Literacies for the networked society

Society is making its way through a profound change. We are leaving the industrial age and entering a new paradigm - one which we could name the network society. 
The main characteristic of this is connectivity. Humans, machines, cultures and economies are connected ever closer, more often, in more detail and across greater distances. Developments in completely different realms are increasingly influencing and interfering with each other.
Acceleration is another characteristic. As each new technical development helps us taking the next step even faster even change itself is accelerating. 

The network society offers us countless new opportunities - but at the same time it confronts us with a demand for new competences. Clearly, the type of work that we will be making our living from in a globally connected hi-tech world will be very different from the work which our welfare society was built upon.
We need a new approach, one that's suitable for the new rules of the game. We need a different perception of the mechanisms which drive change - and we need to weed out old concepts and structures that have become irrelevant and which often end up as barriers to necessary change.
All of this implies a deep cultural shift in attitude and understanding of learning, innovation and action - a shift that's needed all the way from kindergartens to retirees. 

In primary schools one uses the term ”literacies”, to describe the basic skills that are necessary in order to get by in society. In Denmark the law emphasizes 5 literacies: reading, writing, math, English as second language, and basic IT skills. If you lack one or more of these, you will experience that the doors to further learning are shut.
But what's considered basic literacies obviously changes as the conditions in society change. As we shift from the industrial society to a networked society we will need to acquire a number of new, basic competences to supplement the old literacies. 

Put briefly, these new competencies are about understanding the way in which globally connected, complex, dynamic systems work. The internet, international politics, stock markets, or living systems, like our body… These are the sort of complex systems that we need to engage with, and our future welfare will depend on our ability to do so in smart ways.
We will need a completely internalized understanding of the mechanisms that drive such systems. At the moment it may sound very abstract, but concepts like evolution, non-linearity, feedback, self organization and ecology will increasingly be crucial to us in order to asses situations correctly and to act intelligently in relation to the forces that shape our circumstances.

We are becoming particants
Just a few decades back much more of the information one received would concern things that had already taken place. These days, there's a strong trend towards ever fresher information. Increasingly, we receive information in real time and we can observe events as they take place.
This trend will become even stronger as sensors, cameras and data communication spreads to every conceivable device and every corner of the planet. 

The distribution and production of information is changing as well. An increasing part of media consumption is becoming interactive. Rather than the old monolog from a central broadcaster we are entering into dialogues among a large number of participants. 

Consequently, we have much greater possibilities to engage and affect that which we are informed about. A large and growing number of people are no longer staying in the role of a passive audience. Instead, in many contexts, we see a blending of roles that were traditionally clearly separate: Sender/receiver, actor/audience, politician/citizen and teacher/pupil... The lines between them are blurring. We are becoming participants, and not just in the media realm.

From product to process
In the industrial economy, the focus was on providing reliable functionality through mass production. In the knowledge economy, functionality is taken for granted. The fact that a car is good to drive or that a fridge can keep food cold is not enough to compete anymore. Rather, companies must differentiate themselves by trying to match individual consumer's needs and desires, as precisely as possible, even as the circumstances and contexts of the consumer changes. 

Ideally, this is done by creating goods and services that consumers themselves can modify and customize. In this way, companies go beyond merely producing and selling finished products. Instead, consumers are offered access to participate in an ongoing process. 

The world is turning interactive. Not only do our personal choices increasingly determine the look or functionality of the products or services we use. At work it is no longer enough to merely follow orders and do what we usually do. Whether the job we do has relevance and value to others is a matter of our personal creative efforts.
This freedom to be participants and co-creators is also a demand, because if you don't use the opportunity, you will fall behind relative to those that do. Or, you will end up un-employable in a world in which routine work is automated or outsourced to where labor is cheapest.

Innovation is a mindset for change
Constant change is a basic condition of everyday life. The globalised world will not stand still.
As consumers we demand products and services that are ever cheaper, better and more precisely tailored to the changing circumstances we find ourselves in.
As producers, we are faced with a global competition of trying to fulfill those rapidly shifting demands. 

