Looking Forward

There have been a lot of things we haven’t been able to do during the last nine months. But it’s been a good time for reading ebooks and listening to audiobooks. So my on-again-off-again tradition of highlighting interesting books that I have read in the year is on again.

These books have not all been published during the last year, but are ones I’ve read this past year and thought worth mentioning to other folks who read this blog. You’ll note that this is an eclectic combination of books on technology, government, the economy and other non-fiction – but that’s the range of topics that my blog is about.

Anyway, here’s my list for 2020 and a blurb as to why each book is on the list.  I have obviously eliminated from the list the many other books that I’ve read, which I would not recommend you spend your time on. 😊

Technology, AI/Machine Learning and Science

1.      David Carmona - The AI Organization: Learn from Real Companies and Microsoft's Journey How to Redefine Your Organization with AI (2019). Perhaps too many examples from Microsoft, but it is a really good book from A to Z on artificial intelligence.

2.     Cliff Kuang and Robert Fabricant - User Friendly: How the Hidden Rules of Design Are Changing the Way We Live, Work, and Play (2019).  Very interesting review of the leading good (and sometimes bad) user interfaces.

3.     Matthew O. Jackson - The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors (2019). Good, understandable explanations of network measures and phenomena in various domains.

4.     Damon Centola - How Behavior Spreads: The Science of Complex Contagions (2018). Provides a nuanced view of the best time to use weak or strong ties, especially in leading changes in an organization or community.

5.     Eric Topol - Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again (2019). Although it is mostly about the ways that artificial intelligence can re-humanize the patient-doctor relationship, it even has a pretty good, understandable review of general artificial intelligence and machine learning concepts.

6.      Lisa Feldman Barrett - How Emotions Are Made: The Secret Life of the Brain (2017). The title highlights emotions, but this book is not just about emotions.  It instead offers a paradigm shift about how the brain works.  

7.      Jodie Archer and Matthew L. Jockers - The Bestseller Code: Anatomy of a Blockbuster Novel (2016). interesting book, better and more nuanced than the usual summaries about machine learning models to predict the success of books.

8.     Leonard Mlodinow - The Drunkard's Walk: How Randomness Rules Our Lives (2009). Interesting explanations of the implications of probability theory and how most people get probability wrong.

9.     Scott Rigby and Richard M Ryan - Glued to games: how video games draw us in and hold us spellbound (2011). Good review of computer-based games, especially the psychological aspects.

Leadership And Business

10.  Jim McKelvey - The innovation stack: building an unbeatable business one crazy idea at a time (2020). Good, insightful and sometimes funny book by one of the co-founders of Square, with the proposition that success is the result of a chain (better word than stack) of innovations rather than just one big one.

11.   Scott Kupor - Secrets of Sand Hill Road: Venture Capital and How to Get It (2019).  If you want to know how venture capitalists look at startups, this tells you how.

12.   Geoffrey G. Parker, Marshall W. Van Alstyne, Sangeet Paul Choudary - Platform Revolution: How Networked Markets Are Transforming the Economy - and How to Make Them Work for You (2017).  While other books on the subject go more deeply into the broader policy implications of platforms, if you want to start a platform business, this is your best, almost required, user manual.  

13.  Daniel Coyle - The Culture Code: The Secrets of Highly Successful Groups (2018). Culture is a frequently used word to explain the forces that drive behavior in organizations, but too often the concept is fuzzy.  This book is one of the clearest and best on the subject.

14.  Dan Heath - Upstream: The Quest to Solve Problems Before They Happen (2020). Good, as usual for the Heath brothers, well written down to earth, but important concepts underneath and guidance at looking at the more fundamental part of problems that you are trying to solve.

15.  Matt Ridley - How Innovation Works: And Why It Flourishes in Freedom (2020). Includes many short histories of key innovations, not just invention, with an emphasis on the iterative and collaborative nature of the innovation process.  Ridley advocates curtailing IP protections, thus providing more tolerance of risky experiments/innovations.

16.  Rita McGrath - Seeing Around Corners: How To Spot Inflection Points In Business Before They Happen (2019).  Columbia Professor McGrath has made clear that no strategy is sustainable for a long time and in this book, she helps you figure out when you are at good or bad inflection points.

The Economy And Government

17.   Robert H. Frank - Under the Influence: Putting Peer Pressure to Work (2020).  Frank is one of the most creative economists around and in this review of behavioral economics, he highlights how people pursue relative positions of wealth, rather than merely being rational maximizers of wealth.  He also offers a good discussion of public policies to pursue, that are based on this understanding of economic behavior.

18.  Stephanie Kelton - The Deficit Myth: Modern Monetary Theory and the Birth of the People's Economy (2020). Well written, clear exposition of modern monetary theory and the positive and negative consequences of having completely fiat money (no gold standard or fixed currency exchanges). Professor Kelton is an increasingly influential economist and her ideas – whether or not she is given credit – have enabled the US Government to spend more with less angst than used to be the case.

19.  Abhijit V. Banerjee and Esther Duflo - Good Economics for Hard Times: Better Answers to Our Biggest Problems (2019). A review of economics research – and, more important, its limits – in addressing major socio-economic problems.

20. Matthew Yglesias - One Billion Americans: The Case for Thinking Bigger (2020). Although no one (including me) will agree with everything he proposes, this is an interesting book with some original forward thinking – something we need more of as we face a very changed future.

21.  Michael Hallsworth and Elspeth Kirkman - Behavioral Insights (2020). This is a good overview of the application of behavior research to mostly public policy, especially about the UK.

22. Paul Begala - You're fired: the perfect guide to beating Donald Trump (2020). Smart and realistic proposals for the campaign to oppose Trump with many very funny lines.

23. Jane Kleeb - Harvest the Vote: How Democrats Can Win Again in Rural America (2020). Along with Begala, explains her own success in rural America and more generally what needs to be done by Democrats to regain their old reputation as the party of the majority of people.

24. Mark Lilla - The Once and Future Liberal: After Identity Politics (2017). Short review of how the Democratic party became dominated by identity politics and, for that reason, provides a bit of background for the previous too books.  

Have a happy holiday season and a great, much better, year in 2021!

Not that we have to be reminded, but COVID has certainly reminded us of the fact that we are all connected, in varying degrees, and have an impact on each other.  This reminder from a pandemic shouldn’t be a surprise since some of the earliest and best work on real network effects between people has been done by public health experts.

The now classic 2009 book, Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives, by Nicholas Christakis and James Fowler showed the ways that our networks with other people affect, among other things, our emotions, political views, wealth, and health (even body weight).  Given that one of the most important origins of the study of human networks was in public health, it won’t be surprising that Christakis, who specialized in public health, was particularly interested in the long-running Framingham study of health.

For the rest of us, who are not in public health, it would seem that viral pictures and memes on the net form our understanding of network effects.  But there is much more to these connections than simply passing images and memes to one another – even much more than passing diseases to each other.

There have been, at least, three relatively recent books that help describe in more useful and original detail the impact of each of us on each other.  The ideas in these books apply not just to health, but to the economy, business and career success as well.

Robert H. Frank is one of the most creative and insightful economists around.  His book this year, “Under the Influence: Putting Peer Pressure to Work”, is a review of behavioral economics, with an emphasis on network effects.  That includes how it is not only the absolute value of goods and wealth that motivate economic behavior, but our ownership of goods relative to others.  Rather than maximizing wealth, per se, many people want to maximize their relative position.  Thus, he points out that having another billion dollars is not so important to the richest man, as long as he is richer than other men.  (This has important implications for tax policy and other public policies, which he discusses the book.)

Matthew O. Jackson’s 2019 book “The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors” extends the work of Christakis with explanations of network measures and phenomena in the economy. Those economic phenomena include two very relevant to our times – economic inequality and economic crises (or, to put it more simply, economic bubbles that burst).  He shows how the well-known tendency of people to connect with others who are similar to themselves can generate inequality.

In an elaboration of the old adage – “it’s not what you know, but who you know” – Jackson also describes the various ways that our position in our networks influence our behavior and outcomes for us in those networks.

Of course, you may know some people very well, like family, and others not so well.  As Duncan Watts showed in his 2004 book, “Six Degrees: The Science of a Connected Age”, the people you don’t know so well – those with whom you have weak ties – can connect you to almost anyone else on the globe in just a few steps. 

