Work is learning. Learning is the work.
In my last post on continuous learning for collaboration, I mentioned that one of the primary reasons to promote learning at work is because it is directly linked to innovation. Gary Klein examined 120 case studies and in, Seeing What Others Don’t, identified five ways that we gain insight.
For the eleventh consecutive year, Jane Hart has polled thousands of respondents and asked what are their Top Tools for Learning. I contributed my own list of tools once again this year. In addition to the extensive list, complete with Jane’s observations and insights, she provides an interesting look at ten of the emerging trends. I find two of the trends of significant interest.
One of the primary reasons to promote learning at work is because it is directly linked to innovation. Gary Klein examined 120 case studies and in, Seeing what Others Don’t, identified five ways that we gain insight.
Last year I wrote a post — cities & the future of work — as an introduction to my session with the Prime Minister’s Office of Finland. I have been invited back to Helsinki this year to further discuss some issues around reforming the government’s operating practices particularly moving toward a more collaborative culture.
In the emerging network era, leadership is helping communities and networks become more resilient. Government agencies can focus on creating more human organizational structures that enable self-governance. Leadership becomes an emergent property of a network in balance. Depending on any one person to be the leader only dumbs-down the entire network. Viewing all of our work and learning from a network perspective may in the long-run create a better society. One role of government in the network era is to enable knowledge-sharing and curate the knowledge of all citizens. It can start by doing this internally. Countries, regions, and cities should be designed to enable more and better connections between citizens. Learning and innovation are more about making connections than having unique ideas. Increasing connections makes for a more innovative country.
In Finland the government is looking at a cross-sectoral and phenomenon-based approach, which ensures that a phenomenon like youth social exclusion is understood and addressed by government departments together, before individual budgets and projects are initiated. I liken this to agile sensemaking, where these ‘situation rooms’ (work teams) are based on temporary, negotiated hierarchies, that can be re-organized to address different phenomena as they appear.
Every fortnight I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds
“I’m convinced that people think freelancers have their lives funded by some kind of freelancing fairy, and that invoices are therefore an exercise in comedy.” —WhiteOwl
“AI makes us more powerful. It doesn’t make us wiser.” —@joi
“The impact of technology on our lives — and on the future of meaningful work — is the result of research, investment, regulatory, and business model choices that are made by people.” —Byrone Auguste
The Seek > Sense > Share framework of personal knowledge mastery was the result of many iterations over almost 15 years. It is simple to understand but under it are many layers. You can keep digging for a lifetime as each part reveals deeper aspects — algorithms, heuristics, complexity, critical thinking, media literacy, cognitive load management, network analysis, etc.
Many people have shared their PKM routines and it is great to see variations on the theme, as there is no lock-step method or recipe to mastery. We must each find our own path as we likely will not keep to another person’s path over time. PKM is personal.
Clark Quinn has taken my Seek > Sense > Share framework and added a layer that makes it easier to understand. It is not too detailed but gives extra value. Clark has ‘added value‘, a key part of PKM.
A major challenge I have had in my organizational change work is getting people to understand that complicated environments are different from complex ones, and the latter are almost always the situation when people are involved. Generally it means that in complex situations there is less reliance on pre-planning and analysis and a greater emphasis on continuous experimentation coupled with good observation and tracking. Reinforce successful projects and learn constantly in complexity.
According to the Cynefin framework we should Probe > Sense > Respond when dealing with complexity, as opposed to Analyze > Sense > Respond when the situation is complicated. Mechanical systems are complicated, but most human systems are complex. It means that we cannot overplan, though planning itself prepares us to deal with what emerges as we probe complex situations and environments. In complicated conditions we can rely on established good practices, but in complex ones we need to continuously develop our own emergent practices.
This is my 200th post on the topic of complexity. But I have not paid much attention to chaos. In Chaos: A User’s Guide, Bruno Marion concludes that the world today is chaotic.
“Never in the history of humanity has a single human being had so much power. Never in the history of humanity have YOU had so much power!
Optimistic or pessimistic, it is like being a spectator of a film of which we seem to know the ending, whether happy or unhappy. Today one must cease to be a passive spectator but an actor in this fast-changing world.”
