Innovation

Innovation

connections trump expertise

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A recent research paper — Orthodoxy, illusio, and playing the scientific game — looks at why it took so long for the mainstream medical community to accept that the SARS-CoV-2 virus is predominantly spread as an aerosol and not through surface transmissions.

Three fields—political, state (policy and regulatory), and scientific—were particularly relevant to our analysis. Political and policy actors at international, national, and regional level aligned—predominantly though not invariably—with medical scientific orthodoxy which promoted the droplet theory of transmission and considered aerosol transmission unproven or of doubtful relevance. This dominant scientific sub-field centred around the clinical discipline of infectious disease control, in which leading actors were hospital clinicians aligned with the evidence-based medicine movement. Aerosol scientists—typically, chemists, and engineers—representing the heterodoxy were systematically excluded from key decision-making networks and committees. Dominant discourses defined these scientists’ ideas and methodologies as weak, their empirical findings as untrustworthy or insignificant, and their contributions to debate as unhelpful. —Wellcome Open Research 2021-05-24

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you are a commodity and don’t know it

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In platforms and the precariat I asked —If you are not one of the recognized best in your field, can you make a living online or are you just part of some platform’s long tail, valuable only to aggregators and their advertising revenues? As a content creator are you providing the fodder that lets Spotify, Amazon, and YouTube earn huge market valuations? Will there be a middle class in the emerging network creative economy?

Ross Dawson’s 2012 observation was that “in a connected world, unless your skills are world-class, you are a commodity”. Three things will differentiate professionals in such an economy — Expertise, Relationships, and Innovation.

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trust emerges over time

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Imagine a research-intensive organization where scientists should be sharing what they learn, and the official company policy is to share information and expertise among public and private partners. However, the company is ‘downsizing’ and layoffs are based on performance reviews. If one scientist helps a peer develop a patented product, and as a result the peer gets a better annual review, then the former may end up losing their job during the next round of layoffs. This was the situation I found myself in a decade ago.

Sharing knowledge was not a good personal strategy in this work environment even though it was official policy and was the focus of our project. We could not achieve our project objectives because systemic barriers pitted workers against each other in order to remain employed.

In this case, financial rewards for patents impeded learning, and in the end halted any knowledge sharing. In complex systems, the solutions are never simple, but our only hope is learning how to learn better and faster — individually, in teams, as an enterprise, and as a society. If we want to promote learning through knowledge sharing we should first look at what is blocking it.

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the moral minority

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The elites in charge of organizations and institutions like to think they take into account the opinions of experts, but as this pandemic has shown, that is often not the case. The pandemic response in many countries is political, not guided by the best public health knowledge.

“On any particular issue, people at the bottom can usually claim the most expertise; they know their job best. And when someone at the top has to make a difficult decision, they usually prefer to justify it via reference to recommendations from below. They are just following the advice of their experts, they say. But of course they lie; people at the top often overrule subordinates … Elites like to pretend they were selected for being experts at something, and they like to pretend their opinions are just reflecting what experts have said (“we believe the science!”). But they often lie; elite opinion often overrules expert opinion, especially on topics with strong moral colors.” —Overcoming Bias

If there is a moral aspect to the decision, elites feel even more justified in their decisions for doing the ‘right’ thing in spite of contrary evidence.

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beyond civil society, governments, and markets

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Binary thinking is an easy sell. It appeals to our emotions which we developed as children. Binary thinking blinds us. It’s not black and white, or right and wrong, or even Left and Right. Human society is many shades along various spectra. But often politicians and others tell us it’s a simple, binary choice — ”You’re either with us, or with the terrorists.’‘ —President George W. Bush (2001)

Thinking of our society as only Markets and Government (Institutions) ignores the influence and potential of families, communities, and the volunteer sector.  For instance, Public-Private Partnerships are not inclusive. They ignore the Civil sector.

“Every day I’m told our society, our system, has two sectors: the public sector and the private sector — the former referring to government and its agencies, the latter to the market system and its businesses. I’m also told that one sector or the other, or both in partnership, say as a public-private hybrid, offers the best way to deal with this or that domestic policy problem.

Our politicians, policymakers, and media commentators constantly rely on this public-private framework when they talk about fixing America’s health, education, childcare, housing, welfare, infrastructure, energy, communications, and environmental issues. Some proposals call for broader government programs; others urge more privatization; a few recommend improving public-private collaboration.” —David Ronfeldt

Incorporating the third sector, civil society, into decision making is becoming evident in our connected world, especially with an ongoing pandemic.

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creative desperation for desperate times

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My personal knowledge mastery framework was a result of creative desperation. I had just lost my job. My wife was a stay-at-home mother, we had two young children, and we live in a remote economically depressed part of the country. I had spent the last five years working for a learning technologies research centre and then an e-learning start-up. Suddenly I was a freelancer, physically disconnected from any potential clients. It was 1,000 km to the nearest major urban centre.

Gary Klein’s research in, Seeing What Others Don’t, identified five general ways that we gain insights.

  1. Curiosity
  2. Connections
  3. Coincidences
  4. Contradictions
  5. Creative Desperation

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new societal infrastructures

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Continued from — nine shifts

In 2004 Bill Draves and Julie Coates wrote Nineshift: Work, life and education in the 21st Century. That was the same year I started blogging here. Nineshift is based on the premise that during the first two decades of the 21st century, there will be a major shift in how we spend 9 hours of each day.

“There are 24 hours in a day. We have no real discretion with roughly 12 of those hours. We need to eat, sleep, and do a few other necessary chores in order to maintain our existence. That hasn’t changed much through the centuries, so far.

That leaves approximately 12 hours a day where we, as individuals, do have some discretion. That includes work time, play time, and family time.

Of those 12 hours, about 75%, or 9 hours, will be spent totally differently a few years from now than they were spent just a few years ago. Not everything will change, but 75% of life is in the process of changing right now.”

The authors put forth that society would significantly shift what we do with those nine hours and this would be complete by 2020.

  1. People Work at Home — “Work is an activity, not a place.”
  2. Intranets Replace Offices
  3. Networks Replace the Pyramid
  4. Trains Replace Cars
  5. Communities Become More Dense
  6. New Societal Infrastructures Evolve
  7. Cheating Becomes Collaboration
  8. Half of all Learning will be Online
  9. Education becomes Web-based

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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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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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organizations as media

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In discussing organizational models and metaphors, Naomi Stanford refers to Gareth Morgan and his influence on organizational design. “Gareth Morgan’s book Images of Organisation (1986),  for example, offered eight organisational metaphors …” — machine, organism, brain, culture, political system, psychic prison, flux & transformation, and instrument of domination. Other researchers have added to this list — icehotel, wonderland, femicide, justice.

In a 2011 interview, Morgan says that there is one more model that he would have liked to have included.

But, if I had a single choice, the metaphor that I most wish that I had included would be one based on communications theorist Marshall McLuhan’s view that all forms of technology are best understood as extensions of human senses and that “the medium is the message.” More specifically, the metaphor would explore “Organizations as Media” with a particular focus on the transformations created in the wake of phonetic literacy and the rise of new electronic media, particularly the digital forms that are currently unfolding. I believe this metaphor will put the history of formal organizations in new perspective and raise some interesting questions and challenges on how we can expect new organizational forms and associated economic systems to unfold in the years ahead. —Reflections on Images of Organization and Its Implications for Organization and Environment

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