One popular application of that idea is that it is weak ties that are the key to success in finding out about job openings or getting major initiatives accomplished in a large organization.  We’ve all heard about the importance of “networking” in career success.  But, like all such popular ideas, the importance of weak ties has been applied to too many situations, some of which are inappropriate.  

That is the point of Damon Centola’s 2018 book, “How Behavior Spreads: The Science of Complex Contagions”.  He provides a more nuanced view of the best time to use weak or strong ties.  If the goal is to quickly pass information – such as about a job opening or a cute meme – then weak ties will indeed do the best job. However, if the goal is to change the behavior of people, something not as casual as passing along a joke, then having someone lead the change in a networks of strong ties are more effective.

This post can only provide a summary of three of the most interesting authors.  It is worth reading each of these books as well as other books and articles that have contributed to our understanding of this important, but complex, subject of human interactions.

Expect more new knowledge in the future.  After all, there is now a lot of and a growing amount of big data about human interactions that can provide a gold mine to be explored with network analytics, but more generally machine learning and AI.

A final thought … You would expect that the importance of these phenomena of social influence would be built into many models of individual human behavior and then aggregated, network style, into larger models to understand macro phenomena and trends.  Curiously, such network-informed models are few and far between – but that’s a topic for a future blog post.

As a member of a couple of angel investor networks, a former software executive, and a teacher of a graduate course on new product/service creation, I have seen many financial projections from startup founders or even new product managers in large companies.

One very common pattern for sales projections is a straight rising line (simple linear trend). Here’s one that shows consistent growth in sales, with expenses following along in a similar path. Breakeven occurs around the fourth time period — perhaps that’s the second half of the second year.

More optimistic projections take the hockey stick approach. The folks with hockey stick graphs always show the long arm of the stick going up — this product is just going to take off and sales will go through the ceiling!

More often than these hopeful folks realize, the hockey stick goes the other way.

The underlying theory of sales growth in these charts is unclear — if there is even some kind of well-thought out model underlying them. Often there isn’t one and the creators of these projections are just playing with arithmetic.

An improvement over the simplistic linear or wishful hockey stick “model” is the four-stage product life cycle — launch, growth, maturity, decline. With that in mind, you might get to see sales projections that follow this pattern.

It’s a more nuanced, maybe even reasonable, basis for estimations in spreadsheets. But aside from launch, these phases are not easy to identify in real-time. Moreover, these phases are so general as to be generally useless in practice. And that is because this kind of curve has not been tied into what is known about patterns of adoption of new products or services, especially technology products.

There are better ways of thinking about sales projections for new products from startups or established companies. These better approaches are not original with me nor are they new, although they do not seem to have the popularity that you would expect.

Here then is a picture of the general pattern of adoption of technology products — what has long been called the diffusion of innovation or, with the notch between the second and third groups, the chasm that new products need to get across in order to be successful at scale.

While the speed of adoption and the actual distribution of various groups is not always a nice normal distribution, it provides a useful framework to identify when different kinds of people will adopt the new product or service. (This isn’t the place to go into the details about the characteristics of each of the five groups. More about that can be found in such classic books as Diffusion of Innovations by Everett M. Rogers and Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers by Geoffrey A. Moore.) 

There has been a lot written on this S-curve, but curiously very little of it ties the S-curve to the financial demands of a startup or a new product launch in an established company. While many people seem to have a basic understanding of the S-curve for the adoption of new products/services, they don’t link that to the money they need. But the pattern of adoption has direct financial consequences.

What do these financial consequences look like? Consider this picture.

The model can be more granular, but this simple model illustrates the point. The expenses match the various stages of the adoption cycle, including the marketing costs of getting past the chasm. The model also reflects the fact that later adopters usually require more support in their use of products and even greater attention to an easy user interface than at the start. The model also reflects the need to create the product before there is any paying customer at all.

The technology adoption cycle provides the framework, but the actual pattern for a new product can be updated with real-time data that reflects how fast adoption is moving and into which groups.

With this kind of model in hand, the product planner can estimate the pattern of product adoption in the future. In turn, that enables better financial planning to estimate future profits.

It’s worth also saying that in startups, in particular, cash is king. No matter what your profits might be on paper, if there isn’t enough money to pay for essentials — say the salaries of key employees who are writing your software — then your startup is in deep trouble. In startups, misjudgments about the money coming into the business versus the money going out of it can be fatal. So startups are especially vulnerable to inaccurate sales projections.

Whether it’s a new product in a big company or a startup, even a sales projection (and updates) based on an understanding of the adoption of innovations does not necessarily guarantee a big success. But at the least this kind of smart sales projection will help avoid a nasty surprise that leads to a cash crunch.

Product differentiation is often described as the key to business success. Companies are told that unless they really stand out from the crowd, their products or services will become “commoditized” — an undesirable position in the marketplace that results in little or no profit. This has been well-established guideline in the world of technology startups and even new technology-based product development in existing companies.

And that guidance is mostly right. Distinguishing your products from the crowd of competitors often results in greater than average profits. Consider Apple, with less market share than Android, but lots more profit than its smart phone competitors.

Of course, how to go about this is not so simple. One of the best and most inspiring books about how to differentiate — how to be really different — is Harvard Business School Professor Youngme Moon’s book, Different: Escaping the Competitive Herd — Standing Out In A World Where Conformity Reigns But Exceptions Rule.

These quotes summarize her forceful advice:

“What does it mean to be really different? Different in a way that makes a difference. It could mean doing the opposite of what everyone else is doing — going small when everyone else is going big…

“You could even say that breakaway brands revel in our stereotypes, since they make their living turning them upside down…

“These brands are the antithesis of well-behaved, and their mutiny is directed squarely at the category assumptions we bring to the table. And sometimes the transgression is more than a touch provocative; it’s a bit twisted as well. …

“What a breakaway positioning strategy offers is the opportunity to achieve a kind of differentiation that is sustainable over the long term. … it has no competitors; it remains sui generis.”

This advice applies not only to business, but can also apply to politics. That’s why I wrote a post four years ago called “The Breakaway Brand Of 2016” about the 2016 US Presidential election. Although I doubt that he read her book and his approach certainly didn’t please Professor Moon, Trump seemed to have been using it as his playbook for the 2016 election. His was the perfect exemplar of a breakaway brand in politics.

Now the 2020 Election also showed the limits of this approach. In a two-way election in the US, you need a majority (putting aside the Electoral College, for the moment).

It is also often the case that being different means you won’t get a majority, as both Apple and Trump have found out. For Apple, that’s not a problem. For Trump, it meant he lost the election.

While he did receive many votes, the limits of breaking too far away in politics was well stated by the most successful politician in American history, Franklin Roosevelt: “It is a terrible thing to look over your shoulder when you are trying to lead — and find no one there.”

The limits of extreme differentiation are clear enough in electoral contests. But the election result also reminded me that there are limits to being different in business too. I’m especially thinking of most established technology-based, multi-sided platform businesses (like Amazon) and other businesses that depend on direct network effects (like Facebook).

These businesses also need to have a majority (or even more) of the market. That’s because their value to customers depends a lot on network effects. Being too different for most people will mean you do not end up getting the majority of people as customers.

So, differentiating — even creating breakaway brands — is certainly good advice in general. But like any advice, it is not always appropriate. And the art of leadership is knowing when not to follow generally good advice and take a different road — even a different road about being different.

I don’t think I have ever written about my teaching duties before.  But circumstances change, so here goes. 

I have been teaching online since before COVID forced most classes online. Each semester I have an online class I try to experiment and improve.  

But the COVID pandemic has forced an extra dose of creativity and a re-thinking of ideas – some new and some old – about education. Here I want to share with other educators some of what I have learned in the process.  I’ll keep it general as I hope it will contribute to a discussion about how education will occur going forward.

Flipped Classroom

The flipped classroom is not a new idea.  But since long lectures in Zoom taxes almost everyone’s powers of concentration, we made the move to a completely flipped classroom for the completely online courses that are the norm now. 

What used to a live (synchronous) class that combined a lecture and some student interaction has become a workshop this semester. 

The Overview “Lecture”

The lecture material, really an overview of the week’s topic, is now a recorded video that students watch before they go to the live class.  This can be anywhere from 15 minutes to an hour depending upon where we are in the course and the topic. 