No wonder that Innovation is such a buzzword. We need to be innovative in order to invent new solutions and apply our knowledge to changing circumstances in creative ways.
However, Innovation should not be seen as a separate discipline but rather a more general approach; a mindset for change. We need to learn to feel at ease with change, and to think creatively in terms of probabilities and ad hoc solutions - rather than fearing the disruption of certainty and stability.
Perhaps the hardest part of this is to understand that sticking to the old, and saying no to anything new and different, may not be the safest strategy in a changing world - as the music industry and the manufacturers of such products as typewriters, film-based cameras, etc., can testify.

From an educational point of view this mindset could be supported by presenting the world as something which is under continuous change - a development each of us has the possibility to affect. 

We live in a time in which we increasingly will be expected to act and develop in relation to the contexts we are part of. Thus, we should not merely accept the facts and circumstances that we are presented with as static, but rather see them as stages of a continued process.
This requires that one understands the history that preceded the present, and that one understands the forces that are driving development.
It implies that one decides if a situation is satisfactory and good, or if it should be changed. And it requires that one feels relatively at ease dealing with uncertainty and change. 

This all means that teaching innovation would have 5 distinct assumptions integrated: 

o Understanding the world as being under continuous change
o Understanding that each of us has the possibility and is under demand to participate in the further development
o A critical, assessing approach to the existing conditions
o Knowledge of the tools that are necessary in order to act in relation to the development
o A physical environment that can be changed and which invites its users to create and experiment

Lifelong learning
Obviously, accelerating change makes it problematic to think of knowledge and education as something finished and lasting, a product which can be delivered on a mass basis. Facts change, as do the skills that a job demands. Education itself needs to be seen as a process; an ongoing activity in which the real and basic competencies taught is the will and the ability to learn new facts and skills as the world and individual circumstances change.

Lifelong education is an attitude we must adopt, a realization that people need to be active and take it upon themselves to continue building new knowledge and challenge the old assumptions, even if people are not formally enrolled in a school anymore. And just as the roles of producers and consumers will blur in industry, both teachers and students must change their roles; teachers must not treat their students as passive recipients of facts, and students must see themselves as active participants in creating knowledge by exploring, experimenting and discussing.

From mechanics to biology 
 Not only must learning be seen as a continuous process, but a lot of what people need to learn will be about processes: the way things change and the mechanisms and interactions that determine the outcome of change. 

Our scientific tradition has been one of simplification and reductionism: one would examine complex phenomena by splitting it up and studying the components. However, with the development of powerful computers capable of visualizing enormous data sets it has become feasible to study whole, dynamic systems. Scientists can now examine the interaction between the multitude of factors and actions in a system while it is changing - and this has been a major advance towards a more adequate description of reality. 

The paradigm of the industrial age was Newtonian, with simple and clean formulas describing the mechanics of the world. Like a machine, the world was seen as predictable, running as the engineers had designed, more or less unperturbed by events. It was a world with clear-cut answers and rigid hierarchies. The new model is biology. The world, it turns out, is actually a messy ecosystem of complex interactions which can only be predicted with some probability. A rapidly changing global system of interdependence and feedback loops. So, this is where we will be focusing much more; on the changes, processes and interactions - rather than the static part.

This new point of view is needed. The traditional, reductionist approach has clearly become inadequate in order to understand the defining phenomena of this era: The climate, the communication networks, genetics, ecology, consciousness or even life itself. These complex dynamic systems are more than the sum of their parts; they have a decisive qualitative dimension which emerges in the interactions between those parts. 

Understanding systems and processes like these is very different from memorizing facts. Students need to see information in a wider context and to understand the underlying interactions that are changing and shaping the world. Understanding processes allows people to take principles and observations from one field and applying them in other fields; another important skill when disciplines and industries that were once separate are converging and mixing in new ways. 

The paradigm shift described in this essay is not an absolute shift to a completely different logic. This is NOT an argument that facts and specific skills are not important. The point here is that facts and traditional curricula are increasingly in-adequate. Our learning must be augmented by the perspective of change, holism, complex interactions and connectivity. It's a gradual change. We need less linear thinking, more holistic and networked thinking. Less either/or, more both/and. Less single and exclusive truths, more multiple solutions and answers. Less control, more emergence. 