If we seek diverse or divergent views, will the opinions of others change our minds? A recent study seems to indicate that paying attention to views opposed to our own may actually harden our existing perspectives.
“In a study that was published last month in the journal Proceedings of the National Academy of Sciences, my colleagues and I [Christopher A. Bail, Duke University] did just that. We surveyed more than 1,200 Twitter-using Republicans and Democrats about their political views. Then we paid half of them to follow for one month a bot we created that retweeted messages from elected officials and other opinion leaders from the other political party.
Instead of reducing political polarization, being exposed to opposing ideas increased it. Republicans who followed a Democratic bot for one month expressed social policy views that were substantially more conservative at the conclusion of the study. Democrats who followed a Republican bot exhibited very slight increases in liberal attitudes about social issues, but those effects were not statistically significant.” —New York Times 2018-09-08
“Research shows that teams will organize themselves in different ways in response to how different types of complexity strains their sensemaking capacities. In order to increase their sensemaking potential, teams will reorganize their relationships in recognizable ways. We can think of these as emergent patterns of collective sensemaking.” —Bonnitta Roy
The increasing complexity of work is a result of automation, such as AI & robots, who are taking away any repetitive tasks, leaving barely repeatable tasks for humans. In addition to this automation of any work that can be described in a flowchart, we also have a larger number of human connections to deal with and humans by nature are complex. Robin Dunbar showed that we are only able to have a maximum of about 150 real human relationships before our cognitive capabilities are maxed out. Note that 150 is the size of an infantry company, a standard size that has stood the test of battle and time. But I, and many others, have thousands of connections on social media platforms like LinkedIn. How can we make sense of these?
Automation plus the current version of corporate capitalism is creating the perfect storm for those of us commonly known as labour. Most companies and labour laws are structured around an industrial model of capital and labour. The innovation that will save human work will be new business and operating models. Common wisdom is that we need to divide the owners of financial capital from the creators of knowledge capital. Such artificial hierarchies are not needed, though many say that hierarchies exist in nature and therefore are a part of the human condition as well. At least one piece of recent research shows that this is wrong. Early herders produced significant communal works without hierarchies.
“Work by a team of US-based experts on a remote site near Lake Turkana in Kenya contradicts longstanding beliefs about the origins of the first civilisations. It suggests that early communities did not inevitably develop powerful elites or compete violently for scarce resources, but may have worked together to overcome challenges instead.”
“Researchers studying the early history of agricultural societies believe large groups of people built permanent monuments to reinforce identities based on a sense of shared history, ideals and culture.”
“When agrarian societies started to develop, hierarchies started to develop too. Some people became more powerful and disparities in wealth and health and social circumstances emerged. So the big question is: Did the same thing happen in pastoral societies?” said Hildebrand.
“Lothagam North pillar site is the earliest known monumental site in eastern Africa … built by the region’s first herders … and gives us solid evidence that these pastoralists did indeed follow a different trajectory of social change. People came together in large numbers, probably expending blood, sweat and tears to build these large structures, but we have no evidence for hierarchy or social difference.” —The Guardian 2018-08-20
Every fortnight I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.
“People demand freedom of speech as a compensation for the freedom of thought which they seldom use.” —Søren Kierkegaard, via @EskoKilpi
Howard Rheingold: What Technophiles Need To Know: Part One
“Although our present crisis is so threatening precisely because it plays out on the physical plane, where our bodies and other creatures live, it is a crisis of knowledge. We lack a crucial mental skill. I contend that our position today regarding the way we make decisions about technologies is similar to the dilemma that pre-Enlightenment scientists faced in the sixteenth century. We simply don’t have a good method for thinking and making decisions about how to apply (and not apply) the powerful tools of rationality, the scientific method, reductionism, the combination of logic and efficiency embodied by technology.”
Roger Schank: To know AI is to understand it
All the talk about AI these days relates in no way to self refection, to knowing what you need to know, or to anticipating the future. We talk about “AI” but we are not talking about the “I”. We have intelligent entities already. (They are called humans.) When they are confused they ask for explanations. When today’s so-called “AI’s” start doing that, please let me know. In the meantime, it would be nice if there weren’t an article a day in major publications about AI when what they mean is number crunching and pattern matching, not wondering and trying to find out.