Guide to the Future

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Goo-Rue Guide to the Future

WayFinding in a world of Evolving Strange Attractors

Narratives of Causation, Structures of Reasoning

You can’t connect the dots looking forward. You can only connect them looking backwards, so you have to trust that the dots will somehow connect in your future.” ….Steve Jobs

History is complex – we search to understand not just ‘what’ happened but how and why stuff happened. Each account of history has to prioritize a way to organize the material in order to shape a narrative that will offer us a way to reason about our past.


Trying to understand how the future can unfold – is even more complex. Questions arise that go beyond how change happens or even why change happens. We not only can’t know what WILL happen – we can’t know what CAN happen – because, as conditions change so does the range of possible choices also change.

In conditions of accelerating change and change in the conditions of change (remember the emerging conditions of phase transitions?) the possibilities for choosing not only change but proliferate beyond our capacity for speculation. It not just what we do – but what we don’t do. In many cases we don’t even know what is ‘wantable’.

Largely, we have to engage ourselves in the unfolding future by developing a literacy of the possible. A literacy that can read between the lines of innumerable trajectories – between current and possible narratives that shape choice architectures. We must do this in order to proactively entangle our most positive intentions with emerging conditions.

For almost all of human history – no more than 10-15% of humans lived in situations of more than 2000 people. Individual lives were tremendously unstable (who knew what sickness, drought, event would bring their death) – but communities were much more stable (culture could endure the turbulence of change). One’s life remained similar to one’s parents, grandparent and ancestors. In the modern condition, individual lives are often more stable (lower infant mortality, access to social institutional assistance, improving medical knowledge and treatments, etc.) – but our communities are more fluid, turbulent, and unstable (changing economic priorities, increasing mobility, technological transformations, etc.).

For most of human history people were born, lived and died within a radius of 30 to 50 miles. This meant that people’s identity, community and culture were intensely local (this is also a pun – that when humans became urban which was an intensive condition of change – the experience of ‘local’ became increasingly more cosmopolitan). The transition from the hunter-gatherer-agricultural society (traditional society) to industrial and post-industrial society introduced multiple languages, cultural beliefs/values, and other conditions. As Marshall McLuhan noted the industrial society introduced the fact that all cultures were now multinational and all nations were now multicultural.


This means that our environment through which we must ‘wayfind’ includes an increasing quantity and quality of narratives. There is a paradox that emerges with the ‘modern’ condition of increasing population size and density, with the corresponding shift to increasing portions of our populations living in urban versus rural conditions. A paradox associated with the introduction of technologies of mass broadcast communication, the printing press then later radio and television.

This paradox involves the emergence of a richer more complex sense of individuality in a condition of that simultaneously creates a ‘mass culture’.

With the development of the Industrial world ‘nations’ emerged that were not dependent on the concept of ‘empire’ (although empires continue). To create the ‘Imagined Community’ (a nation as a socially constructed community, imagined by the people who perceive themselves as part of that group), requires a certain technological capacity for ‘mass communication’ and mass monitoring of a population that has reached a certain level size and density. In a way Jean Jacque Rousseau’s notion that it was the ‘general will of the people that is the sovereign’ recognized this shift.

In this way we see that equal to and perhaps simultaneous with the introduction of ‘mass communication’ is the development and stewarding of a common narrative. The industrial society melded the narrative medias of religion and science.

In “Mind and Nature: A necessary unity” Gregory Bateson noted that – Science probes; it does not prove. Although, we often assert that some latest piece of research has proved some theory or hypothesis. To definitively prove something, we would have to verify all possible instances of a claim – for example to prove all swans were white one would have to show that all swans that have existed, currently exist and will exist are in fact white. This is impossible.

What science does do, however, is to assemble honest evidence that supports an honest claim that provides a more ‘reasonable’ explanation than any other and/or makes better, more precise predictions.

"even a fully deterministic system can be essentially unpredictable due to sensitivity to initial conditions" “even a fully deterministic system can be essentially unpredictable due to sensitivity to initial conditions”

How we frame our observations, shapes the way we attribute causation, and therefore structures how we reason to form theories and explanations. Most of the time we observe a correlation—first X happens and then Y happens, and we want to explain why. We try to fit our observations of the sequences of events into patterns (stories) that make sense of what we think is unfolding. The metaphors and frames we use to make sense of our observation also have an entailing logic that in turn provides a narrative structure. Science has become a dominant narrative structure.