Freedom and responsibility
 As the world becomes closer connected we become ever more interdependent. We affect each other more directly in a close global interaction - but paradoxically, we are also experiencing a growing individualization. More of us are reaching a level of education and wealth at which we can begin to demand a great deal of individual freedom and service. But the individualization also implies that we must personally take initiatives and assume the responsibility for our choices and action - or lack thereof. We must learn to take a pro-active approach - for instance in maintaining our skills or our health. 

However, the demand that we each contribute with something original, doesn't mean that we all must work every man to himself. To the contrary; the lone, eccentric inventor is not the way most development happens. In reality, most innovation and development takes place in networks, and the individual needs to see his or her efforts as parts of larger context. The keywords for success are participation and interaction. In order to be part of the process, we need to be curious and explorative, willing to share experiences, able to communicate, and receptive to input from others.

Importantly, a new layer of abstraction has been added; as the digital networks develop we will be interacting in environments that increasingly are a seamless blend of the physical and the virtual.
Readiness to interact and make choices is what will divide the first and second tiers in future societies. The first tier will be those with a predominantly active approach to the many choices and possibilities. They will use interactivity as a strong tool to pursue their own goals. They will not be led by what choices the menu happens to offer, instead they will explore and demand exactly what they need to move forward in their circumstances. The second tier will be those who mainly just passively accept what is suggested, without raising questions or making demands that exceed the agenda set up by the system. 

The key words are assuming responsibility. What people achieve from interacting with the system will depend on taking part in defining the direction they want things go. As the opportunities for interaction increase and co-creation increase, the information society will demand much more personal initiative than the industrial society did.

What does it mean to be human? As technology develops to the point of matching peoples' own capabilities there is a need to try to understand and maintain what it means to be human. We are faced with the fundamental question of who we are, and what we want to do.

The American scientist in the field of robots and artificial intelligence, Hans Moravec, has compared qualifications to a landscape of mountains and valleys. He describes technological development as water, gradually rising and flooding the lower lying areas. The areas that are left sticking out of the water are the skills and abilities where humans are superior to machines.
Large parts of this landscape have already been flooded. Machines are better at remembering a whole telephone directory, crunching mathematical algorithms or doing monotonous and physically demanding work. Consequently, there are lots of tasks where people are not used anymore.
The water level keeps rising, flooding even areas that we used to think of as the exclusive domain of academics, even those with high levels of education are threatened with losing their jobs, as machines become capable of doing them cheaper and better.
This raises the question of which skills will stay above water level the longest? What are the abilities which machines will match last, if ever?

The following example gives a hint at answers. Imagine someone, who, feeling ill with a sore throat, visits a doctor to check if they have a bad infection. During the consultation the doctor took a swab sample from the throat and placed it in a little test dish for two minutes. The test turned out to be positive; there was an infection. The doctor prescribed penicillin, and then the patient collected the medicine from the pharmacy.
In principle, at some point in the future, this consultation may be handled automatically. The patient may consult a machine by simply breathing into some sensor at the top, and then waiting for the pills to be dispensed at the bottom. If all that the doctor does is to follow a procedure along the lines of, if positive then prescribe penicillin or if negative do not, then the doctor will be in serious danger of being replaced by machines, regardless of the number of years of hard study and training.
Another possibility is for the doctor to engage as a human being, wondering why this was the third time in a year the patient was ill. The doctor may probe deeper by asking if everything was all right at home, if the patient was stressed, if the children were well, and if the patient was eating properly; trying to understanding the deeper reasons for the illness. 

A machine will not be able to conduct this kind of examination very well, but human doctors can do it because they know the patient, and because they are human and can intuitively relate to patient's circumstances. This is an ability that doctors get by growing up as a human, in addition, of course, to being trained as doctors. 

Likewise, a teacher, an architect or a salesperson can deliver much higher quality work and service towards customers by using some of the skills they have by virtue of being human. As we increasingly will interact with each other as representations on machines, it becomes crucial to remember to keep acting as a human and treating others as humans - not as if they were machines. 

