cynefin and pkm

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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.

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dwindling jobs

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In Only Humans Need Apply, the authors note that one phenomenon of machine automation and augmentation is a decrease in entry-level jobs.

“We seem to have automated away the first few rungs of the traditional career ladder. In automating the routinized work that people used to cut their teeth on, they have also eliminated the means to pick up ‘soft skills’ to be effective with customers and within a large organization … In order to enter step-in jobs at early levels in their careers, students will need to acquire as much knowledge as they possibly can while in school, and as much on-the-job training while in internships.”

Tom Graves, in — Where have all the good jobs gone? — digs deeper into this phenomenon in a recent blog post.

“But as machines and IT-systems take on more and more of the routine rule-based and analytic decisions – the ‘easily repeatable processes’, the ‘automatable’ aspects of business – a key side-effect is, almost by definition, that the skill-levels needed to resolve the ‘non-automatable’ decisions will increase … To put it the other way round, the machines do all of the easy work, and (usually) do it well: but that means that all the hard work is left to the humans.”

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engaging with reality

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Every fortnight, since 2009, I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

@EmilyHaber [German Ambassador to USA] — “Hannah Arendt, a German Jew, political theorist and philosopher, was born on this day in 1906. One of her many legacies: Totalitarianism can flourish where people systematically refuse to engage with reality, and are ready to replace reason with ideology and outright fiction.”

@NeinQuarterly — “Our discontent. It can’t wait til winter.”

@JasonHickel — “Capitalism produces ecological crisis for the same reason it produces inequality: because the fundamental mechanism of capitalist growth is that capital must extract (from nature and from labour) more than it gives in return.”

@DavidOBowles — “I’ll let you in on a secret. I have a doctorate in education, but the field’s basically just a 100 years old. We don’t really know what we’re doing. Our scholarly understanding of how learning happens is like astronomy 2000 years ago.
Most classroom practice is astrology.”

@edmorrison — “Teams are the smallest unit of systems change. And within teams, the smallest unit of change is the conversation.”

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debunking handbook 2020

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The Debunking Handbook 2020 has just been published and is an excellent free guide to address the mass amounts of misinformation, disinformation, and propaganda that flow through our digital communications everyday and then influence real life behaviours. I have discussed some of these phenomena previously, in confronting the post-truth machines and pre-bunking the conspiracy theorists.

The 19-page Handbook provides these handy definitions.

  • Misinformation: False information that is disseminated, regardless of intent to mislead.
  • Disinformation: Misinformation that is deliberately disseminated to mislead.
  • Fake news: False information, often of a sensational nature, that mimics news media content.
  • Continued influence effect: The continued reliance on inaccurate information in people’s memory and reasoning after a credible correction has been presented.
  • Illusory truth effect: Repeated information is more likely to be judged true than novel information because it has become more familiar.

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supporting workplace performance

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Many workplace performance issues cannot be solved through training, such as:

  • Poor communications
  • Unclear expectations (such as policies & guidelines)
  • Inadequate resources
  • Unclear performance measures
  • Rewards and consequences are not directly linked to the desired performance

The barriers above can be addressed without training. Only when there is a genuine lack of skills and knowledge is training required. Even a trained worker, without the right resources and with unclear expectations, may still not perform up to the desired standard. Allison Rossett states that “… performance support is a repository for information, processes, and perspectives that inform and guide planning and action.” There are many cases where performance support is needed to help workers, even if they are trained.

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when training is the wrong solution

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Training is too often the proverbial hammer in search of nails. It’s an easy check mark to show that action has been taken, assuming that improving individual skills is the core issue that needs to be addressed. But training does not improve diversity.

Firms have long relied on diversity training to reduce bias on the job, hiring tests and performance ratings to limit it in recruitment and promotions, and grievance systems to give employees a way to challenge managers … The positive effects of diversity training rarely last beyond a day or two, and a number of studies suggest that it can activate bias or spark a backlash. … That’s why interventions such as targeted college recruitment, mentoring programs, self-managed teams, and task forces have boosted diversity in businesses. Some of the most effective solutions aren’t even designed with diversity in mind. —HBR 2016-07

In another experiment with 10,000 employees of large global corporations, diversity training had little impact where it mattered.

We found very little evidence that diversity training affected the behavior of men or white employees overall—the two groups who typically hold the most power in organizations and are often the primary targets of these interventions. —HBR 2019-07-09

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complexity rules

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  1. We live and work in a complex system. Simple, traditional linear models do not work in complex systems.

  2. Campbell’s Law is a real thing – people change their behavior to meet targets. These ‘corruption pressures’ often have unintended consequences.

  3. Unintended consequences are often negative like the Cobra Effect – things are far worse than when you started. —What’s the Pont? 2020

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hopes and fears

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Every fortnight I curate some of the observations and insights that were shared on social media. I call these Friday’s Finds.

“Hope and fear cannot occupy the same space. Invite one to stay.” —Maya Angelou

@Tom_Peters — “Sunday reminder to leaders. The way you have behaved in the last 6 months and will behave in the next 6 months will define your entire professional career. (David Brooks: ‘resumé virtues’ [‘accomplishments’] versus ‘eulogy virtues’ [what folks say at your funeral])”

@DebraWatkinson — “If we walked into a store with a lit cigarette — wait no one does that nowadays, but we once could and did. This anti-mask thing is so much more than political. It is anti-society.”

@CriticalLearner — “At some point, I have to remember my grandfather’s advice — you can’t say the right thing to the wrong person — at which point it is best to move on.”

@CarvingThought — “‘Research’ is often disguised procrastination. Iteration is the key to rapid learning.”

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decision-making and trustworthiness

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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:

  1. Wolves — Set direction, and identify relevant issues
  2. Turtles — Define the problems
  3. Bears — Generate alternatives and recommend solutions
  4. Turtles — Check on the potency of the recommended solutions
  5. Wolves — Integrate the solutions, keep the records, communicate the decisions

Could this model be incorporated into our current organizations?

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revisiting cooperation

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“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.

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