Humans are driven to generate explanations for why things happened the way they did. That’s not to say that our explanations are unimportant – as Kurt Lewin says: “There is nothing more practical than a good theory”. Theories help us to determine what questions to ask. Theories are the way scientists create stories that link causes and effects into coherent wholes or systems. Understanding whole system is vital because the ‘causes’ derived as systemic properties are indirect and often very hard to track. The difficulty of understanding systemic causation is evident in the difficulty of people to grasp climate change. What a good story-as-theory can do is enable our mind to grasp complex causalities, in a way that is more like direct causation.

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Goo-Rue Guide to the Future

WayFinding in a world of Evolving Strange Attractors

Intensive Properties and Change

A key to being able to identify the difference between exponential change and situations that could lead to a phase transition is understanding the nature of what is changing.

Most people think of measuring reality in terms of adding or subtracting ‘stuff’. For example, length, width, depth, volume, weight, etc. vary by changing the amount of matter - having more of or less of what is being measured. These types of measures are called extensive properties.

The conditions that enable phase transitions depend on what is called intensive properties. These are measurable domains such as temperature, pressure, density, connectivity, conductivity, viscosity and malleability.

                 Goo_Rue_V1-I3_Ven.jpg - 44.69 kB

 

These measurable properties are not primarily dependent on the amount of matter – but on the conditions of populations. For example, one can’t measure the ‘temperature’ of a single molecule because temperature is a measure of the ‘activity’ of a mass of molecules. In this way if you have a liter of water and divide it in half you will have two ½ liters of water. But if the liter of water is 90 degrees Celsius and you divide the liter in two. You will have two ½ liters but each ½ liter will be 90 degrees Celsius not two ½ liters that are each 45 degrees Celsius.

Phenomena measurable by intensive properties, are subject to the particular type of change we have been discussing - Phase Transition. A phase transition is a very dramatic type of change within a very narrow band of measurement. Such as when water turns to ice.


For example, a body of water (a large population of water molecules) may be measured to have a temperature of 90 degrees Celsius (a level of activity in the population). We can track the incremental decreases in temperature – from 90 degrees, to 89 degree all the way to 1 degree Celsius. While the water is noticeably colder – the nature of water, its wetness, fluidity, etc. has not changed. One more incremental decrease to just above 0 degrees no fundamental change.

However, once the temperature decreases one more increment to -1 degree – the water changes in a fundamental way. It is now solid – no swimming is possible. Two completely different ‘substances’ or ‘conditions’ are evident on each side of 0.

 Goo_Rue_V1-I3_Phase.png - 78.96 kB

In the image above we see the thresholds for phase transitions of water.

Phase transitions are very difficult to anticipate or predict unless we have already experienced it. Trend analysis does not prepare the observer for this type of change.


Why are intensive properties and phase transitions important to understanding social and technological change? Among many properties describing human societies, population density and connectivity represent key intensive properties. As human populations experience increases in density we see critical phase transitions where divisions of labour can proliferate exponentially. What does a phase transition look like in a social context?

For example, when humans were hunter-gatherers local groups generally never exceeded a population of 150-250 – a density that can only sustain very rudimentary divisions of labour (e.g. elder, adult, child, male-female, hunter-gatherer, shaman-healer). As humans became agricultural societies – local groups were able to increase population densities (by exponential amounts in some cases). This enabled a phase transition where many more permanent divisions of labor, and whole new occupations arose, each occupation also becoming a domain of specialized knowledge, as well as new ways for a person to ‘be’ and establish identity in society (e.g. a shoe-maker, tailor, baker, herbalist, etc.).


New institutions become necessary as well. The agricultural society was more than a large gathering of hunter gatherers who could farm, it required a new institutional framework with many new institutions, and conventions. Along with increases in population density and new division of specialized labor comes the need to increase the level and types of exchange.

The rise of civilization demonstrated a phase transition enabling and enabled by large city-states. A similar phase transition occurred in the course of the emergence of the industrial society – exponential rise in population density, more levels of specialization, more exchange – whole new institutions (e.g. enablers for the governance of market and democratic political economies, public education, impartial justice, etc.).

Other types of ‘intensity’ can change the conditions of a social context, even those, with a relatively stable population density. For example, the emergence of new communication technologies that enable simulations of increased density (through the sense of collapsing distance) and/or increased connectedness. Understanding the impact of increased population, connectiveness and communication ‘densities’ can help to imagine the potential in the future of the emerging digital environment. Social media has been described as an exponential increase in ‘density’ of communication and connectedness.

The digital environment fundamentally disrupts the industrial economy, its institutions and its organizations. For example, by enabling conditions that inevitably favor hyper-connectivity which inevitably leads to a hyper-division-of-labour (or hyper-specialization). This in turn entails a requisite hyper-exchange. Together hyper specialization & exchange produce a hyper-knowledge-metabolism.

