How to Train Your Corporate Rebel

Tebel Talent Cover
Rebel Talent by Francesca Gino makes the case for encouraging individualism in the workplace

13 March 2019 – Francesca Gino, author of Rebel Talent: Why It Pays to Break the Rules at Work and In Life, is my kind of girl. She’s smart, thinks for herself, isn’t afraid to go out on a limb, and encourages others to do the same.

That said, I want to inject a note of caution for anyone considering her advice about being a rebel. There’s an old saying: “The nail that sticks up the most is the first to get hammered down.” It’s true in carpentry and in life. Being a rebel is lonely, dangerous, and is no guarantee of success, financial or otherwise.

I speak from experience, having broken every rule available for as long as I can remember. When I was a child in the 1950s, I wanted to grow up to be a beatnik. I’ve always felt most comfortable amongst bohemians. My wife once complained (while we were sitting in a muscle car stopped by the highway waiting for the cop to give me a speeding ticket) about my “always living on the edge.” And, yes, I’ve been thrown out of more than one bar.

On the other hand, I’ve lived a long and eventful life. Most of the items on my bucket list were checked off long ago.

So, when I ran across an ad in The Wall Street Journal for Gino’s book, I had to snag a copy and read it.

As I expected, the book’s theme is best summed up by a line from the blurb on its dust jacket: “ … the most successful among us break the rules.”

The book description goes on to say, “Rebels have a bad reputation. We think of them as trouble-makers. outcasts, contrarians: those colleagues, friends, and family members who complicate seemingly straight-forward decisions, create chaos, and disagree when everyone else is in agreement. But in truth, rebels are also those among us who change the world for the better with their unconventional outlooks. Instead of clinging to what is safe and familiar, and falling back on routines and tradition, rebels defy the status quo. They are masters of innovation and reinvention, and they have a lot to teach us.”

Considering the third paragraph above, I hope she’s right!

The 283-page (including notes and index) volume summarizes Gino’s decade-long study of rebels at organizations around the world, from high-end boutiques in Italy’s fashion capital (Milan), to the world’s best restaurant (Three-Michelin-star-rated Osteria Francescana), to a thriving fast-food chain (Pal’s), and an award-winning computer animation studio (Pixar).

Francesca Gino is a behavioral scientist and professor at Harvard Business School. She is the Tandon Family Professor of Business Administration in the school’s Negotiation, Organizations & Markets Unit. No slouch professionally, she has been honored as one of the world’s top 40 business professors under 40 by Poets & Quants and one of the world’s 50 most influential management thinkers by Thinkers50.

Enough with the “In Praise Of” stuff, though. Let’s look inside the book. It’s divided into eight chapters, starting with “Napoleon and the Hoodie: The Paradox of Rebel Status,” and ending with “Blackbeard, ‘Flatness,’ and the 8 Principles of Rebel Leadership.” Gino then adds a “Conclusion” telling the story of Risotto Cacio e Pepe (a rice-in-Parmigiano-Reggiano dish invented by Chef Massimo Bottura), and an “Epilogue: Rebel Action” giving advice on releasing your inner rebel.

Stylistically, the narrative uses the classic “Harvard Case Study” approach. That is, it’s basically a pile of stories, each of which makes a point about how rebel leaders Gino has known approach their work. In summary, the take-home lesson is that those leaders encourage their employees to unleash their “inner rebel,” thereby unlocking creativity, enthusiasm, and productivity that more traditional management styles suppress.

The downside of this style is that it sometimes is difficult for the reader to get their brain around the points that Gino is making. Luckily, her narrative style is interesting, easy to follow and compelling. Like all well-written prose she keeps the reader wondering “What happens next?” The episodes she presents are invariably unusual and interesting themselves. She regularly brings in her own exploits and keeps, as much as possible, to first-person active voice.

That is unusual for academic writers, who find it all too easy to slip into a pedantic third-person, passive-voice best reserved for works intended as sleep aids.

To give you a feel for what reading an HCS-style volume is like, I’ll describe what it’s like to study Quantum Dynamics. While the differences outnumber the similarities, the overall “feel” is similar.

The first impression students get of QD is that the subject is entirely anti-intuitive. That is, before you can learn anything about QD, you have to discard any lingering intuition about how the Universe works. That’s probably easier for someone who never learned Classical Physics in the first place. Ideas like “you can’t be in two places at the same time” simply do not apply in the quantum world.