As before, I tend to use video of other speakers to break up the experience so that the students don’t just watch me or my slides. This also lets the students see that some of the ideas they are being taught come from other human beings, not just textbooks – and they can see those other folks, in all their glory and with their tics, quirks, etc.  Video is also useful to practice the old adage that it is better to let someone see the story than to relate it to them.

Making The Lecture More Interactive

Because the “lecture” is a recorded video, we can lose the opportunity that students have in synchronous classrooms to ask questions, make comments and contribute to each other’s knowledge.  After creating a video in PowerPoint, we don’t just post the video online.  Instead, we use VoiceThread which enables more interaction.  Students can insert comments, questions, replies of any kind – using text, voice or video.  My students have generally stuck to text.  Then the faculty and other students can reply. 

It’s not quite the same thing as a lecture in a live, synchronous classroom, but it comes close enough.  In the first three of these videos, we have averaged about 50 comments each.  That is a good level of engagement, in fact much more than was the case in the face-to-face classroom equivalents of these lectures.

Although VoiceThread integrates reasonably well with the learning management system we use – Canvas – it has its limits for this purpose.  We can set up an assignment that requires students to watch the whole video, but VoiceThread only seems to enable this to happen if the students look at all the comments that have been inserted into the video.  From the perspective of increasing their learning, that’s not such a bad idea, but it would be nice to require them just to see the video.  Apparently, that feature is coming sometime in the future.

And Zoom, Of Course

Like many others, we use Zoom for the synchronous class sessions, which are workshops in our case.  A typical session starts off with a review of any issues that arose in student assignments in general.  Then we turn to the draft of an assignment the students worked on before class. That assignment is usually the completion of an analysis in a workbook which is relevant to the topic of the week.

By now, most people are familiar with Zoom so there is a little learning curve. And, as software goes, it is stable.  Even when it runs into a problem, it will reboot itself and pick up in the meeting where it left off.

From the teacher’s view, there are at least two benefits in comparison with the traditional classroom.  First, you can more closely scan the faces of students to see if they are engaged. 

Second, it is easy for students to show their work to the whole class by sharing their screen.  In traditional face-to-face classrooms, it would take a couple of minutes for a student to get up and make the transfer to some device that everyone could see – and in that process the momentum of the discussion would be broken. Now, it happens in a second.

With this ability, we ask two or three students to show their work to the whole class in Zoom.  Then both the faculty and other students ask questions and provide feedback on that work.  This not only helps the students who are getting this feedback, but it helps other students to realize what they too might have missed or need to do.

Breakout Groups

Then the students are put into small breakout groups where they present their work to each other.  This is very useful especially for the rest of the students who weren’t lucky enough to be selected for the class-wide presentations.  

We use Zoom breakout groups with random assignment. When students are only paired, there can be a lot of breakout groups.  Zoom can handle this number. 

However, it has its limitations which in part reflect the challenge the company faces in addressing its diverse markets.  In our situation, we have more than faculty member and want each to drop in and out of these break out groups to see how things are going. We finally figured out that we need to make them co-hosts before the break out, but it still isn’t the smoothest process.

We had hoped to use BigBlueButton (BBB) for breakout groups.  BBB is video software specifically designed for education.  Frankly it wasn’t great a few years ago, but it has been much improved recently.  It looked like a better way than Zoom for us to do class breakout group and its user interface and features were better.  But unfortunately, BBB has a hard-coded maximum number of breakout groups, which is 8 – too little for our purposes.

Music

We all face that period on Zoom before class starts and the students are straggling in.  (This behavior seems to be a carry over from physical face-to-face classrooms. 😉) What do you do to get the attention of students, maybe even encourage their on-time attendance?

One of my colleagues suggested using music in the three minutes or so before class starts.  She had in mind some strong, percussive music to wake up the students.

That seemed like an idea worth trying.  But I didn’t want just any music. I thought it might be useful to have a song that was appropriate to the topic of the class.  And a couple of months ago, I spent more time than I should have searching for just the right percussive, but appropriately themed, music to use.  It was a mix, although mostly classic rock.

And it worked!  Students show up early chatting with each other about what the song might be and about the song when they hear it.  In my last class, I even got a request to set up a Spotify playlist of these songs.

The Results So Far

Overall, the results so far have been very encouraging – better than expected and in many ways better than traditional classrooms. Students seem to grasp the subject matter better, which is the primary aim of course.

But they are also engaged much more.  Attendance has been near perfect. 

Another measure tells the story better.  The online class is officially 90 minutes long, ending at lunchtime on a Saturday.  At the official end, I tell the students that they are under no obligation to stay longer. Yet, in the three classes we’ve had so far, a majority of the students stay for more than a half hour to an hour more. Several stay on in Zoom longer than that – some for two hours (when I told them I had to shut it down).

Your experience may vary, since each class and cohort of students is different. These were about sixty students in a master’s degree professional program at Columbia University. But before you jump too quickly to the conclusion that these lessons aren’t relevant to your students, you might want at least to try them. 

Do your own experiments and contribute your own observations to this discussion about teaching in a COVID-infused online world – and the world that will be changed after COVID is controlled. After all, it is not just the people in front of you who are students, but all of us are lifelong learners.

This past week started the COVID-postponed Intelligent Community Forum’s Annual Summit – now virtual and continuing over two weeks.  As usual as Senior Fellow at ICF, I made a presentation yesterday and led a workshop on “Bringing Broadband To Your Community”.  

I have previously reported on what is happening in cities this year. In the face of COVID-inspired video conferencing and the departure from offices and some previously popular cities, the question is raised again – can we level the playing field again between the biggest metropolises and elsewhere in the US that have not had broadband?

Many communities now recognize that they will be completely left out of a post-COVID economy.  They are hoping that some outside organization – a benevolent telecommunications company or some government agency – will come in and make the necessary investment so that their community has the broadband it needs.

Considering how many politicians have included broadband as a basic part of our infrastructure, it may be possible that at least the government will provide a lot of funding next year.  But it is worth noting that talk about the government investing on broadband is not new and not all that much has happened in the past.

So in my presentation at the ICF summit, I drew attention to some examples of communities that just went ahead and built this for themselves.  You may have already heard of Chattanooga, Tennessee and Lafayette, Louisiana, both of which deployed broadband through their electric utilities that are owned by the city government.

But here I want to give some credit to two examples that are not so well known.  The first is in a poorly served urban community in San Francisco.  The second is in a rural area that had expected to be the last to get broadband in England. 

Although San Francisco bills itself as the high-tech capital of the world, the reality is that 100,000 of its residents (1 in 8) do not have a high-speed Internet connection at home.  This situation, by the way, is not unique to San Francisco. Many otherwise well-connected cities have vast areas without affordable broadband – not quite Internet deserts, but with Internet effectively out of reach to low income residents for technical or financial reasons.

So in conjunction with an urban wireless Internet provider, Monkeybrains (great name!), the city government rolled out its Fiber to Housing initiative last year.  According to a report “Can San Francisco Finally Close its Digital Divide?” in November 2019, they had already free, high-speed internet to more than 1,500 low-income families in 13 housing communities – public housing.  By this past summer, the number was increased to 3,500 families.  While there is still a long way to go, the competition has already forced traditional Internet service providers to step up their game as well.  

In a very different community in rural England, there is a related story, except this region, unlike San Francisco, is the last place you would expect to find broadband.  In the northwest corner of England, surrounding the not-so-big city of Lancaster (population around 50,000), a non-profit community benefit society was created to provide broadband for the rural north.  It is called B4RN.

As they proclaim on their website, they offer “The World’s Fastest Rural Broadband [with] Gigabit full fibre broadband costing households just £30/month”. As of the middle of last year, they had more than 6,000 fully connected rural households.  

In speaking with Barry Forde, CEO of B4RN, I learned a part of the story that should resonate with many others.  The community leaders who wanted to bring broadband to their area tried to explain to local farmers the process of building out a fiber network.  They noted that the technology costs of these networks are often dwarfed by the construction costs of digging in the ground to lay the fiber. The farmers then responded that digging holes was something they could do easily – they already had the equipment to dig holes for their farming!  With that repurposing of equipment, the project could move much more quickly and less expensively.

I can’t go into the whole story here, but this video gives a good summary of the vision and practical leadership that has made B4RN a success. 

Frankly, if B4RN can do it, any community can do it.  Whether it’s in one of the most costly cities or in the remote countryside, a little creativity and community cooperation can make broadband possible.