I want to paraphrase Marshall McLuhan to create a McLuhanism relevant to the emerging digital environment:

If Social Media is the Medium,

Then Social Computing is the Message.

This entails that Organizations cannot be Architected as ‘manufacturing machines’.

They must now be Architected to be Programmable, Complex Adaptive Systems. 

​For McLuhan, a Medium was anything that extended the mind, body or senses. By this definition a Medium could be a new technology, a process, an idea or an original creative work.

The message of a Medium was not its contents. ‘The Message’ is found in differences arising as a result of changes of scale, pace, scope or pattern caused by a ‘new medium’. These are difference that we observe in changes of behavior, aspiration, relationships, structures, processes, possibilities of action, etc. in individuals, society or culture. The message only becomes clear in the resulting in changes human interactions and activities. The message may include a social-cultural phase transition.

It is not the information conveyed through the Medium but rather it is the Medium’s ability to effect change that is the key factor that enables us to realize and understand a Medium.

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Goo-Rue Guide to the Future

WayFinding in a world of Evolving Strange Attractors

Attractors, Boundary Conditions and Change

In this post I want to explore more deeply the concept of boundary conditions that shape attractors and include intensive properties. An interesting example of boundary conditions relevant to economic thinking arises from the work of Ronald Coase. Coase won a Noble in economics for 1937 paper “The Nature of the Firm”. The central question Coase was exploring was "Why and under what conditions should we expect firms to emerge?" This question aimed to explain an apparent contradiction of the market economy which is lauded to be the most efficient way to get things done.


The modern firms emerge due to the needs of an entrepreneur who begins to hire people, Coase's analyzed the conditions that the entrepreneur would find it less expensive to hire people than contracting out particular tasks. Why is it less costly and more efficient to have people gathered under one ‘umbrella’ to get things done”. Coase’s answer was that despite the alleged efficiency of the market economy, firms provided a competitive advantage because they reduced transaction costs. 

These costs involve material (and related issues), effort, attention, energy, time (conveniences) and information flows embedded in the production systems (including search, negotiating terms, coordination, enforcement and communication). By sharing purpose; dividing labor; and providing control through establishing roles, responsibilities and methods of communication – it is both less expensive and easier to get things done.

By reducing these costs (boundary conditions) it made the hierarchic organizational structure efficient. For example, it is costly for everyone (in time, effort, money and resources) to search for employees or work opportunities, to constantly negotiate enforceable terms and compensation, and to coordinate collective effort toward common purposes (principal-agent problem). Longer-term contracts, divisions of labor, and other structures of the organization enabled significant savings in costs as well as higher productivity.

But it is not only efficiency, according to Douglas Allen, there is another important dimension of transaction costs. In Allen’s reading of Coase, transaction costs are the costs (time, effort, money, opportunity) necessary to establish and maintain any system of rules and rights, which in turn defines what institutions are – systems of rules. The implication of this is that transaction costs also include the costs of mitigating bad behaviors. According to Allen, institutions emerge to maximize wealth and minimize the costs of establishing and maintaining themselves.

The effective upper limit on the size which a traditional organization can reach and continue to be efficient is the threshold where the internalization of transaction costs begins to exceed the transaction costs of a market situation. As the organization gets bigger there will be diminishing returns to efforts to create more efficient management regimes.


Coase also noted other ‘human’ reasons for a hierarchical organization in a market system, such as:

  • Some people prefer to work under direction (to follow) and are prepared to accept the corresponding conditions & restrictions;
  • Some people prefer to direct others (to lead or manage) and are willing to accept the responsibilities and costs related to this role; and
  • Some people prefer goods produced by firms.

Throughout human civilization (after humans had become agriculturalist and before the advent of the digital environment), there have been environmental and other constraints that have produced various forms of transaction costs.  These transaction costs determined boundary conditions shaping hierarchy as the attractor of efficient organizational structures – an attractor of efficiency and governance.

We know that for most of human history we have lived in small group of hunter-gatherer what have always been relatively egalitarian – and thus the tendency to hierarchic structure is much less a result of human traits and much more the result of the boundary conditions of transactional constraints shaping an attractor of organizational efficiency.

Among the numerous changes in the conditions of change that the evolving digital environment is enabling, is the fundamental collapse of traditional transaction costs and the emergence of new platforms of productivity and ways to get things done. We are in the midst of transitioning a fundamental threshold enabling new types and varieties of exchange through an exponential decrease in transaction costs – including those related to patterns and rates of interactive exchange, as well as those associated with search, negotiation, enforcement, coordination and communication. In this way, different types of intensity thresholds (density, connectedness, etc.) are instigating a massive societal phase transition.