Basically, to learn QD, you have to start with a generous dose of “willing suspension of disbelief.” You do that by studying stories about experiments performed in the late nineteenth century that simply didn’t work. At that time, the best minds in Physics spent careers banging their heads into walls as Mommy Nature refused to return results that Classical Physics imagined she had to. Things like the Michelson-Moreley experiment (and many other then-state-of-the-art experiments) gave results at odds with Classical Physics. There were enough of these screwy results that physicists began to doubt that what they believed to be true, was actually how the Universe worked. After listening to enough of these stories, you begins to doubt your own intuition.

Then, you learn to trust the mathematics that will be your only guide in QD Wonderland.

Finally, you spend a couple of years learning about a new set of ideas based on Through the Looking Glass concepts that stand normal intuition on its head. Piling up stories about all these counter-intuitive ideas helps you build up a new intuition about what happens in the quantum world. About that time, you start feeling confident that this new intuition helps you predict what will happen next.

The HCS style of learning does something similar, although usually not as extreme. Reading story after story about what hasn’t and what has worked for others in the business world, you begin to develop an intuition for applying the new ideas. You gain confidence that, in any given situation, you can predict what happens next.

What happens next is that when you apply the methods Gino advocates, you start building a more diverse corporate culture that attracts and retains the kinds of folks that make your company a leader in its field.

There’s an old one-line joke:

I want to be different – like everybody else.”

We can’t all be different because then there wouldn’t be any sameness to be different from, but we can all be rebels. We can all follow the

  1. READY!
  2. AIM!
  3. FIRE!

mantra advocated by firearms instructors everywhere.

In other words:

  1. Observe what’s going on out there in the world, then
  2. Think about what you might do that breaks the established rules, and, finally,
  3. Act in a way that makes the Universe a better place in which to live.

What is This “Robot” Thing, Anyway?

Robot thinking
So, what is it that makes a robot a robot? Phonlamai Photo/Shutterstock

6 March 2019 – While surfing the Internet this morning, in a valiant effort to put off actually getting down to business grading that pile of lab reports that I should have graded a couple of days ago, I ran across this posting I wrote in 2013 for Packaging Digest.

Surprisingly, it still seems relevant today, and on a subject that I haven’t treated in this blog, yet. It being that I’m planning to devote most of next week to preparing my 2018 tax return, I decided to save some writing time by dusting it off and presenting it as this week’s posting to Tech Trends. I hope the folks at Packaging Digest won’t get their noses too far out of joint about my encroaching on their five-year-old copyright without asking permission.

By the way, this piece is way shorter than the usual Tech Trends essay because of the specifications for that Packaging Digest blog, which was entitled “New Metropolis” in homage to Fritz Lang’s 1927 feature film entitled Metropolis, which told the story of a futuristic mechanized culture and an anthropomorphic robot that a mad scientist creates to bring it down. The “New Metropolis” postings were specified to be approximately 500 words long, whereas Tech Trends postings are planned to be 1,000-1,500 words long.

Anyway, I hope you enjoy this little slice of recent history.


11 November 2013 – I thought it might be fun—and maybe even useful—to catalog the classifications of these things we call robots.

Lets start with the word robot. The idea behind the word robot grows from the ancient concept of the golem. A golem was an artificial person created by people.

Frankly, the idea of a golem scared the bejeezus out of the ancients because the golem stands at the interface between living and non-living things. In our enlightened age, it still scares the bejeezus out of people!

If we restricted the field to golems—strictly humanoid robots, or androids—we wouldnt have a lot to talk about, and practically nothing to do. The things havent proved particularly useful. So, I submit that we should expand the robot definition to include all kinds of human-made artificial critters.

This has, of course, already been done by everyone working in the field. The SCARA (selective compliance assembly robot arm) machines from companies like Kuka, and the delta robots from Adept Technologies clearly insist on this expanded definition. Mobile robots, such as the Roomba from iRobot push the boundary in another direction. Weird little things like the robotic insects and worms so popular with academics these days push in a third direction.

Considering the foregoing, the first observation is that the line between robot and non-robot is fuzzy. The old 50s-era dumb thermostats probably shouldnt be considered robots, but a smart, computer-controlled house moving in the direction of the Jarvis character in the Ironman series probably should. Things in between are – in between. Lets bite the bullet and admit were dealing with fuzzy-logic categories, and then move on.

Okay, so what are the main characteristics symptomatic of this fuzzy category robot?