Machines will have machine values These human abilities discussed above are rooted in the way people are. How people see their surroundings, and peoples' needs and desires, are closely linked to the physical size of the human species, to the fact that humans walk on two legs, that they are mammals, and that they have lived their lives in interaction with other humans. All this taken together, equips people with a shared understanding of each other, an understanding which machines cannot fully participate in. 

The way a snail comprehends the world is determined by the fact that it senses through its tentacles and its tongue, and has to use its muscles to slide along the ground. Similarly, a machine, to the extent that it really will attain independent thinking and consciousness, will be shaped by its physical makeup. If machines develop values, these will be specific to machines, just as human values are specific to humans.
Humans feel hunger, cold, fear, boredom, ageing, sexual desire, etc., in ways that are rooted in the body, and this is reflected in aesthetic and ethical values. There are things that humans may value highly, but which machines will have no preconditions for understanding. 

The problems arise, when machines increasingly take decisions and act as experts on behalf of people. In these circumstances there is a risk that human values will be left entirely out of the equation, unless there is an insistence that they must be included and maintained. It is an ongoing power struggle between humans and their creations, in which people need to secure that the highest priority will still be given to the values and virtues that people want and excel in. If the battle is lost, then there is a risk of becoming both unemployed and feeling homeless in an in-human world. 

Often, human values are hard to translate into monetary terms, and therefore they are easily forgotten and ignored in the competition of the market economy. Insisting on human values thus implies that everyone, to some extent, thinks and acts beyond the purely rational and economic logic. A very difficult thing to do, indeed. 

Love and creativity What are these human values? Creativity has been mentioned. Creative thinking helps people develop by finding solutions that transcend the linear mechanical logic of machines. Creative thinking is about invention, new ways of working, new understandings of the world, and solving problems, we've never faced before. At a deeper level, creativity is what elevates existence to a higher level than stark necessity. Creativity adds beauty and aesthetic pleasure to life: concepts that are specific to humans. 

But greatest among human values is love. Love has many faces. In its everyday version, love is expressed as care, service, solidarity, empathy, understanding, forgiveness, respect and belonging. It takes time to experience love. Time to meet, time to build trust, time to be anything but efficient. Feeling love adds quality and meaning to life. It is a good and a necessity, but it is very hard to quantify and include in the program of machines, and therefore there is a tendency to leave it out.

Love and creativity are values that people increasingly demand as they climb up Maslows hierarchy of needs, moving from survival and basic physical needs towards the need for self-fulfillment. Love and creativity are also values that only humans can deliver in a satisfactory way. 

There is a choice. On the one hand, technology can free people from tasks that are exhausting and unpleasant. On the other hand, there is a risk of losing out to a system that has been turned against humans, because people did not leave room for the deepest values when the goals were defined.
As in the earlier discussion on assuming responsibility, the issue is one of defining our own goals, and then using technology as a tool to achieve them. Otherwise, the system will default to providing what it thinks is best: but what is best for a machine is not necessarily what is best for a human.

New Learning environments
Naturally, the new understanding and the demand for new competencies, that are described in the first part of this essay, should be reflected in the world view and the approach that we instill in our children through school. In a dynamic world we need learn about the nature of change and participating in change.
This would require that we change the way we teach. Certainly, our understanding of the world must still be solidly based on knowing real, specific facts. But the specific knowledge should be put into a greater, systemic context - and achieving this level of understanding will in turn require a very different learning process and learning environment. 

The educational method should emphasize that the pupils cooperate, investigate, experiment, work in a cross disciplinary fashion, and use their body and senses. The pupils should experience the mechanisms of interaction and change through simulations, games, experiments, projects, cooperating and communicating - thus achieving an intuitive grasp of concepts that might be forbiddingly complicated at purely theoretical level. 

The learning environment should be inviting and supportive of this type of processes. In order to emphasize cross disciplinary and to train the ability to transfer observations and experiences between a variety of problems and contexts, we should integrate and coordinate the many formal and informal environments in which learning takes place. Learning does not just happen in schools, but at museums, at home, or by visiting businesses or research environments. Also, a significant and increasing part of learning is not based in the physical part of reality. It's virtual, using the media or participating in online activities. Thus, media and online universes should be much better integrated into the educational process.