And it need not be gigabit everywhere to start or having nothing at all.  Build what you can, get people to use it and the demand will grow to support upgrades. An intelligent community grows step by step this way. 

These were the important lessons of the ICF Summit on Tuesday.

If you have a new product or service in mind, you know that you need to find a way to differentiate it from the alternatives that people are already using or could use. But then maybe you have a hard time coming up with ways to make what you are offering really different and new.

This is a basically a challenge to your creativity. And many of us think we need to twist our brains to come up with good creative ideas, which is hard work we don’t feel we can do.

Although we have come to frequently expect new technology products, the challenge of creativity is especially hard for technologists.  They have lived in a world that demands no software bugs, no downtime and the like.  They are by training (as the A students many were in school) and maybe by nature perfectionists.  

A perfectionist mindset undermines the kind of experimental approach and its possibility of failure which is necessary for innovation.  For that reason, creativity can seem to be an insurmountable, impossible challenge – to be both perfect and creative is a low probability occurrence.

Coming up with new ideas shouldn’t be such a challenge.  Consider just two of many authors.  Tina Seelig, Professor of Practice at Stanford, has written and spoken about creativity and innovation.   The titles of two of her books offer a quick summary of her themes — “InsightOut” Get Ideas Out Of Your Head and Into the World” and “inGenius: A Crash Course on Creativity” .

William Duggan of Columbia Business School has also written “Creative Strategy: A Handbook for Innovation” in which he champions the innovation matrix as a means of generating new ways of looking at the world. You break down what you’re trying to do into its parts and then search for any company that provides a model of how to do that part well. It’s a tool for what’s called recombinant innovation.

In addition to books on creativity, however, consider a methodology for analysis and software design from more than forty years ago that was named after its originators – Yourdon and DeMarco.  If it is remembered at all, it is for data flow diagrams.

That’s not what I want to emphasize here. Nor do I plan to lead an effort to revive the popularity of Yourdon-DeMarco structured analysis/design and the classic waterfall development lifecycle that it aimed to improve.  Nor am I advocating for the underlying idea that there could be a complete and correct design up front in that lifecycle.

Yourdon and DeMarco had even more important guidance for software designers, although that seems to have been lost in the history of software design.  

That guidance:  think more conceptually, more abstractly.  They distinguished between the logical level (the “what”) and the physical level (the “how”).   At the physical level, you would describe the implementation.  At the logical level, traditionally, you would describe essentially what the organization is trying to do.  When thinking about a problem, separate out its implementation (how you see it operate) from its intention.

When it comes time to re-design a system or designing a new product, you first rearrange what is happening at the logical level.  Only after that makes sense to everyone do you worry about how it will be implemented.

By the way, this is not something that requires an excessive amount of writing upfront. Instead, it is often better to explain this to someone else verbally. Because you are trying to communicate clearly and concisely in conversation rather than impress someone with a document.  

Look at what is happening and describe it in simple words, before you use a fancy name for it that you might have been taught.  Often the solution to a problem is obvious if you listen to yourself carefully.  (Maybe recording it helps.)  That’s what you should start with.

Thinking this way makes things clear and clarity yields insight. Sometimes the solution can be blindingly simple once you look at things conceptually. The ancient story of Alexander the Great and the Gordon knot is a good example. The knot only had to be broken. Instead of meticulously searching where to pull on it so it would unravel, he just cut it.

One often cited example of the reverse approach and of missed opportunities that result is in the transportation industry.  When airplanes and airlines first appeared, there was an opportunity for the railroads to invest and own the new industry.  Instead of thinking of themselves as the movers of people and goods over long distances (the higher conceptual level), they thought of themselves as the operators of railroads (the lower physical level).  As they say, the rest is history.

You don't need to twist your brain to arrive at innovative solutions.  Actually, conventional thinking often requires more brain twisting than creative thinking.  Using the approaches that I’ve outlined here require less, not more, brain twisting to be creative.

Today, something a little different, but not too different — it’s about one of the public policy implications of an important change in the economy that technology has enabled.

As we all know, the gig economy has been growing. According to a report from Upwork and the Freelancers Union this year, more than a third of the workforce is freelancing. Many of us make at least part of our living in the gig economy and most of the rest of us depend at least part of the time on people who are gig workers.

In California, there has been a movement to apply to gig workers some of the protections that were put in place for the fast-growing number of American industrial workers 80 to 100 years ago — minimum wage, a fixed work week, unemployment insurance, assistance due to workplace accidents and the like.

In response to California’s law that requires Uber and Lyft to reclassify its contractors as employees who are provided with employee benefits, the company proposed its own reform plan for the gig economy. Dara Khosrowshahi, Uber’s CEO, wrote an op-ed in the New York Times on August 10, 2020, titled “I Am the C.E.O. of Uber. Gig Workers Deserve Better. Gig workers want both flexibility and benefits — we support laws that could make that possible.”

In it, he proposed:

"that gig economy companies be required to establish benefits funds which give workers cash that they can use for the benefits they want, like health insurance or paid time off. Independent workers in any state that passes this law could take money out for every hour of work they put in. All gig companies would be required to participate, so that workers can build up benefits even if they switch between apps.”

The New York Times columnist Shira Ovide followed up with a story titled “Uber’s Next Idea: A New Labor Law …Uber’s “third way” would offer its drivers flexibility plus some benefits. It’s not totally crazy.” Hmm, not totally crazy? That doesn’t sound like an endorsement, but it’s also not dismissive. Something has to be done to equalize the protections for them with employees, while giving them the flexibility that Uber advocates.

In line with their approach, Uber and similar companies are supporting California’s Proposition 22 on the ballot this November to get them out from under the State government’s push to treat their drivers as employees. Not surprisingly, many progressive and labor groups oppose Prop 22. This picture illustrates the concerns of the opponents:

But there is a larger question here beyond benefits and rights for gig workers because the change in the nature of employee-employer relationships has been as significant as the growth of the gig economy.  With increasing automation and more coming with AI, de-unionization and frequent layoffs among other trends, frankly, a job is not what it used to be.  Moreover, the situation is not likely to improve since the long-term loyalty between employer and employee that was common decades ago is generally rare now.

It’s time to realize that the economy – not just for freelancers and gig workers – has changed a lot since the Progressive and New Deal reaction to the excesses of corporations a hundred years ago. The gig rights debate seems to be too limited and too much based on last century thinking which is increasingly inappropriate for our technology-based economy.

Putting aside the limitations of Proposition 22, why not take the general proposal for gig contractors that Khosrowshahi described in his NY Times piece and expand it?

Why not go full Uber! (Something Uber itself may not like, after all.)

What does that mean?  Gig workers need a better contract and so do “employees”.

Any individual – whatever the label – who is providing a service to a company would have a contract with that company which clearly states adherence to government laws and regulations on: minimum payment per hour, extra payment for more than a certain number of hours of work per week, expenses incurred performing duties on behalf of the company, safety, discrimination, normal workers compensation for accidents that occur while working on behalf of the company, and the right to form any association (union) they wish.  

Khosrowshahi emphasizes the freedom and control over their lives that gig workers have.  OK, maybe it is time to give employees that same freedom.

That brings up the other current disparities between gig workers and employees, especially health insurance, sick/family/vacation leave and unemployment insurance which are tied to employment status.  Gig/freelance workers need this as well, but it is also time to disassociate these benefits from the companies where people work – all in the cause of the freedom that Khosrowshahi promotes.

For example, the money companies used to spend on health insurance premiums and the like would now be paid directly to the employees.  The employees would get their own health insurance and not be limited to the third insurance plans their company has pre-selected. Government options could also be offered for health insurance.  (Similarly, gig or freelance workers could have those premiums built in to their contracts, at a minimum being the percentage of a full work week that they devote to the company.)

In this way, there would be no windfall for corporations after they would be relieved of paying benefits to employees.  The shift can be done in a revenue/cost neutral way, leaving employers, companies and governments financially where they were before the shift.  

Providing protections for everyone who works for someone else, no matter whether that’s on a gig/freelance basis or “permanently”, will help everyone get some more freedom from the fear of economic dislocation.  Also, they will finally have the freedom to pursue their entrepreneurial dreams as well, which could help grow the economy more than forcing them to be locked into jobs that don’t fulfill their potential.

Finally, governments will, in the process, have to adjust their understanding of the nature of work in this century, which is no longer what it was when most current laws and policies were put in place.

