Interesting Books In 2020

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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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!

© 2020 Norman Jacknis, All Rights Reserved

Words Matter In Building Intelligent Communities

The Intelligent Community Forum (ICF) is an international group of city, town and regional leaders as well as scholars and other experts who are focused on quality of life for residents and intelligently responding to the challenges and opportunities provided by a world and an economy that is increasingly based on broadband and technology.

To quote from their website: “The Intelligent Community Forum is a global network of cities and regions with a think tank at its center.  Its mission is to help communities in the digital age find a new path to economic development and community growth – one that creates inclusive prosperity, tackles social challenges, and enriches quality of life.”

Since 1999, ICF has held an annual contest and announced an award to intelligent communities that go through an extensive investigation and comparison to see how well they are achieving these goals.  Of hundreds of applications, some are selected for an initial, more in-depth assessment and become semi-finalists in a group called the Smart21.

Then the Smart21 are culled to a smaller list of the Top7 most intelligent communities in the world each year.  There are rigorous quantitative evaluations conducted by an outside consultancy, field trips, a review by an independent panel of leading experts/academic researchers and a vote by a larger group of experts.

An especially important part of the selection of the Top7 from the Smart21 is an independent panel’s assessment of the projects and initiatives that justify a community’s claim to being intelligent.

It may not always be clear to communities what separates these seven most intelligent communities from the rest.  After all, these descriptions are just words.  We understand that words matter in political campaigns.  But words matter outside of politics in initiatives, big and small, that are part of governing.

Could the words that leaders use be part of what separates successful intelligent initiatives from those of others who are less successful in building intelligent communities?

In an attempt to answer that question, I obtained and analyzed the applications submitted over the last ten years.  Then, using the methods of analytics and machine learning that I teach at Columbia University, I sought to determine if there was a difference in how the leaders of the Top7 described what they were doing in comparison with those who did not make the cut.

Although at a superficial level, the descriptions seem somewhat similar, it turns out that the leaders of more successful intelligent community initiatives did, indeed, describe those initiatives differently from the leaders of less successful initiatives.

The first significant difference was that the descriptions of the Top7 had more to say about their initiatives, since apparently they had more accomplishments to discuss.  Their descriptions had less talk about future plans and more about past successes.

In describing the results of their initiatives so far, they used numbers more often, providing greater evidence of those results.  Even though they were discussing technology-based or otherwise sometimes complex projects, they used more informal, less dense and less bureaucratic language.

Among the topics they emphasized, engagement and leadership as well as the technology infrastructure primarily stood out.  Less important, but also a differentiation, the more successful leaders emphasized the smart city, innovation and economic growth benefits.

For those leaders who wish to know what will gain them recognition for real successes in transforming their jurisdictions into intelligent communities, the results would indicate these simple rules:

  • Have and highlight a solid technology infrastructure.
  • True success, however, comes from extensive civic engagement and frequently mentioning that engagement and the role of civic leadership in moving the community forward.
  • Less bureaucratic formality and more stress on results (quantitative measures of outcomes) in their public statements is also associated with greater success in these initiatives.

On the other hand, a laundry list of projects that are not tied to civic engagement and necessary technology, particularly if those projects have no real track record, is not the path to outstanding success – even if they check off the six wide-ranging factors that the ICF expects of intelligent communities.

While words do matter, it is also true that other factors can impact the success or failure of major public initiatives.  However, these too can be added into the models of success or failure, along with the results of the textual analytics.

Overall, the results of this analysis can help public officials understand a little better how they need to think about what they are doing and then properly describe it to their citizens and others outside of their community.  This will help them to be more successful, most importantly for their communities and, if they wish, as well in the ICF awards process.

© 2020 Norman Jacknis, All Rights Reserved

Trump And Cuomo COVID-19 Press Conferences

Like many other people who have been watching the COVID-19 press conferences held by Trump and Cuomo, I came away with a very different feeling from each.  Beyond the obvious policy and partisan differences, I felt there is something more going on.

Coincidentally, I’ve been doing some research on text analytics/natural language processing on a different topic.  So, I decided to use these same research tools on the transcripts of their press conferences from April 9 through April 16, 2020.  (Thank you to the folks at Rev.com for making available these transcripts.)

One of the best approaches is known by its initials, LIWC, and was created some time ago by Pennebaker and colleagues to assess especially the psycho-social dimensions of texts.   It’s worth noting that this assessment is based purely on the text – their words – and doesn’t include non-verbal communications, like body language.

While there were some unsurprising results to people familiar with both Trump and Cuomo, there are also some interesting nuances in the words they used.

Here are the most significant contrasts:

  • The most dramatic distinction between the two had to do with emotional tone. Trump’s words had almost twice the emotional content of Cuomo’s, including words like “nice”, although maybe the use of that word maybe should not be taken at face value.
  • Trump also spoke of rewards/benefits and money about 50% more often than Cuomo.
  • Trump emphasized allies and friends about twenty percent more often than Cuomo.
  • Cuomo used words that evoked health, anxiety/pain, home and family two to three times more often than Trump.
  • Cuomo asked more than twice as many questions, although some of these could be sort of rhetorical – like “what do you think?”
  • However, Trump was 50% more tentative in his declarations than Cuomo, whereas Cuomo had greater expressions of certainty than Trump.
  • While both men spoke about the present tense much more than the future, Cuomo’s use of the present was greater than Trump’s. On the other hand, Trump’s use of the future tense and the past tense was greater than Cuomo’s.
  • Trump used “we” a little more often than Cuomo and much more than he used “you”. Cuomo used “you” between two and three times more often than Trump.  Trump’s use of “they” even surpassed his use of you.

Distinctions of this kind are never crystal clear, even with sophisticated text analytics and machine learning algorithms.  The ambiguity of human speech is not just a problem for machines, but also for people communicating with each other.

But these comparisons from text analytics do provide some semantic evidence for the comments by non-partisan observers that Cuomo seems more in command.  This may be because the features of his talks would seem to better fit the movie portrayal and the average American’s idea of leadership in a crisis – calm, compassionate, focused on the task at hand.

© 2020 Norman Jacknis, All Rights Reserved