Harold Jarche

leadership through cooperation

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One of the few areas where most nations cooperate is in infectious disease control. For nation states, cooperation is the best option in this type of ‘prisoner’s dilemma‘.

“On 31 December 2019, the World Health Organization was alerted about a cluster of pneumonia cases of unknown etiology in Wuhan, China, which prompted international concern of the potential public-health impact of an outbreak of a new virus [COVID-19] … With the now global spread of the virus, the urgency of a coordinated international response has amplified … This multi-pronged approach to curtail the outbreak, strongly supported by existing R&D, is a testament to the collaborative response of international organizations and the research and clinical communities … The initial global response to the 2019-nCoV outbreak illustrates the power of rapid communication and the importance of sustained research and collaborations that can be leveraged in future outbreaks. Sustained cooperation is essential to their resolution.” —Nature 2020-02-03

In Canada, special funds of up to $1 million per project have been allocated for rapid research into the recent outbreak of the novel corona virus [COVID-19]. Other nations, institutions, and corporations are also cooperating on molecular assays to diagnose COVID-19, including — China, Germany, Hong Kong, Japan, Thailand, USA. A special English-Chinese translation engine for scientific and medical use is being made freely available to researchers around the world by UK-based St. John’s Innovation Centre.

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learning about machine learning

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Why is machine learning [ML] important for your business? If you work at Nokia, your Chairman can explain it to you in a one hour presentation he developed over six months of research. Risto Siilasmaa helped make his network smarter. Everyone needs to know if ML can help with their business problems, but first they have to understand the basics, says Siilasmaa.

  • Digitization has created an explosion of information
  • ML is based on models like logistic regression, which can be fairly easy to understand
  • ML is fitting the model to the data
  • ML is neural networks learning from data sets
  • The more high quality data, and computing power, the fewer mistakes ML will make
  • In a large neural network you can have 100 million parameters in a single layer
  • Flawed outputs can happen if human oversight confirms incorrect ML conclusions (human oversight becomes very important)
  • A neural network first learns from a data set (time consuming) and then can be tested against other data sets
  • The important work is done by systems of ML systems
  • Machines are still getting faster and more tools are being developed
  • The data we are helping create (e.g. through use of speech recognition) is feeding AI corporations
  • ML can be tricked if you know the underlying algorithms
  • Remember: Garbage-in, Garbage-out
  • Big question: What data will we need in the future to make better decisions?
  • Business and human work is moving to — Low Predictability + High Complexity
  • ML can help to experiment faster and better in order to deal with Low Predictability + High Complexity
  • The future of work: First experiment … then develop a strategy

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RTFM

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If you find that people on social media have a tendency toward anger and outrage there is one action we all can take to diffuse the situation. It’s simple, but first we have to stop and think. If there is but one practice that will help make social media more civil, it is to always read the full article or reference before sharing and especially before commenting. In short — RTFM.

I recently posted a link to an article on Twitter — How McKinsey Destroyed the Middle Class — with this quote, “In effect, management consulting is a tool that allows corporations to replace lifetime employees with short-term, part-time, and even subcontracted workers, hired under ever more tightly controlled arrangements … Technocratic management, no matter how brilliant, cannot unwind the structural inequalities that are dismantling the American middle class.”

I don’t agree with the entire article but there is some truth that large consultancies have helped to get rid of middle management, blocking career growth for workers at the bottom of the hierarchy, and shifting non-management personnel to contracted or part-time workers.

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when trust is lost

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When trust is lost, knowledge fails to flow. When knowledge flow is stemmed, trust is lost. There is widespread outcry in China over the death of Doctor Li Wenliang who identified the novel corona virus, was reprimanded by the police for discussing it in public, and then died from the virus.

“For many people in China, the doctor’s death shook loose pent-up anger and frustration at how the government mishandled the situation by not sharing information earlier and by silencing whistle-blowers. It also seemed, to those online, that the government hadn’t learned lessons from previous crises, continuing to quash online criticism and investigative reports that provide vital information.” —NYT 2020-02-07

Contrast this with the sharing of research about the virus and how to counter it among the global medical and immunology communities. Researchers in one time zone work all day and then pass off their findings to teams on the other side of the earth. It’s a 24/7 example of working out loud and learning as the work.

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skepticism and complexity

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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.

“Skepticism: the mark and even the pose of the educated mind.” —John Dewey

“A developed country is not a place where the poor have cars. It’s where the rich use public transportation.” —@EnriquePenalosa

“We rarely appreciate all that our enemies do for us. They test us and make us stronger. They exploit our flaws, motivating us to correct them. They show us what we do not wish to become. So many memorable lessons. What fine teachers our enemies are.” —@TheStoicEmporer

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anger, outrage & belonging

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A topic of conversation in our monthly coffee club video call this morning was polarization — how different sides increasingly do not listen to each other but instead amplify their own positions. We can each come up with several examples, either from the political, or cultural spheres. Social media have made us all spectators in various clashes, as I noted about the Internet of Beefs. Each side is focused on winning but in the end, like many a divorce, neither side does.