Almost every pitch deck for a startup (or even a new corporate-funded initiative) starts with a customer problem. In some form or other, the entrepreneur/intrapreneur says: "Here is a customer problem. The customer's problem is an opportunity for us because we know how to solve that problem." And then they go on to ask for the money they need to bring their solution to life.

Having been on the receiving end of these pitches many times, I have often thought that the presenter too quickly jumped on the first problem they saw and it was not the real problem the potential customer had. So if they tried to fix the superficial problem, the entrepreneur/intrapreneur would not get the market traction they hoped for – and it wouldn’t be worth it for us to invest in an idea with no traction.

That’s why in my last post I reviewed the key points in Dan Heath’s book “Upstream: The Quest To Solve Problems Before They Happen”.  In a nutshell, his message is that you have to go upstream beyond the first problem (downstream) you see and find the root cause of that problem.

An example of thinking about a root cause can be found in the 500-year-old poem that is supposed to have been about the English King Richard III's loss in 1485 at the Battle of Bosworth Field to Henry Tudor who then became king:

For want of a nail the shoe was lost. For want of a shoe the horse was lost. For want of a horse the rider was lost. For want of a rider the message was lost. For want of a message the battle was lost. For want of a battle the kingdom was lost. And all for the want of a horseshoe nail.

It isn’t always easy to figure out where upstream the problem is.  In post-mortems on fatal catastrophes, root cause analysis often starts with the Five Whys technique. 

But you do not need a catastrophic failure to motivate you to use this method. Anytime you want to understand better the problems that customers or constituents are facing, you can use the method.  

It is quite easy to explain, although much harder for most people to do.  Here is a simple example.

Five Whys is especially useful in thinking about any new product or service you hope to bring into the world.  If you identify the root cause of the problem, you’ll be able to come up with the right solution.  If you identify a solution for the superficial complaint a customer has, you may well end up doing the right thing about the wrong thing.

A famous quote attributed to Henry Ford identifies how you can go astray: “If I had asked people what they wanted, they would have said faster horses.” There were several root causes of the problem that annoyed Ford’s customers, none of which could have been fixed by getting horses to go faster.

As you can see from the 5 Whys picture of a restaurant’s problem, people often think about causes in a linear fashion.  Event A causes Event B, which causes Event C, etc. So all you need to do is go back from where you started, say Event C.  This is sometimes called Event-Oriented thinking.

But life is more complicated than that.  In his book, eventually Dan Heath introduces the necessity of Systems Thinking, since upstream you may well find not a linear series of causes, but a set of interrelated factors.   This picture nicely summarizes the difference.

You may recognize the feeling of being caught in a loop, being in a “Catch-22” situation where you go in circles.  Since Catch-22 was originally about absurdity in wars and not an everyday experience, perhaps this Dilbert cartoon provides a better simple example.

Properly assessing the forces and their mutual reinforcement – in other words, doing systems thinking – is even harder than struggling with the 5 Whys of a simple linear chain of causes.  But it is necessary to really understand the world you are operating in.

Again, especially for those devising new products or services, it is that understanding which will help you avoid significant, strategic business errors.

In business, we are frequently told that to build a successful company we have to find an answer to the customer’s problem.  In government, the equivalent guidance to public officials is to solve the problems faced by constituents.  This is good guidance, as far as it goes, except that we need to know what the problem really is before we can solve it.

Before those of us who are results-oriented, problem solvers jump into action, we need to make sure that we are looking at the right part of the problem.  And that’s what Dan Heath’s new book, “Upstream: The Quest To Solve Problems Before They Happen” is all about.

Heath, along with his brother Chip, has brought us such useful books as “Made To Stick: Why Some Ideas Survive and Others Die” and “Switch: How to Change Things When Change Is Hard”.

As usual for a Heath book, it is well written and down to earth, but contains important concepts and research underneath the accessible writing.

He starts with a horrendous, if memorable, story about kids: 

You and a friend are having a picnic by the side of a river. Suddenly you hear a shout from the direction of the water – a child is drowning. Without thinking, you both dive in, grab the child, and swim to shore. Before you can recover, you hear another child cry for help. You and your friend jump back in the river to rescue her as well. Then another struggling child drifts into sight…and another…and another. The two of you can barely keep up. Suddenly, you see your friend wading out of the water, seeming to leave you alone. “Where are you going?” you demand. Your friend answers, “I’m going upstream to tackle the guy who’s throwing all these kids in the water.”

Going upstream is necessary to solve the problem at its origin – hence the name of the book.  The examples in the book range from important public, governmental problems to the problems of mid-sized businesses.  While the most dramatic examples are about saving lives, the book is also useful for the less dramatic situations in business.

Heath’s theme is strongly, but politely, stated:

“So often we find ourselves reacting to problems, putting out fires, dealing with emergencies. We should shift our attention to preventing them.”

This reminds me of a less delicate reaction to this advice: “When you’re up to your waist in alligators, it’s hard to find time to drain the swamp”.  And I often told my staff that unless you took some time to start draining the swamp, you are always going to be up to your waist in alligators.”

He elaborates and then asks a big question:

We put out fires. We deal with emergencies. We stay downstream, handling one problem after another, but we never make our way upstream to fix the systems that caused the problems. Firefighters extinguish flames in burning buildings, doctors treat patients with chronic illnesses, and call-center reps address customer complaints. But many fires, chronic illnesses, and customer complaints are preventable. So why do our efforts skew so heavily toward reaction rather than prevention?

His answer is that, in part, organizations have been designed to react – what I called some time ago the “inbox-outbox” view of a job.  Get a problem, solve it, and then move to the next problem in the inbox.

Heath identifies three causes that lead people to focus downstream, not upstream where the real problem is.

Problem Blindness – “I don’t see the problem.”

A Lack of Ownership – “The problem isn’t mine to fix.”

Tunneling – “I can’t deal with the problem right now.”

In turn, these three primary causes lead to and are reinforced by a fatalistic attitude that bad things will happen and there is nothing you can do about that.

Ironically, success in fixing a problem downstream is often a mark of heroic achievement.  Perhaps for that reason, people will jump in to own the emergency downstream, but there are fewer owners of the problem upstream.

…reactive efforts succeed when problems happen and they’re fixed. Preventive efforts succeed when nothing happens.  Those who prevent problems get less recognition than those who “save the day” when the problem explodes in everyone’s faces.  

Consider the all too common current retrospective on the Y2K problem.  Since the problem didn’t turn out to be the disaster it could have been at the turn of the year 2000, some people have decided it wasn’t real after all.  It was, but the issue was dealt with upstream by massive correction and replacement of out-of-date software.

Heath realizes that it is not simple for a leader with an upstream orientation to solve the problem there, rather than wait for the disaster downstream.

He asks leaders to first think about seven questions, which explores through many cases:

How will you get early warning of the problem?

How will you unite the right people to assess and solve the problem?

Where can you find a point of leverage?

Who will pay for what does not happen?

How will you change the system?

How will you know you’re succeeding?

How will you avoid doing harm?

Some of these questions and an understanding of what the upstream problem really is can start to be answered by the intelligent use of analytics.  That too only complicates the issue for leaders, since an instinctive heroic reaction is much sexier than contemplating machine learning models and sexy usually beats out wisdom :-)

Eventually Heath makes the argument that not only do we often focus on the wrong end of the problem, but that we think about the problem too simplistically. At that point in his argument, he introduces the necessity of systems thinking because, especially upstream, you may find a set of interrelated factors and not a simple one-way stream. 

[To be continued in the next post.]

A couple of weeks ago, along with the Intelligent Community Forum (ICF) co-founder, Robert Bell, I had the opportunity to be in a two-day discussion with the leaders of Tallinn, Estonia — via Zoom, of course. As part of ICF’s annual selection process for the most intelligent community of the year, the focus was on how and why they became an intelligent community.

They are doing many interesting things with technology both for e-government as well as more generally for the quality of life of their residents. One of their accomplishments, in particular, has laid the foundation for a few others — the strong digital identities (and associated digital signatures) that the Estonian government provides to their citizens. Among other things, this enables paperless city government transactions and interactions, online elections, COVID contact warnings along with protection/tracking of the use of personal data.

Most of the rest of the world, including the US, does not have strong, government-issued digital identities. The substitutes for that don’t come close — showing a driver’s license at a store in the US or using some third party logon.