Learning will be extended in time. Since the world and our understanding of it changes so quickly, learning needs to be a continuous, lifelong process. Ideally, learning should not be separate from work and leisure. We must extend and open up the educational system in order to interact with people through all the stages of careers. In short; we must create a culture of learning.
Furthermore, in order to measure and expose whether students have in fact learned these new competences, we need to develop new forms of assessment.

Systems thinking 
 The curriculum itself needs adjustment. First of all, much greater emphasis should be put on the understanding of general, systemic mechanisms which pupils can observe and apply across the traditional boundaries between disciplines. The systemic approach is not in contradiction to the learning of specific facts. The following example of an educational course illustrates how the systemic learning can be achieved while teaching specific facts from traditional disciplines along the way. 

For this example we've chosen feedback - a mechanism that is fundamental to the study of complex, dynamic systems:
Feedback is important to understand in order to asses how fast a situation can change when the interactions of a number of factors and agents result in a self re-enforcing effect - or conversely; how other systems are able to maintain balance and homogeneity even under intense pressure from the outside. Feedback is a result of the connectivity between the parts in a system. The more factors are connected and the tighter they are coupled, the stronger the effects of feedback can be. 

There are two types of feedback. Positive feedback amplifies a tendency towards change. Inversely, negative feedback is stabilizing, by compensating for changes that might alter an established equilibrium. Finally, the effect of feedback can vary depending on the length of the delay from when a change occurs till the signal of that change affects the ongoing process.
Feedback can be explained through examples from a variety of disciplines. 

Examples of positive feedback are: Climate change: When snow melts, the surface of the earth becomes darker. Therefore it absorbs more heat, and this in turn makes even more snow melt. As the enormous Siberian tundra is thawing, methane - a greenhouse gas - gets released from the ground, thus amplifying the greenhouse effect. In the field of economy as well as in culture, we experiences sudden fads and fashion crazes when the fact that many people choose a particular strategy in itself will attract many others to make the same choice - whether it is buying certain stocks, garments, watching a particular movie or choosing a spot for holidays. This has led to a lot of interest in so called ”tipping points” at which a particular tendency achieves the critical mass needed to start re-enforcing itself. 

In practical terms one can experience positive feedback by creating acoustic feedback, where a high pitched tone emerges when a microphone is placed sufficiently close to the loudspeaker that plays back what the microphone is picking up. Similarly, one can experiment with the beautiful video feedback patterns that emerge when a camera is pointed towards the monitor that is showing the output from the camera. 

Negative feedback can be exemplified with a thermostat. If the temperature becomes too high, the supply of heat is cut off. If the temperature becomes too low, the heat is turned on. In that way the system keeps the temperature within a certain temperature range. In biology there are many examples of systems that ensure homeostasis - stable conditions. For instance, the body has a number of mechanisms to keep temperature constant. In many an organization one can experience a great deal of inertia. Each person or department wishes to maintain it's activities and existence, so even if an organizational change is necessary for the survival of the whole, it can be resisted and defused by the individual departments of the system. In some cases this inertia is a deliberate part of the process; for instance in law-making. Laws take time to change, in particular the constitution. 

The lag time between stimulus and response within the feedback loop can have a strong influence on the outcome. The pattern of spreading of a disease during an epidemic depends on the incubation time - that is, how long it takes from a person catches a disease and till that person becomes so ill that one becomes aware of the problem. In politics and media, information is spread almost immediately now. It is possible to make decisions and respond to challenges very quickly - however this carries the risk that reactions and rumours can suddenly escalate completely out of proportion because there is no time for reflection or filtering the flood of information. 

Within retail one tries to avoid the ”whip-lash” effect which occurs when plans are made in response to data that have already changed by the time the plan is executed. For instance, a manufacturer is notified of a strong increase in demand for a certain item, but that demand may have cooled off by the time the manufacturers has ramped up production to meet it. 

At a practical level most people know the delay in signals from regulating the temperature of the bathing water. At first the water is too cold, so one turns up the hot faucet. This makes the water too hot, so one turns up the cold faucet - and probably too much. Because of the delay in the reaction one has to home in on the correct temperature through a number of adjustments. 

