authority plus reputation

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According to Daniel Mezick, there are two systems in an organization — formal and informal. Leadership is exercised through authority in the formal system, and through reputation in the informal one. Combined, Mezick calls this simply, ‘hierarchy’, which he says is neither good nor bad. It just is.

I would agree that both exist. We are getting more interested in the informal system and the value of reputation as society and our organizations become more networked. In a networked world, reputation is gaining power. In organizations with increasingly shorter lifespans, formal authority is temporary while reputation can extend beyond the life of an organization.

Rita McGrath says that, “Hierarchy [authority] creates the illusion of control”.

“… but you want to avoid the usual trappings of hierarchy—top-down decision making, approvals, committees. These things create the illusion of control, but in a world that’s changing rapidly, they ironically don’t provide you with actual control. The more you rely on top-down decision making, the less you get that input from the edges that’s so critical to the ability to respond to rapidly changing external environments.”

People in positions of organizational authority may have temporary power but it is their reputation that will help them find their next engagement. There are few of us working today who will not have a ‘next engagement’. Helping make our networks smarter, more resilient, and able to make better decisions will enhance our reputation in our human networks. It will also make for more human organizations.

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extracting human value

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Automation + Capitalism makes for a perfect storm that many of us will not weather. Does ‘Artificial Intelligence’, the current top buzzword, really mean that we program our biases into automated decision-making systems, seal them in a proprietary black box, and let the status quo reign, with no illusion of ethics, morals, or humanity? Maintaining this status quo is the core operating model of global management consultancies like McKinsey.

“We are now living with the consequences of the world McKinsey created. Market fundamentalism is the default mode for businesses and governments the world over. Abstraction and myth insulate actors from the atrocities they help perpetuate. Businesses that resisted the pressure to rationalize every decision based on its impact on shareholder value were beaten out or eaten up by those who shed the last remnants of their humanity. With another heavyweight on the side of management, McKinsey tipped the scale even further away from labor, contributing directly to the increase in wealth inequality plaguing the world. Governments are now more similar to the private sector and more reliant on their services. The “best and the brightest” devote themselves to client service instead of public service. —Current Affairs 2019-02-05

It is reinforced by an expressed attitude that human work is something that can be broken into components and used like bits of machinery. People are merely the sum of the work that can be extracted from them by the capitalist machine. They have no other value in this economic system, and hence are always viewed as expenses.

“We see a world beyond employment and, arguably, the fundamental underpinnings for a world where work is constantly reinvented. Work is deconstructed into tasks, dispersed in time and space, and executed through many virtual and market relationships other than traditional employment. The organization is permeable, interconnected and collaborative and can change in shape. The reward is impermanent, individually defined and uses imaginative elements such as game points, reputation, mission.” —WillisTowerWatson: Future of Work

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learning myths & superstitions

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In Millennials, Goldfish & other Training Misconceptions my colleague Clark Quinn has written a handy guide for every training shop or L&D department. Using his decades of experience combined with a scientist’s analytical mind, Clark first looks at learning ‘myths’ — beliefs we hold that aren’t true. Each myth is analyzed from seven perspectives:

  1. The Claim
  2. The Appeal
  3. The Potential Upside
  4. The Potential Downside
  5. How to Evaluate
  6. What the Evidence Says
  7. What to Do

This book is a useful job aid for anyone supporting learning in the workplace. Clark uses a different approach for ‘superstitions’ — AKA bad practices. He examines each of these from five perspectives:

  1. The Claim
  2. The Practice
  3. The Rationale
  4. Why it Doesn’t Work
  5. What to do Instead

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education does not destroy creativity

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There is a certain irony that the most popular TED Talk — Do schools kill creativity? — is seriously questioned in a TEDx talk over a decade later. Ken Robinson’s talk on creativity has had over 55 million views. Basically he says that our schools suck the creativity out of children.

“Picasso once said this, he said that all children are born artists. The problem is to remain an artist as we grow up. I believe this passionately, that we don’t grow into creativity, we grow out of it. Or rather, we get educated out of it.” —Ken Robinson

With only 2,000 views to date, Elisabeth McClure, a researcher with the LEGO foundation, presents a case that counters Robinson’s views on creativity — Are children really more creative than adults? McClure starts by stating there is no evidence that the cited Land & Jarman study on creativity was ever published and may never have happened. NASA has no record of it.

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“normal is the bias”

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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.

“The secret of the demagogue is to appear as dumb as his audience so that these people can believe themselves as smart as he is.” —Karl Kraus, via @TrutherBotPop

We analyzed 16,625 papers to figure out where AI is headed next

“Every decade, in other words, has essentially seen the reign of a different technique: neural networks in the late ’50s and ’60s, various symbolic approaches in the ’70s, knowledge-based systems in the ’80s, Bayesian networks in the ’90s, support vector machines in the ’00s, and neural networks again in the ’10s.

The 2020s should be no different, says [Prof. Pedro] Domingos, meaning the era of deep learning may soon come to an end. But characteristically, the research community has competing ideas about what will come next—whether an older technique will regain favor or whether the field will create an entirely new paradigm.”