Digital identities have also enabled an E-Residency program for non-Estonians, now used by more than 70,000 people around the world.

As they describe it, in this “new digital nation … E-Residency enables digital entrepreneurs to start and manage an EU-based company online … [with] a government-issued digital identity and status that provides access to Estonia’s transparent digital business environment”

This has also encouraged local economic growth because, as they say, “E-Residency allows digital entrepreneurs to manage business from anywhere, entirely online … to choose from a variety of trusted service providers that offer easy solutions for remote business administration.” The Tallinn city leaders also attribute the strength of a local innovation and startup ecosystem to this gathering of talent from around the world.

All this would be a great story, unusual in practice, although not unheard of in discussions among technologists — including this one. As impressive as that is, it was not what stood out most strongly in the discussion which was Tallinn’s unconventional perspective on the important issue of trust.

Trust among people is a well-known foundation for society and government in general. It is also essential for those who wish to lead change, especially the kind of changes that result from the innovations we are creating in this century.

I often hear various solutions to the problem of establishing trust through the use of better technology — in other words, the belief that technology can build trust.

In Tallinn’s successful experience with technology, cause-and-effect go more in the opposite direction. In Tallinn, successful technology is built on trust among people that had existed and is continually maintained regardless of technology.

While well-thought out good technology can also enhance trust to an extent, in Tallinn, trust comes first.

This is an important lesson to keep in mind for technologists who are going about changing the world and for government leaders who look on technology as some kind of magic wand.

More than once in our discussions, Tallinn’s leaders restated an old idea that preceded the birth of computers: few things are harder to earn and easier to lose than trust.

Every developing technology has the potential for unintended consequences. Blockchain technology is an example.

Although there are many possible uses of blockchain as a generally trusted and useful distributed approach to storing data, its most visible application has been virtual or crypto-currencies, such as Bitcoin, Ethereum and Litecoin. These once-obscure crypto-currencies are on a collision course with another trend that in its own way is based on technology — mostly digital government-issued money.

Although there are many possible uses of blockchain as a generally trusted and useful distributed approach to storing data, its most visible application has been virtual or crypto-currencies, such as Bitcoin, Ethereum and Litecoin. These once-obscure crypto-currencies are on a collision course with another trend that in its own way is based on technology — mostly digital government-issued money.

In particular, another once-obscure idea about government money is also moving more into the mainstream — modern monetary theory (MMT), which I mentioned few weeks ago in my reference to Stephanie Kelton’s new book, “The Deficit Myth”. In doing a bit of follow up on the subject, I came across many articles that were critical of MMT. Some were from mainstream economists. Many more were from advocates of crypto-currencies, especially Bitcoiners.

Although I doubt that Professor Kelton would agree, many Bitcoiners feel that governments have been using MMT since the 1970s — merely printing money. They forget about the tax and policy stances that Kelton advocates.

Moreover, there is a significant difference in the attitude of public leaders when they think they are printing money versus  borrowing it from large, powerful financial interests. James Carville, chief political strategist and guru for President Clinton famously said,

“I used to think that if there was reincarnation, I wanted to come back as the president or the pope or as a .400 baseball hitter. But now I would like to come back as the bond market. You can intimidate everybody.”

For Bitcoiners, the battle is drawn and they do not like MMT. Here is just a sample of the headlines from the last year or so:

Bitcoin vs. Modern Monetary Theory

Bitcoin, The Alternative to the Modern Monetary Theory

Satoshi’s Rebuttal of Modern Monetary Theory examines the origin of the century-old economic ideology and how bitcoin invalidates many of its core principles, saying, “The decentralized nature of Bitcoin meant that even if the State wanted to shut it down, it would be an incredibly difficult project to kill at a certain scale. This new form of money was able to exist out in the wild, allowing for the first time the possibility of a true competitor to fiat money.”

This more balanced article still portrays a contest of Modern Monetary Theory Versus Bitcoin.

It is worth noting that MMT raises very challenging issues of governance. Who decides how much currency to issue? Who decides when there is too much currency? Who decides what government-issued money is spent on and to whom it goes? This is especially relevant in the US, where the central bank, the Federal Reserve, is at least in theory independent from elected leaders.

However, it also gives the government what may be a necessary tool to keep the economy moving during recessions, especially major downturns. Would a future dominated by cryptocurrencies, like Bitcoin, essentially tie the hands of the government in the face of an economic crisis? — just as the gold standard did during the Panic of 1893 and the Great Depression (until President Roosevelt suspended the convertibility of dollars into gold)?

This picture shows MMT as a faucet controlling the flow of money as the needs of the economy changes. If this were a picture of Bitcoin’s role, the faucet would be almost frozen, dripping a relatively fixed amount that is dependent upon Bitcoin mining.

Less often discussed is that cryptocurrencies, as a practical matter, also end up needing some governance. I am not going to get into the weeds on this, but you can start with “In Defense of Szabo’s Law, For a (Mostly) Non-Legal Crypto System”. The implication is that cryptocurrencies need some kind of rules and laws enforced by some people. Sounds like at least a little bit of government to me.

Putting that aside, if Bitcoin and/or other cryptocurrencies succeed in getting widespread adoption, then it would seem that they would limit the ability of governments to encourage or discourage economic growth through the issuance of money.

Of course, some officials do not seem to worry too much. This attitude is summed up in a European Parliament report, published in 2018.

Decentralised ledger technology has enabled cryptocurrencies to become a new form of money that is privately-issued, digital and that permits peer-to-peer transactions. However, the current volume of transactions in such cryptocurrencies is still too small to make them serious contenders to replace official currencies.

Underlying this are two factors. First, cryptocurrencies do not perform the role of money well, because their value is very volatile and they are thus not very good stores of value. Second, cryptocurrencies are managed in ways that are very primitive compared to what modern currencies require.

These shortcomings might be corrected in the future to increase the popularity and reach of cryptocurrencies. However, those that manage currencies, in other words monetary policymakers, cannot be outside any societal system of checks and balances.

For cryptocurrencies to replace official money, they would have to conform to the institutional set up that monitors and evaluates those who have the power to manage money.

They do not seem to be too worried, do they? However, cryptocurrency might eventually derail the newfound freedom that government economic policy makers have realized they have through MMT. As we have seen in the past, new technologies can suddenly grow very fast and blindside public officials. As Roy Amara, past president of The Institute for the Future, said, “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run”.

I have been doing research about the future impact of artificial intelligence on the economy and the rest of our lives. With that in mind, I have been reading a variety of books by economists, technologists, and others.

That is why I recently read “Capital and Ideology” by Thomas Piketty, the well-known French economist and author of the best-selling (if not well read) “Capital in the Twenty-First Century”. It contains a multi-national history of inequality, why it happened and why it has continued, mostly uninterrupted. At more than 1100 pages, it is a tour de force of economics, history, politics and sociology.

In considerable detail, for every proposition, he provides reasonable data analyses, which is why the book is so long. While there is a lot of additional detail in the book, many of the themes are not new, in part because of Piketty’s previous work.  As with his last book, much of the commentary on the new book is about income and wealth inequality.  This is obviously an important problem, although not one that I will discuss directly here.

Instead, although much of the focus of the book is on capital in the traditional sense of money and ownership of things, it was his two main observations about education – what economists call human capital – that stood out for me. The impact of a second wave and a second kind of capital is two-fold.

1.      Education And The US Economy

From the mid-nineteenth century until about a hundred years later, the American population had twice the educational level of people in Europe. And this was exactly the same period that the American economy surpassed the economies of the leading European countries. During the last several decades, the American population has fallen behind in education and this is the same time that their incomes have stagnated.  It is obviously difficult to tease out the effect of one factor like education, but clearly there is a big hint in these trends.

As Piketty writes in Chapter 11:

The key point here is that America’s educational lead would continue through much of the twentieth century. In 1900–1910, when Europeans were just reaching the point of universal primary schooling, the United States was already well on the way to generalized secondary education. In fact, rates of secondary schooling, defined as the percentage of children ages 12–17 (boys and girls) attending secondary schools, reached 30 percent in 1920, 40–50 percent in the 1930s, and nearly 80 percent in the late 1950s and early 1960s. In other words, by the end of World War II, the United States had come close to universal secondary education.