These examples of feedback illustrate that it is possible to explain a general phenomenon based on well known everyday situations. One can learn facts about specific subjects from the examples, but the crucial part is to move beyond specifics and demonstrate how the exact same general mechanisms are manifest in many other, very different contexts. The point here is that by learning the general mechanism from several, different contexts, one can better recognize and apply that mechanism when it's encountered later in some new context. 

Furthermore, by drawing on examples from a variety of contexts, it's possible to explain the mechanisms in different ways, thus hopefully reaching more students: By drawing an effect, through mathematical descriptions, through anecdotes, by experiencing it through physical experiments, and by observing and reflecting on experiences in everyday life.
In the same fashion as the explanation of feedback, it is possible to create courses that illustrate the other core mechanisms that are crucial to be familiar with in order to understand complex, dynamic systems. 

The following will, briefly, present some of the elements that could be included: 

Evolution
Darwin described evolution in biology, but the principle of the survival of the fittest could just as well be used to describe the gradual development of new product features in a market place - or how ideas or social norms develop in culture.

Evolution is a mechanism which can develop and optimize without having any central authority or goal. Evolutionary pressure can explain how resistance develops, for instance in a medical treatment in which some of the harmful bacteria that were supposed to be killed by a specific medicine happen to survive. The strain of bacteria that is resistant to the medicine will have plenty of opportunities to thrive after its relatives have been killed off. 

 The evolutionary mechanism can also explain why systems with a great diversity are less vulnerable to invasions from new organisms or diseases that are better fit to exploit a particular ecological niche. If the system is a monoculture, this means that the whole system could be taken over - while a diverse population would mean that there are limits as to how large a share of the system a single new, dominating species can invade. 

This is obviously crucial to be aware of in agriculture, but the exact same principal considerations are relevant in order to maintain a vibrant culture, or in order to fight computer virus in a global net, dominated by a small handful of software solutions.

In practical terms one can experiment with evolution by using computer programs based on so called genetic algorithms. In these programs virtual creatures can be optimized to solve a particular problem - without any human efforts at programming. In stead the systems breeds better algorithms by using what nature uses: a combination of random mutations and the mixing of genes that, over many generations, creates a better fit to solve the task. 

Emergence
 ”Emergence” or ”self organization” are terms that describe how elaborate and complex functions can appear through the interaction between many simple functions. We know the phenomenon from ant hills or bee hives, where the actions of an individual ant or bee follow extremely simple guidelines - but where the collective interaction never the less results in an amazingly advanced, flexible and robust system at the macro level. 

Emergence can be a social phenomenon as well. The widespread use of mobile phones and internet connections have led to what's called ”smart mobs” - when groups are suddenly organized around a specific political cause or some special interest or hobby. ”The wisdom of the crowds” is a related internet phenomenon, perhaps best illustrated by the online encyclopedia Wikipedia, a work of reference of surprisingly high quality, even though it is produced by a large number of amateurs at a minimum level of central planning. 

Because self organization emerges through a dynamic interaction, it is precisely the emergent phenomena that one misses, when studying a system from a reductionist and static point of view. Therefore, to observe the emergent qualities that are an important feature of any living system, one needs to use a holistic approach. 

In a religious context, one could argue that the difference between believers and atheists is whether you believe that the universe is governed by some metaphysical power - or if reality is basically a great machine running an enormous number of large and small processes, each of which could in theory be mapped and explained. Emergence could be seen as an interface between the explicable and the divine. Are life and consciousness emergent properties, the random result of an enormous number of simple interactions - or have they been put here intentionally, by some divine designer?

In practical terms one can experience emergence by looking at the individual images of a movie, very slowly at first, but gradually speeding up to the point where the pictures suddenly ”come alive”. Another practical experiment is to listen to the individual tracks of a multi-track recording of music. Depending on how you combine the tracks, the music can change its character completely. 

Self organization can be observed in the various archetypical patterns that show up in innumerous different natural contexts and scales. The hexagonal pattern from beehives are an example. The same pattern can be found in the shield of turtles and in the structure of crystals. It emerges because it is the optimal way to achieve an extremely stable, scaleable construction with a minimum of materials. That's way engineers often mimic the patterns that have emerged in nature - for instance, the hexagonal pattern is used when constructing the core of airplane wings. Fibonacci-numbers is another pattern which can be found in countless contexts in nature - a relationship which can be observed in the spirals of seashells or in the way the seeds of sunflowers are arranged. 