First, its gotta be artificial. A cloned sheep is not a robot. Even designer germs are non-robots.
Second, its gotta be automated. A fly-by-wire fighter jet is not a robot. A drone linked at the hip to a human pilot is not a robot. A driverless car, on the other hand, is a robot. (Either that, or its a traffic accident waiting to happen.)

Third, its gotta interact with the environment. A general-purpose computer sitting there thinking computer-like thoughts is not a robot. A SCARA unit assembling a car is. I submit that an automated bill-paying system arguing through the telephone with my wife over how much to take out of her checkbook this month is a robot.

More problematic is a fourth direction—embedded systems, like automated houses—that beg to be admitted into the robotic fold. I vote for letting them in, along with artificial intelligence (AI) systems, like the robot bill paying systems my wife is so fond of arguing with.

Finally (maybe), its gotta be independent. To be a robot, the thing has to take basic instruction from a human, then go off on its onesies to do the deed. Ideally, you should be able to do something like say, Go wash the car, and itll run off as fast as its little robotic legs can carry it to wash the car. More chronistically, you should be able to program it to vacuum the living room at 4:00 a.m., then be able to wake up at 6:00 a.m. to a freshly vacuumed living room.

Farsighted Decisions

"Farsighted" book cover
Farsighted: How We Make the Decisions That Matter the Most by Steven Johnson

30 January 2019 – This is not a textbook on decision making.

Farsighted: How We Make the Decisions That Matter the Most does cover most of the elements of state-of-the-art decision making, but it’s not a true textbook. If he’d really wanted to write a textbook, its author, Steven Johnson, would have structured it differently, and would have included exercises for the student. Perhaps he would also have done other things differently that I’m not going to enumerate because I don’t want to write a textbook on state-of-the-art decision making, either.

What Johnson apparently wanted to do, and did do successfully, was lay down a set of principles today’s decision makers would do well to follow.

Something he would have left out, if he were writing a textbook, was the impassioned plea for educators to incorporate mandatory decision making courses into secondary-school curricula. I can’t disagree with this sentiment!

A little bit about my background with regard to decision-theory education: ‘Way back in the early 2010s, I taught a course at a technical college entitled “Problem Solving Theory.” Johnson’s book did not exist then, and I wish that it had. The educational materials available at the time fell woefully short. They were, at best, pedantic.

I spent a lot of class time waving my hands and telling stories from my days as a project manager. Unfortunately, the decision-making techniques I learned about in MBA school weren’t of any help at all. Some of the research Johnson weaves into his narrative hadn’t even been done back then!

So, when I heard about Johnson’s new book, I rushed out to snag a copy and devoured it.

As Johnson points out, everybody is a decision maker every day. These decisions run the gamut from snap decisions that people have to make almost instantly, to long-term deliberate choices that reverberate through the rest of their lives. Many, if not most, people face making decisions affecting others, from children to spouses, siblings and friends. Some of us participate in group decision making that can have truly global ramifications.

In John McTiernan’s 1990 film The Hunt for Red October, Admiral Josh Painter points out to CIA analyst Jack Ryan: “Russians don’t take a dump, son, without a plan. Senior captains don’t start something this dangerous without having thought the matter through.”

It’s not just Russians, however, who plan out even minor actions. And, senior captains aren’t the only ones who don’t start things without having thought the matter through. We all do it.

As Johnson points out, it may be the defining characteristic of the human species, which he likes to call Homo prospectus for their ability to apply foresight to advance planning.

The problem, of course, is the alarming rate at which we screw it up. As John F. Kennedy’s failure in the Bay of Pigs invasion shows, even highly intelligent, highly educated and experienced leaders can get it disastrously wrong. Johnson devotes considerable space to enumerating the taxonomy of “things that can go wrong.”

So, decision making isn’t just for leaders, and it’s easier to get it wrong than to do it right.

Enumerating the ways it can all go disastrously wrong, and setting out principles that will help us get it right are the basic objectives Johnson set out for himself when he first decided to write this book. To wit, three goals:

  • Convince readers that it’s important;

  • Warn folks of how easily it can be done wrong; and

  • Give folks a prescription for doing it right.

Pursuant to the third goal, Johnson breaks decision making down into a process involving three steps:

Mapping consists of gathering preliminary information about the state of the Universe before any action has been taken. What do we have to work with? What options do we have to select from? What do we want to accomplish and when?