At the same time, the secondary schooling rate was just 20–30 percent in the United Kingdom and France and 40 percent in Germany. In all three countries, it is not until the 1980s that one finds secondary schooling rates of 80 percent, which the United States had achieved in the early 1960s. In Japan, by contrast, the catch-up was more rapid: the secondary schooling rate attained 60 percent in the 1950s and climbed above 80 percent in the late 1960s and early 1970s.

In the second Industrial Revolution it became essential for growing numbers of workers to be able to read and write and participate in production processes that required basic scientific knowledge, the ability to understand technical manuals, and so on.

That is how, in the period 1880–1960—first the United States and then Germany and Japan, newcomers to the international scene—gradually took the lead over the United Kingdom and France in the new industrial sectors. In the late nineteenth and early twentieth centuries, the United Kingdom and France were too confident of their lead and their superior power to take the full measure of the new educational challenge.

How did the United States, which pioneered universal access to primary and secondary education and which, until the turn of the twentieth century, was significantly more egalitarian than Europe in terms of income and wealth distribution, become the most inegalitarian country in the developed world after 1980—to the point where the very foundations of its previous success are now in danger? We will discover that the country’s educational trajectory—most notably the fact that its entry into the era of higher education was accompanied by a particularly extreme form of educational stratification—played a central role in this change.

In any case, as recently as the 1950s inequality in the United States was close to or below what one found in a country like France, while its productivity (and therefore standard of living) was twice as high. By contrast, in the 2010s, the United States has become much more inegalitarian while its lead in productivity has totally disappeared.

2.     The Political Competition Between Two Elites

By now, most Americans who follow politics understand that the Democratic Party has become the favorite of the educated elite, in addition to the votes from minority groups. This coalition completely reverses what had been true of educated voters in most of the last century, who were reliable Republican voters. In the process, the Democratic Party has lost much of its working-class base.

The Republicans have been the party of the economic elite, although since the 1970s some of the working-class have joined in, especially those reacting to increased immigration and civil rights movements.

What Piketty points out is that, in this transition, working-class and lower income people have decreased their political participation, especially voting. He thinks that is because these voters felt that the Democratic Party has been taken over by the educational elite and no longer speaks for them.

What many Americans may not have realized is that this same phenomenon has happened in other economically advanced democracies, such as the UK and France. Over the longer run, Piketty wonders whether such an electoral competition between parties both dominated by elites can be sustained – or whether the voiceless will seek violence or other undemocratic outlets for their political frustrations.

In Chapter 14, he notes that, at the same time that the USA has lost the edge arising from a better educated population, it and other advanced economies that have now matched or surpassed the American educational level, have elevated education to a position of political power.

We come now to what is surely the most striking evolution in the long run; namely, the transformation of the party of workers into the party of the educated.

Before turning to explanations, it is important to emphasize that the reversal of the educational cleavage is a very general phenomenon. What is more, it is a complete reversal, visible at all levels of the educational hierarchy. we find exactly the same profile—the higher the level of education, the less likely the left-wing vote—in all elections in this period, in survey after survey, without exception, and regardless of the ambient political climate. Specifically, the 1956 profile is repeated in 1958, 1962, 1965, and 1967.

Not until the 1970s and 1980s does the shape of the profile begin to flatten and then gradually reverse. The new norm emerges with greater and greater clarity as we move into the 2000s and 2010s. With the end of Soviet communism and bipolar confrontations over private property, the expansion of educational opportunity, and the rise of the “Brahmin left,” the political-ideological landscape was totally transformed.

Within a few years the platforms of left-wing parties that had advocated nationalization (especially in the United Kingdom and France), much to the dismay of the self-employed, had disappeared without being replaced by any clear alternative.

A dual-elite system emerged, with on one side, a “Brahmin left,” which attracted the votes of the highly educated, and on the other side, a “merchant right,” which continued to win more support from both highly paid and wealthier votes.

This clearly provides some context for what we have been seeing in recent elections.  And although he is not the first to highlight this trend, the evidence that he marshals is impressive.

Considering how much there is in the book, it is not likely anyone, including me, would agree with all of the analysis. In addition to the analysis, Piketty goes on to propose various changes in taxation and laws, which I will discuss in the context of other writers in a later blog. For now, I would only add that other economists have come to some of the same suggestions as Piketty, although they have completed a very different journey from his.

For example, Daniel Susskind in The End Of Work is concerned that a large number of people will not be able to make a living through paid work because of artificial intelligence. The few who do get paid and those who own the robots and AI systems will become even richer at most everyone else becomes poorer. This blends with Piketty’s views and they end up in the same place – a basic citizen’s income and even a basic capital allotment to each citizen, taxation on wealth, estate taxes, and the like.

We will have much to explore about these and other policy issues arising from the byproducts of our technology revolution in this century.

To many people, the current applications of artificial intelligence, like your car being able to detect where a lane ends, seem magical. Although most of the significant advances in AI have been in supervised learning, it is the idea that the computer is making sense of the world on its own — unsupervised — which intrigues people more.

If you’ve read some about artificial intelligence, you may often see a distinction between supervised and unsupervised learning by machine. (There are other categories too, but these are the big two.)

In supervised learning, the machine is taught by humans what is right or wrong — for example, who did or did not default on a loan — and it eventually figures out what characteristics would best predict a default.

Another example is asking the computer to identify whether a picture shows a dog or a cat. In supervised learning, a person identifies each picture and then the computer figures out the best way to distinguish between each — perhaps whether the animal has floppy ears ;-)

Even though the machine gets quite good at correctly doing this, the underlying model of the things that predict these results is also often opaque. Indeed, one of the hot issues in analytics and machine learning these days is how humans can uncover and almost “reverse engineer” the model the machine is using.

In unsupervised learning, the computer has to figure out for itself how to divide a group of pictures or events or whatever into various categories. Then the next step is for the human to figure out what those categories mean. Since it is subject to interpretation, there is no truly accurate and useful way to describe the categories, although people try. That’s how we get psychographic categories in marketing or equivalent labels, like “soccer moms”.

Sometimes the results are easy for humans to figure out, but not exactly earth shattering, like in this cartoon.

In the case of the computer that is given a set of pictures of cats and dogs to determine what might be the distinguishing characteristics, we (people) would hope that computer would figure out that there are dogs and cats. But it might instead classify them based on size — small animals and big animals — or based on the colors of the animals.

This is all sounds like it is unsupervised. Anything useful that the computer determines is thus part of the magic.

How Unsupervised Is Unsupervised Machine Learning?

Except, in some of the techniques of unsupervised learning, especially in cluster analysis, a person is asked to determine how many clusters or groups there might be. This too limits and supervises the learning by the machine. (Think about how much easier it is to be right in Who Wants To Be A Millionaire if the contestant can narrow down the choices to two.)

Even more important, the computer can only learn from the data that it is given. It would have problems if pictures of a bunch of elephants or firetrucks were later thrown into the mix. Thus, the human being is at least partially supervising the learning and certainly limiting it. The machine’s model is subject to the limitations and biases of the data that it learned on.

Truly unsupervised learning would occur the way that it does for children. They are let out to observe the world and learn patterns, often without any direct assistance from anyone else. Even with over-scheduling by helicopter parents, children can often freely roam the earth and discover new data and experiences.

Similarly, to have true unsupervised learning of machines, they would have to be able to travel and process the data they see.

At the beginning of his book Life 3.0, Max Tegmark weaves a sci fi tale about a team that built an AI called Prometheus. While it wasn’t directly focused on unsupervised classification, Prometheus was unsupervised and learned on its own. It eventually learned enough to dominate all mankind. But even in this fantasy world, its unsupervised escape only enabled the AI machine to roam the internet, which is not quite the same thing as real life after all. It is likely, for a while longer, that a significant portion of human behavior will occur outside of the internet :-)

(As we saw with Microsoft’s chatbot Tay, an AI can also learn some unfortunate and incorrect things on the open internet.)

While not quite letting robots roam free in the real world, researchers at Stanford University’s Vision and Learning Lab “have developed iGibson, a realistic, large-scale, and interactive virtual environment within which a robot model can explore, navigate, and perform tasks.” (More about this at A Simulated Playground for Robots)

There was HitchBOT a few years ago which traveled around the US, although I don’t think that it added to its knowledge along the way, and it eventually met up with some nasty humans. (For more see here and here.)

