Work is learning. Learning is the work.
Complexity
In a series of three posts, Jonathan Weisberg explains the Zollman effect. Here are some highlights.
“More information generally means a better chance at discovering the truth, at least from an individual perspective. But not as a community, Zollman finds, at least not always. Sharing all our information with one another can make us less likely to reach the correct answer to a question we’re all investigating.”
I am following up from thoughts on the cynefin framework and how it has informed my own work since 2007. We are almost at the end of our exploratory looking at ways in which personal knowledge mastery and cynefin may be connected, and I hope this will lead to better ways of sensemaking in uncertainty.
The first concept that I would like to use is — levels of abstraction. Low levels of abstraction mean that information and knowledge are understandable to few people. The lowest level would be me understanding something only to myself. Higher levels of abstraction would make this more understandable to more people, but losing nuance and context in the process. High levels of abstraction are good for things that everyone should understand, such as the symbols and markings on a map.
We live and work in a complex system. Simple, traditional linear models do not work in complex systems.
Campbell’s Law is a real thing – people change their behavior to meet targets. These ‘corruption pressures’ often have unintended consequences.
Unintended consequences are often negative like the Cobra Effect – things are far worse than when you started. —What’s the Pont? 2020
In the book Systems Thinking: Managing chaos and complexity, J. Gharajedaghi provides an example of decision-making by Indigenous people of North America. The Six Nations of the Iroquois Confederacy (Mohawk, Oneida, Onondaga, Cayuga, Seneca, and Tuscarora) had given specific roles to its member tribes, namely Wolves (Pathfinders), Turtles (Problem Formulators), and Bears (Problem Solvers). Solving problems (e.g. governance) went like this:
Could this model be incorporated into our current organizations?
“collaboration means ‘working together’. That’s why you see it in market economies. markets are based on quantity and mass.
cooperation means ‘sharing’. That’s why you see it in networks. In networks, the nature of the connection is important; it is not simply about quantity and mass …
You and I are in a network – but we do not collaborate (we do not align ourselves to the same goal, subscribe to the same vision statement, etc), we *cooperate*” —Stephen Downes
When work requirements are relatively simple, they can be addressed by standardized procedures and best practices. This is the type of work that is getting automated every day. Once a flowchart can describe a process, the algorithms can get to work replacing humans. Complicated work, where systems can be analyzed and understood, can be addressed through industry best-of-breed work practices and can be assisted by enterprise software to ensure people know what is going on.
“The illiterate of the 21st Century are not those who cannot read and write but those who cannot learn, unlearn and relearn.” ―Alvin Toffler,
I read Toffler’s book, Powershift: Knowledge, Wealth, and Power at the Edge of the 21st Century, shortly after it was published in 1990. He saw a shift in power developing due to advances in technology — from force and wealth — to knowledge.
It means that we are creating new networks of knowledge … linking concepts to one another in startling ways … building up amazing hierarchies of inference … spawning new theories, hypotheses, and images, based on novel assumptions, new languages, codes, and logics. Businesses, governments, and individuals are collecting and storing more sheer data than any previous generation in history (creating a massive, confusing gold mine for tomorrow’s historians).
But more important, we are interrelating data in more ways, giving them context, and thus forming them into information; and we are assembling chunks of information into larger and larger models and architectures of knowledge.
None of this implies that the data are correct; information, true; and knowledge, wise. But it does imply vast changes in the way we see the world, create wealth, and exercise power.
Not all this new knowledge is factual or even explicit. Much knowledge, as the term is used here, is unspoken, consisting of assumptions piled atop assumptions, of fragmentary models, of unnoticed analogies, and it includes not simply logical and seemingly unemotional information data, but values, the products of passion and emotion, not to mention imagination and intuition.
It is today’s gigantic upheaval in the knowledge base of society — not computer hype or mere financial manipulation — that explains the rise of a super-symbolic economy.
In early March I wrote how I was making sense of our digital world at the beginning of this pandemic. Some of my practices have held but after six months, some have changed. For example I see information from the WHO and CDC as lagging indicators, and no longer my first stop to find out what is happening now. I understand that they reflect the makeup of their members and funders more so than being a neutral point of view from the medical community.
I am also starting to understand that public health experts and epidemiologists, while both medical professionals, can have widely diverging perspectives on this pandemic. These are not the only knowledge silos dealing with a global problem from their unique and often blinkered perspectives. No single perspective can understand all the complexities.
On 30 September I will be participating in a series of exploratory sessions with Dave Snowden — learning & sense-making in uncertainty and continuous flux and I have discussed some of the concepts previously in sensemaking in uncertainty.
Dave Snowden and Harold Jarche have been exploring different aspects of learning, knowledge management and innovation for decades. This is the first time they are coming together to explore the similarities, differences and potential synergies between their approaches.
As part of our preparation, Dave and I are recording a video for participants to understand each of our frameworks/models/perspectives before we get into deeper conversations and explorations.
First, I would like to recognize my early inspirations for personal knowledge mastery.
When the pandemic broke out, the situation was generally chaotic and the best response was to act firmly, such as establish a lock-down as soon as possible.
“In the chaotic domain, a leader’s immediate job is not to discover patterns but to stanch the bleeding. A leader must first act to establish order, then sense where stability is present and from where it is absent, and then respond by working to transform the situation from chaos to complexity, where the identification of emerging patterns can both help prevent future crises and discern new opportunities. Communication of the most direct top-down or broadcast kind is imperative; there’s simply no time to ask for input.” —Snowden & Boone, HBR 2007
Now the pandemic is in its sixth month. We can make some sense of it, even though much is complex. The best response therefore is to probe — the Cynefin framework calls for those in positions of decision-making to probe, sense, and respond, using safe-to-fail experiments. A reductionist approach will not work in the complex domain.
The TIMN model [Tribes + Institutions + Markets + Networks] developed by David Ronfeldt has influenced much of my own work in looking at how we are moving toward a network society and must create organizational forms that are beyond national governments and beyond markets. Even combining the efforts of civil society, governments, and markets will not be enough to address our greatest challenges — climate change and environmental degradation.
These have been my assumptions to date.
David Ronfeldt and John Arquilla have recently published an update of their original 1999 work on the ‘Noosphere’ — Whose Story Wins: Rise of the Noosphere, Noopolitik, and Information-Age Statecraft.