“Listening to SCAN on the radio this last week, especially on the AM band—and then watching and listening to much of the Impeachment trial on TV and radio—it became clear to me that the Republican and Democratic parties are like divorced parents fighting over children who are also taking sides. Typically of people who don’t get along, they make broad and demeaning assumptions about each other, full of characterization and dismissiveness. Whether they are right or wrong about each other are beside this simple point: they are locked in a conflict that will only be resolved, unhappily, when one or the other wins. —Doc Searls 2020-02-01

Unfortunately — in an economy fueled by advertising — taking a neutral position does not make business sense. Constant outrage brings more eyeballs, so that is what both mainstream media and consumer social media encourage. Outrage has made Facebook so successful. Leaning toward neutrality — like the news outlet Ha’aretz does — is a dangerous business position when advertising pays the bills.

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the cooperative imperative

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Collaboration is working together for a common purpose, often directed externally by a boss or client. Cooperation is freely sharing with no expectation of direct reciprocity — quid pro quo. Nicholas Christakis’s ‘social suite’ is a blueprint of a range of traits that are common among all human societies, though not always manifested in the same way. One of these common traits is cooperation.

In our society, the market currently dominates how we organize. It is competitive. School is competitive, with individual grades. Work is competitive, with many more applicants than positions available. Individual performance reviews dominate in the workplace. We are told that we have to create our personal brands, because the world is competitive. But is this natural?

According to The Collaboration Paradox: Why Working Together Often Yields Weaker Results, some of the reasons that workplace collaboration fails is due to — overconfidence in our collective thinking, peer pressure to conform, and reliance on others to do the work. The article goes on to show that collaboration works when — we work with people with different skills, we do what each person does best, and we all contribute our own work.

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constant doubt and outrage

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When I was visiting Rome in 2012 I met a fellow tourist, an older gentleman from Australia, who told me that he had stopped a pick-pocket on the train who was trying to lift his wallet. He had cried out and grabbed the thief’s hand. As the train came to a stop, the locals on the train created a human wall and forced the thief out, while at the same time calling for the police. They then apologized on behalf of their city. Rome is a 2,750 year-old community that keeps on trying, in spite of its challenges, because its people believe in the city. This is how most humans act — cooperatively — most of the time, as this is part of our common social suite.

The Internet of Beefs (IoB)

But we are also influenced by our social networks and when these become what Venkatesh Rao calls the Internet of Beefs (IoB) then we collectively drag ourselves down. Rao defines two groups — Knights and Mooks — who continuously do battle on digital social media. Each Knight has many follower Mooks, and these Mooks do battle in the Knight’s name. Rao says that one such Knight is Nassim Nicholas Taleb.

“And in one corner by himself, of course, is Nassim Taleb beefing with all comers on all topics … Taleb muddying the factional boundaries of the culture war is one of the few genuinely amusing theaters of the conflict on the IoB. The blast radius around his twitter feed is not a safe space for anyone besides members of his own cult of Mesopotamian personality.”  —Venkat Rao

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keep it simple

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It is informative to have your work reflected back by others who have interpreted it in their own ways. This feedback gets integrated into my own continuing development of my sensemaking frameworks. Making these frameworks as simple as possible, but no more, has been my work since 2003 when I decided to become a freelancer and start blogging my ideas ‘in the open’.

“One of the golden rules of sense-making is that any framework or model that can’t be drawn on a table napkin from memory has little utility. The reason for this is pretty clear, if people can use something without the need for prompts or guides then there are more likely to use it and as importantly adapt it. Models with multiple aspects, more than five aspects (its a memory limit guys live with it) or which require esoteric knowledge are inherently dependency models. They are designed to create a dependency on the model creator” —Dave Snowden (2015)

Karen Jeannette showed what PKM and Seek > Sense> Share meant to her.

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no cheap tickets

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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.

“There are no cheap tickets to mastery. You have to work at it, whether that means rigorously analysing a system or rigorously casting off your own paradigms and throwing yourself into the humility of not knowing” —Donella Meadows

“Any method that starts by asking you to imagine an ideal future is deeply suspect from a complexity perspective. Far too likely to give an excuse to avoid the reality of the present.” —@snowded

“As Socrates showed us, there exist few quicker ways to make enemies than to ask the right questions of those vested in the wrong answers.” —@MattPirkowski

“If everyone did their best, 95% of problems would remain.” —W. Edwards Deming

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