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“taking responsibility for our own work and learning”

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“To a great extent PKM [personal knowledge management] is about shifting responsibility for learning and knowledge sharing from a company to individuals and this is the greatest challenge for both sides. Companies should recognise that their employees are not ‘human resources’, but investors who bring their expertise into a company. As any investors they want to participate in decision-making and can easily withdraw if their ‘return on investment’ is not compelling. Creativity, learning or desire to help others cannot be controlled, so knowledge workers need to be intrinsically motivated to deliver quality results. In this case ‘command and control’ management methods are not likely to work.

Taking responsibility for own work and learning is a challenge for knowledge workers as well. Taking these responsibilities requires attitude shift and initiative, as well as developing personal KM knowledge and skills. In a sense personal KM is very entrepreneurial, there are more rewards and more risks in taking responsibility for developing own expertise.” —Lilia Efimova (2004)

Lilia’s writing about personal knowledge management was my inspiration to create a framework for sensemaking in this digitally networked world. I was looking for a way to connect and build my knowledge networks. The personal knowledge mastery concept led me to test out and develop ways to inform my own practice. I saw my blog as a platform to make implicit knowledge (e.g. not codified or structured) more explicit, through the process of regularly writing out my thoughts and observations.

Lilia’s 2010 post on teams, communities, and networks inspired my many versions of the perpetual beta model.

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losing by winning

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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.

@csessums — ‘Metrics are great; however, don’t forget the law(s) – Goodhart’s Law: “When a measure becomes a target, it ceases to be a good measure,” & Campbell’s Law: The more a metric is used, the more likely it is to “corrupt the process it is intended to monitor.”’

The death of consensus, not the death of truth via @willrich45

“We are living in a time of tremendous social change and contention. Within the West, power is being negotiated around issues of climate change, migration, race and ethnicity, and gender and gender identity, amongst many other issues. At a geopolitical level, traditional alliances are beginning to realign and change in unexpected ways, and we should expect the EU and China’s visions of the internet in particular to have a stronger hold on global discourse about internet governance. It’s not enough to adapt for a digital environment; we have to understand the politics and societal dynamics behind these changes …

The question I leave for you today is this: What are the new institutions of journalism, and how are they adapting for the actual dynamics of the networked world, where communities of affiliation are not simply separating into echo chambers but actively acting in contention with each other? How will we in journalism operate in an environment of dissensus? What can we do to shape our media environments of today?”

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filter failure is a human failure

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There was an explosion on social media over an incident between school boys, on an official school trip to demonstrate in Washington DC, shown in a video vocally berating a Native American elder. Here is one of the latest articles about it, showing additional video — don’t doubt what you saw with your eyes. Mainstream media, like our own Canadian Broadcasting Corporation, are trying to establish a very difficult-to-find middle ground. I have opinions on what I have seen and read but I am not ready to share these in public. I am talking about them in private with some trusted friends and colleagues. I will share if and and when it is appropriate.

“It’s not information overload, it’s filter failure”, wrote Clay Shirky a decade ago. As the online space of social media gets more polluted and manipulated by trolls, bots, and hidden agendas, then filters become critical. Sensemaking cannot be done alone. Every thinking person has to find ways to understand issues of importance. If professional journalists can be co-opted by bots, what about the rest of us?

“Using bots to seed a divisive meme is akin to lightly blowing on an ember to start a fire. In a healthy society, that ember quickly burns out, starved for fuel. In a society in transition, the landscape is littered with desiccated institutions and ideas, ready to ignite.” —John Robb

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learning in complexity and chaos

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Most of our current work structures are designed to address complicated situations, such as constructing a building, launching a campaign, or designing a piece of equipment. But more of our challenges are complex and cannot be solved in a standard way —  inequality, refugees, populism, racism. Whenever people are involved, within a global context of climate change, the situation is likely complex. In complex situations there is less reliance on detailed plans and analysis and a greater emphasis on continuous experimentation coupled with good observation and tracking. We have to learn constantly in complexity.

Complexity & Chaos

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 human systems are complex. It means that we cannot over-plan, 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.

In Chaos: A User’s Guide, Bruno Marion concludes that the world today is not just complex, but even 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.”

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gaining insight at work

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With increasing complexity in most aspects of a network society, the way that we support organizational learning must change. With low levels of complexity, knowledge can be codified into documentation and distributed throughout the organization. Best practices can be determined and then people can be trained to perform these methods at work. Basic aircraft flight operations can be taught in this way. But complex problems require implicit knowledge that cannot be put into a manual. This type of knowledge is nuanced and dependent on the context and situation. For example, negotiating the creation of the United Nations required many conversations and involved a myriad of social connections. It required social learning, which is how we gain insights, by connecting with others and learning while we work.

Social learning is the process by which groups of people cooperate to learn with and from each other. The network era is creating a historic reversal of education, as discourse replaces institutions, and social learning in knowledge networks obsolesces many aspects of organizational training. It is as if Socrates has come back to put Plato’s academy in its place, but this time the public agora is global.

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