I believe that the digital environment is a change in the conditions of change which will entail new constraints. As these new constraints become more established and widespread (e.g. approaching near zero marginal costs) they will determine a different attractor of organizational efficiency, which I will begin to elaborate in later posts. Others refer to this change in the conditions of change with different terms. For example, Jeremy Rifkin in his book “Zero Marginal Cost Society” refers to the emerging ‘collaborative commons’ as the inevitable form of organizational efficiency and attractor of governance.

I will elaborate these concept in greater detail as I progress in later posts.

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Goo-Rue Guide to the Future

WayFinding in a world of Evolving Strange Attractors

Change in Conditions of Change

In my introduction I spoke about change in conditions of change. In this post I want to explore this further. It’s old news that we are living in times of accelerating change. Most people accept this as fact. What is more challenging than simply grasping the speed of change – is understanding how many ways things are changing.

Most of us tend to suffer what Ray Kurzweil calls the ‘intuitive linear view’ of how the situation today will transform into the future. We tend to extrapolate the rate change in the last few years to try to imagine tomorrow. For example, if it took 12 years for the market for smart phones to reach 3.3 billion people (35% of the world’s population) then we naturally extrapolate that to reach 70% of the world’s population will take another 12 years.

However, many times change is exponential – we already know this to be the case in relation to computers. Almost everyone is familiar with Moore’s Law which describes the computational advances since the invention of the integrated computer chip. Essentially our computers have doubled in power every 18 months. A great number of technologies have exhibited the same exponential and even super-exponential increase in performance and capabilities.

An illustrative quote from Kurzweil’s “The Law of Accelerating Returns” provides fodder for our imagination. In his statement he takes into account the exponential change demonstrated by a great deal of progress in science and technology:

An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense “intuitive linear” view. So we won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress (at today’s rate).


Not only are we all vulnerable to an intuitively linear imagination of the unfolding future, but we also tend to imagine future technology overlaid on existing social situations such as current organizational and institutional frameworks. For example, in the face of innovations like Bitcoin, we will easily imagine that we will still go shopping at the mall only we’ll use crypto-currency instead of our credit & debit cards. It’s hard to imagine the disappearance or transformation of the mall from shopping destinations to new forms of urban village as a result of other innovations such as the shift to shopping online for our goods (often personally customized) which will be delivered via self-driving/autonomous forms of transportation.

A core challenge of thinking about the future is that everything is interdependent (if not downright entangled). This means everything influences the way everything else changes. This is what complexity means – like ecologies, living systems even political economies. Complexity makes control and prediction impossible (if it were possible the market and the weather would never surprise us).

Complexity also makes it difficult to know where to begin in describing and thinking about how not only the future can unfold but how any complex system actually changes as it operates. As I noted in the first post, it’s as if we are finding our way in a changing landscape and each step changes the environmental conditions of the next step.

As McLuhan noted, people tend to move toward the future while looking in the rear-view mirror. As difficult as it is to predict the progress of future technologies what is much more difficult to comprehend, is how we will experience such changes. What will society, the economy, and our institutions look like in 20 years (or more)? It is very hard for people to ‘think exponentially’. This challenge to thinking contributes to the difficulty of understanding change in complex systems – such as climate change.

There are many methods for future gazing. Most have evolved with a scientific understanding of the limits of reductive and positivist approaches. From initial efforts to forecast and predict the future towards current approaches that seek to reveal our assumptions and open our thinking.


The definition for foresight that I’ve developed from my own work and research is:

Foresight is not about predicting what will happen.

Foresight is about understanding evolving conditions in order to imagine what they can enable.

Foresight needs Rigor and Imagination. ​

Essentially, we have to imagine - what affordances, what unseen possibilities, what new combinations of what is, what novel inventions - people can discover within the currents of today’s situation and what people can creatively do as a consequence. To quote Stuart Kauffman (I will discuss his work in later posts), the key constraint for foresight is that:

We not only don’t know what Will happen – we don’t know what CAN happen 

In simple language, we use foresight to understand trajectories of change and the conditions they create – so we can imagine what can be enabled within these conditions.

There is lots of debate about revolution versus evolution. I believe framing change in this way easily misleads us into a non-productive either-or frame. What is important is to understand that change occurs simultaneously through many different processes and on many different time scales.

The next post will continue exploring a type of change that looks like exponential change and certainly is a change in conditions of change but which is better described as Phase Transitions - sometimes more becomes different. I touched on this in the previous introductory post.

In later posts I will continue to explore other types of change and distributions of differences. I will also outline other key domains that indicate unprecedented areas of change.

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