demanding environment will be very limited if old and unjustified assumptions about what people can or can’t do are not examined. If we continue to assume that some people are born intelligent, while most are not, and continue to see intelligence as a fixed, personal possession, the options for large-scale systemic changes will be few.

If, on the other hand, we were to visit recent findings of neurosciences, relational psychology and computational social science, we would see intelligence as something more fluid. Then a whole different set of opportunities would become visible. Perhaps a bigger problem than low-skilled people would be the low-skilled occupations we have created.

There is a misunderstanding of the relationship between “nature” and “nurture” as causes of our intelligence. In most cases, genes do not establish limits that determine the space for personal growth. Recent scientific findings show that everyday life plays a role in defining how and when the genes themselves are expressed in us. Genes, the nature, take their cues from nurture. Environmental influences can be less reversible than genetic ones.

There is another argument than the science of genes about whether intelligence is fixed or can be expandable. Many people tend to think that they live their life with a fixed-capacity. Some people think differently. They have a growth mindset, as Stanford professor Carol Dweck calls it. They think that minds are like bodies: people come in different shapes and sizes, but everyone can benefit from exercise.

Individuals who believe that they can grow, tend to enjoy challenges. They like pushing themselves because they think that struggling leads to something good. People who think that their minds are fixed often see challenges as a threat to their imagined level of ability. They don’t like having to try new things, or making mistakes, because they interpret that as evidence of inadequacy.

These mindsets come from the way people around us respond to our successes and failures. Belief systems are contagious. If, over an extended period of time, people are treated as if they are intelligent, they actually become more so. The opposite can also be true.

Success in life has been seen governed by two concepts: skills and effort; how bright you are and how hard you work. Recently, researchers have claimed that there is a third and decisive concept. It is the practice of lifelong curiosity: “knowing what to do when you don’t know what to do” as Piaget put it.

The collective intelligence of our societies depends on the tools that augment human intelligence. We should welcome the fact that people today are smarter in large measure because they have invented and use smarter tools. Making tools is what human beings have always done. The interactions between tools and human minds are so complex that it is very hard to try to draw a line between humans and technology. Neither is it a zero-sum game where the human brain is losing to technological intelligence, but as technology changes, people and what people do, are necessarily changed.

To benefit from technology, we need resourcefulness. It means to be constantly looking for new tools with which to augment our intelligence. It also means new services: if you have a smartphone in your pocket, you should have an easy access to education in your pocket. Smarter and smarter tools surround us, but if we don’t want to learn the new practices and take up the new roles that the new technologies make possible, they might as well not be there. It is sometimes not easy, because the challenge with new technologies is, what is called “functional fixedness”, our inability to see more than the most obvious use cases.

There is more to being intelligent than using the latest technologies; how we interact with others is a crucial element of how smart we are in practice. Intelligence is social and arises in communities and communication. The world has never been a more networked place, and yet schools and workplaces still focus on individuals. That needs to change.

Human behavior is learned in relations. Our brains are wired to notice and imitate others. Computational social science has proved that behavior can be caught like a disease merely by being exposed to other people. Perhaps you can catch intelligence from others the same way? Learning and also non-learning can be found in communication. It is not that people are intelligent and then socially aware. Social intelligence is not a separate type of intelligence. All intelligence emerges from the efforts of the community.

Work starts from problems and learning starts from questions. Work is creating value and learning is creating knowledge. Both work and learning require the same things: interaction and engagement.

Scientists have discovered that learning is learnable. With the help of modern tools, we can create ways for very large numbers of people to become learners. But learning itself has changed, it is not first acquiring skills and then utilizing those skills at work. Post-industrial work is learning. It is figuring out how to solve a particular problem and then scaling up the solution in a reflective and iterative way — both with technology and with other people.

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From the industrial economy to the interactive economy

Over the past years, mobile technologies and the Internet have laid the foundation for a very small size, low-cost enterprise with the potential for managing large numbers of business relationships.

The impact of these new actors has been hard to grasp because we are used to thinking about work from a different perspective. Our thinking arises from a make-and-sell economic model. Most managers still subscribe to this and think that the core of creating value is to plan and manage a supply chain. A supply chain is a system of assets and transactions that in the end make the components of the customer offering. At the beginning of the supply chain are the raw materials and the ideas that start the sequence leading, hopefully, to a sale.

This is now being supplanted by a different paradigm; a relational, network approach enabled by new coordination technologies. The manufacturer may even be just one of the nodes in the network and the customer is not a passive consumer but an active part of the plan.

The old model companies are ill equipped for this digital transformation. Mass-production and mass media organizations are still much more prepared to talk to customers than to hear from them, not realizing that one-way communication was just a fleeting accident of technological development. It is not that customers didn’t have needs and reflections they would have liked to communicate.

We are passing through a technological discontinuity of huge proportions. The rules of competition may even be rewritten for the interactive age. The new interactive economy demands new skills: managing the supply-chain is less important than building networks and enabling trust in relations. You could perhaps call the new reversed sequence an on-demand-chain. It is the opposite of the make-and-sell model. It is a chain of relationships and links that starts from interaction with the customer and leads up to the creation of the on-demand offering. As Steve Jobs put it in a different context: “you start with the customer experience and work backwards to the technology. You can’t start with the technology and try to figure out where you’re going to try to sell it.”

Adapting the interactive model is not as easy as identifying customer segments or a niche market because communication can no longer be confined to sales and marketing, or to the ad agency, as in the make-and-sell model. Also to talk about a “segment of one” is misleading because one-way communication changes here to true two-way dialogue. The interactive enterprise must be able to integrate its entire network around the needs of each individual customer context. The on-demand-chain means continuous on-demand learning and continuous change. Your dialogue with an individual customer will change your behavior toward her and change that customer’s behavior toward you. People develop together in interaction.

A learning relationship potentially makes the whole network smarter with every individual interaction creating network effects. Accordingly, the enterprise increases customer retention by making loyalty more convenient than non-loyalty as a result of learning. The goal is to create more value for the customer and to lower her transaction costs. This kind of relationship ensures that it is always in the customer’s self-interest to remain with the people who have developed the relationship to begin with. The main benefit for the network partners may not be financial. The most valuable thing is to have access to “community knowledge”, a common movement of thought. It means to be part of a network where learning takes place faster than somewhere else.

In the mass-market economy, the focus was to create a quality product. With increased global competition and with so many quality products around that is not enough any more. To succeed you need high-quality relationships. When customers are identified as individuals in different use contexts, the marketing process is really a joint process of solving problems. You and your customer necessarily then become cooperators. You are together trying to solve the customer’s problem in a way that both satisfies the customer and ensures a profit for you.

The relational approach is the third way to work. It is not about having a fixed job role as an employee or having tasks given to you as a contractor. The most inspiring and energizing future of work may be in solving problems and spotting opportunities in creative interaction with your customers.

The industrial make-and-sell model required expert skills. The decisive thing was your individual knowledge. Today you work more from your network than your skills. The decisive thing is your relations.

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