Perhaps self-driving cars or walking robots will eventually be able to see the world freely as we do. Ford Motor Company’s proposed delivery robot roams around, but it is not really equipped for learning. The traveling, learning machine will likely require a lot more computing power and time than we currently use in machine learning.

Of course, there is also work on the computing part of problem, as this July 21st headline shows, “Machines Can Learn Unsupervised ‘At Speed Of Light’ After AI Breakthrough, Scientists Say.” But that’s only the computing part of the problem and not the roaming around the world part.

These more recent projects are evidence that the AI researchers realize their models are not being built in a truly unsupervised way. Despite the hoped-for progress of these projects, for now, that is why data scientists need to be careful how they train and supervise a machine even in unsupervised learning mode.

If you work in a factory or somewhere else that requires you to touch things or people, the COVID shutdowns and social distancing have clearly been a difficult situation to overcome. 

But it seems that the past few months have also been very trying for many people who worked in office settings before COVID set in.  The Brady Bunch meme captured this well.  However, to me, that’s something which is less a reflection of reality than a lack of imagination and experience.

I’m in the minority of folks who have worked remotely for more than ten years.  By now, I’ve forgotten some of the initial hiccups in doing that.  Also, the software, hardware and bandwidth have gotten so much better that the experience is dramatically better than when I started.

So, I’m a little flummoxed by some of what I hear from remote working newbies.  First off, of course, is the complaint that people can’t touch and hug their co-workers anymore.  Haven’t they been to training about inappropriate touching and how some of these physical interactions can come off as harassment?  Even if these folks were in the office, I doubt they would really be going around making physical contact with co-workers.

Then there is the complaint about the how much can be missed in communication when conversations are limited to text messages and emails. That complaint is correct.  But why is there an assumption that communication is limited to text.  If you had a meeting in a conference room or went to someone’s office for a talk, why can’t you do the same thing via videoconference?

(My own experience is that remote work requires video to be successful because of the importance of non-text elements of human communication.  That’s why I’m assuming that the virtual communication is often via video.)

In the office you could drop by.  Users of Zoom and similar programs are often expected to schedule meetings, but that’s not a requirement.  You can turn on Zoom and, just like in an office, others could connect to you when you want.  They’ll see if your busy.  And, if you’re a really important person, you can set up a waiting room and let them in when you’re ready.

There is even a 21st century version of the 19th century partner desks, although it’s not new.  An example is the always-on Kubi, pictured below, that has been around for a few years.

Perch, another startup, summarized the idea in this video a few years back.  Foursquare started using a video portal connecting their engineering teams on the two coasts eight years ago.  (A few months ago before COVID, a deal was reached to merge Foursquare with Factual.)

By the way, the physical office was no utopia of employee interaction.  A variety of studies, most famously the Allen Curve, a very large reduction in interaction if employees were even relatively short physical distances from each other.  With video, all your co-workers are just a click away. While your interactions with the colleague at the next desk may be less (if you want), your interactions with lots of other colleagues on other floors can happen a lot more easily.

And then, despite evidence of increased productivity and employee happiness with remote work, there is the statement that it decreases innovation and collaboration.  

Influential articles, like Workspaces That Move People in the October 2014 issue of the Harvard Business Review, declared that “chance encounters and interactions between knowledge workers improve performance.”  

In the physical world, many companies interpreted this as a mandate for open office plans that removed doors and closed offices.  So how did that work out?  

According to a later article – The Truth About Open Offices – in the November–December 2019 issue of the Harvard Business Review reported that, “when the firms switched to open offices, face-to-face interactions fell by 70%”.    (More detail can be found in Royal Society journal article of  July 2018 on “The impact of the ‘open’ workspace on human collaboration”.

The late Steve Jobs forcefully pushed the idea of serendipity through casual, random encounters of employees.  That idea was one of the design principles of the new Apple headquarters.  Now with COVID-driven remote work, some writers, like Tiernan Ray in ZDNET on June 24, 2020, are asking “Steve Jobs said Silicon Valley needs serendipity, but is it even possible in a Zoom world?”.

There is nothing inherently in video conferencing that diminishes serendipitous meetings.  Indeed, in the non-business world, there are websites that exist solely to connect strangers together completely at random, like Chatroulette and Omegle. 

Without going into the problems those sites have had with inappropriate behavior, the same idea could be used in a different way to periodically connect via video conferencing two employees who otherwise haven’t met recently or at all.  Nor does that have to be completely random.  A company doing this could also use some analytics to determine which employees might be interested in talking with other employees that they haven’t connected with recently.  That would ensure serendipity globally, not just limited to the people who work in the same building.

It's not that video conferencing is perfect, but there is still an underappreciation of how many virtual equivalents there are of typical office activities – and even less appreciation for some of the benefits of virtual connections compared to physical offices.

To me, the issue is one of a lag that I’ve seen before with technology.  I’ve called this horseless carriage thinking.  Sociologists call it a cultural lag.  As Ashley Crossman has written, this is “what happens in a social system when the ideals that regulate life do not keep pace with other changes which are often — but not always — technological.”

Five years ago, I wrote about the possibility of dynamic budgeting.  I was reminded of this again recently after reading Stephanie Kelton’s eye-opening new book, “The Deficit Myth”.  

Her argument is that, since the U.S. dropped the gold standard and fixed exchange rates, it can create as much money as it wants. The limit is not an illusory national debt number, but inflation.  And in an economy with less than full employment, inflation is not now an issue. Her explanation of the capacity of the Federal government to spend leads to her suggestions for a more flexible approach to dealing with major economic and social issues. 

Although Dr. Kelton was the former staff director for the Democrats on the Senate Budget Committee, she doesn’t devote many words to the tools used in budgeting.  However, the argument that she makes reminds me again that the traditional budget itself has to change, especially shifting to a dynamic budget.

While states and localities are not in the same position as the Federal government, they also face unpredictable conditions and could benefit from a more flexible, dynamic budget.  Of course, in the face of COVID and economic retraction the necessity of re-allocating funds has become more obvious.

In an earlier blog, I wrote about a simple tax app that is now feasible and also eliminates the bumps in incentives that are caused by our current, old-fashioned tax bracket scheme.   This was not using some untested, cutting-edge technology.  Instead, the solution could use phones, tablets and laptops doing simple calculations that these devices have done for decades.

Similarly, what is now well-established technology could be used to overcome the problems with traditional fixed budgeting.  (By the way, the same applies to the budgets that corporations devise.)

So, what are the problems that everyone knows exist with budgets?

1.       They’re wrong the day they are approved since they are trying to predict precisely a future that cannot be known precisely ahead of time.  This error is made worse by the early deadlines in the typical budget process. If you run a department, you are likely to be asked by the budget office to prepare estimates for what you’ll need in a period that will go as far as 18 or even 24 months into the future.

2.      It’s not clear how the estimates are derived. Typically, there are no underlying rules or models, just the addition of personnel and other basic costs that are adjusted from the last year.  This is despite the fact that some things are fairly well known.  For example, it is fairly straightforward to estimate the cost of paying unemployment to an average individual.  What is harder is to figure out how many unemployed people there will be – and, of course, you need to know the total number of unemployed and the average cost in order to compute the total amount of money needed.

3.      Given these problems, in practice during any given budget year, all kinds of exceptions and deviations occur in the face of reality.  But the rest of the budget is not readjusted, although the budget staff will often hold back money that was approved as it takes from “Peter to pay Paul”.  The process often seems and is very arbitrary.

Operating in the real world, of course, requires continual adjustments.  Such adjustments can best be accommodated if the traditional fixed budget was replaced by a dynamic budget at the start of the budget process. 

One way of doing this is familiar to almost every reader of this blog – the spreadsheet.  The cells in spreadsheets don’t always have hard fixed numbers, like fixed budgets.  Instead many of those spreadsheets have formulas. 

And Congress could also not so much the individual amounts for each agency or program, but their relative priorities under different scenarios.  Thus, in a recession there would be a need for more unemployment insurance funding, but that would recede in the face of other priorities if the economy is booming.

To go back to the unemployment example, the actual amount needed in the budget will change as we get closer to the month being estimated and can be more accurate in its estimates of the number of people who will be unemployed. 

Of course, the reader who knows my background won’t be surprised that I think the formulas in these cells could be derived by the use of some smart analytics and machine learning.  Ultimately, these methods could be enhanced with simulations – after all, what is a budget but an attempt to simulate a future period of financial needs?

More on that in another post sometime in the future.