Arguably, the most amazing patterns are fractals. They are basically very simple mathematical algorithms which can unfold into ever changing complex graphic patterns. Fractals can be visualized on any PC using a simple program, and by changing the parameters of the program it's possible to experience another characteristic of complex, dynamic systems: That even tiny changes in the initial conditions can lead to dramatically different developments when the program unfolds. 

Probability vs. Certainty
Complex dynamic systems can fall into completely unstructured conditions - known as chaos. Chaos is a state from which it is impossible to predict the outcome. In fact, there is a lot more chaos in the world than one might imagine, but you have to know where to look for it - or rather; you have to examine the system at the right scale. Intuitively, a brick wall seems solid and structured, but if you look very close, it is composed of atoms, in which the exact placement of the individual electrons is impossible to predict. The movements of electrons are chaotic, still there is enough of a pattern in the movement, that we describe orbits in which there's a great likely hood that the electrons will be present. Large complex systems behave in much the same fashion. It is impossible to predict the local weather accurately, but the general climate can be predicted with some probability. 

When dealing with complex dynamic systems one should be prepared for things to develop in unexpected ways. Since the world predominately consists of complex, dynamic systems it makes sense to work from calculated risks rather than in expectation of a particular outcome. Yet we often demands assurance that no one can realistically give. Politicians cannot tell with certainty if we will be better of if Turkey joins the European Union - but they (and the voters) can estimate the probability. In genetics you cannot completely predict the result of adding or removing parts of a gene - you need to take a calculated risk. And the same holds true for issues such as climate change, the safety of nuclear energy or trends in the stock market. 

Sociologists use the term ”risk society” to describe how modern life demands us to continuously weigh possibilities and risks. One of the reasons for this is that we are offered so many choices in many aspects of our life. Intuitively, many prefer what's well known and safe, but the point is that what is well known and well established may no longer be the safest choice. Innumerous companies have folded because they failed to change when a new and better technology changed the market. In stead the tried to compete by optimizing the technology they were used to (think: Film, LP records, typewriters, tea clippers or horse carts…)

The renowned and thoroughly correct Encyclopedia Britannica is experiencing some tough competition from Wikipedia, which basically is the result of loose cooperation among thousands of amateurs.
Looking up a subject in Britannica one can certain that the information there is correct - or at least is was correct at time the volume went into print.
If you look up the same top in Wikipedia, there's a certain risk that the information could be less than correct. The quality of any particular article is less predictable, and as a user, one should keep this in mind. It should be part of weighing the drawbacks with the advantage. In the case of Wikipedia an article could equally well be more up to date, more extensive and more nuanced than what you would find in Britannica - and not least; Wikipedia is freely available instantly from any PC with an internet connection.
Under rapidly changing conditions these qualities may seem more important than the absolute, hidebound authority.

Connectivity
The more factors that are connected and the faster and tighter the connections among them are, the more complex interactions can take place in the system - and the more complex self organized phenomena that will be likely to generate.
Knowledge and thinking is built up in the brain as billions of neurons connect to each other - and in much the way one might describe the power of the internet as a result of the increasing number of links between the information that the computers on the web contain. 

Ecology describes the flow of resources through nature and the interaction between many types of organisms. The mechanisms in ecology are exactly those found in other complex dynamic systems; evolution, interdependence, positive and negative feedback etc.
Thus complex dynamic system are more akin to what we know from the living systems of biology than to the linear, mechanical paradigm which has been dominating our worldview since the industrial age began. 

The new paradigm of complexity is gradually becoming common sense, for instance there's a growing awareness of humanity's interdependence with nature and of the consequences of the way we interact with the rest of the ecosystem.
Just as the networked society demands us to assume responsibility and act as co-creators, we should start seeing ourselves as co-creators and co-responsible for the condition of our ecosystem.

All in all, The mechanisms described above are the basis of new understanding and a new approach which is crucial to have if we wish to thrive in the global networked society.