Predicting consists of prognisticating, for each of the various options available, how the Universe will evolve from now into the foreseeable (and possibly unforeseeable) future. This is probably the most fraught stage of the process. Do we need a Plan B in case of surprises? As Sean Connery’s “Mac” character intones in Jon Amiel’s 1999 crime drama, Entrapment: “Trust me, there always are surprises.”

Deciding is the ultimate finish of the process. It consists of finally choosing between the previously identified alternatives based on the predicted results. What alternative is most likely to give us a result we want to have?

An important technique Johnson recommends basing your decision-making strategy on is narrative. That explicitly means storytelling. Johnson supplies numerous examples from both fiction and non-fiction that help us understand the decision-making process and help us apply it to the problems we face.

He points out that double-blind clinical trials were the single most important technique that advanced medicine from quackery and the witch-doctor’s art to reliable medical science. It allowed trying out various versions of medical interventions in a systematic way and comparing the results. In the same way, he says, fictional storytelling, allows us to mentally “try out” multiple alternative versions of future history.

Through storytelling, we explore various possibilities and imagine how they might turn out, including the vicissitudes of Shakespeare’s “slings and arrows of outrageous fortune,” without putting in the time and effort to try them out in reality, and thereby likely suffering “the fuss of mass destruction and death.”

Johnson suggests that’s why humans evolved the desire and capacity to create such fictional narratives in the first place. “When we read these novels,” he says, “ … we are not just entertaining ourselves; we are also rehearsing for our own real-world experiences.”

Of course, while “deciding” is the ultimate act of Johnson’s process, it’s never the end of the story in real life. What to do when it all goes disastrously wrong is always an important consideration. Johnson actually covers that as an important part of the “predicting” step. That’s when you should develop Mac’s “Plan B pack” and figure out when to trigger it if necessary.

Another important consideration, which I covered extensively in my problem solving course and Johnson starts looking at ‘way back in “mapping” is how to live with the aftermath of your decision, whether it’s a resounding success or a disastrous failure. Either way, the Universe is changed forever by your decision, and you and everyone else will have to live in it.

So, your ultimate goal should be deciding how to make the Universe a better place in which to live!

Why Diversity Rules

Diverse friends
A diverse group of people with different ages and nationalities having fun together. Rawpixel/Shutterstock

23 January 2019 – Last week two concepts reared their ugly heads that I’ve been banging on about for years. They’re closely intertwined, so it’s worthwhile to spend a little blog space discussing why they fit so tightly together.

Diversity is Good

The first idea is that diversity is good. It’s good in almost every human pursuit. I’m particularly sensitive to this, being someone who grew up with the idea that rugged individualism was the highest ideal.

Diversity, of course, is incompatible with individualism. Individualism is the cult of the one. “One” cannot logically be diverse. Diversity is a property of groups, and groups by definition consist of more than one.

Okay, set theory admits of groups with one or even no members, but those groups have a diversity “score” (Gini–Simpson index) of zero. To have any diversity at all, your group has to have at absolute minimum two members. The more the merrier (or diversitier).

The idea that diversity is good came up in a couple of contexts over the past week.

First, I’m reading a book entitled Farsighted: How We Make the Decisions That Matter the Most by Steven Johnson, which I plan eventually to review in this blog. Part of the advice Johnson offers is that groups make better decisions when their membership is diverse. How they are diverse is less important than the extent to which they are diverse. In other words, this is a case where quantity is more important than quality.

Second, I divided my physics-lab students into groups to perform their first experiment. We break students into groups to prepare them for working in teams after graduation. Unlike when I was a student fifty years ago, activity in scientific research and technology development is always done in teams.

When I was a student, research was (supposedly) done by individuals working largely in isolation. I believe it was Willard Gibbs (I have no reliable reference for this quote) who said: “An experimental physicist must be a professional scientist and an amateur everything else.”

By this he meant that building a successful physics experiment requires the experimenter to apply so many diverse skills that it is impossible to have professional mastery of all of them. He (or she) must have an amateur’s ability pick up novel skills in order to reach the next goal in their research. They must be ready to work outside their normal comfort zone.

That asked a lot from an experimental researcher! Individuals who could do that were few and far between.

Today, the fast pace of technological development has reduced that pool of qualified individuals essentially to zero. It certainly is too small to maintain the pace society expects of the engineering and scientific communities.

Tolkien’s “unimaginable hand and mind of Feanor” puttering around alone in his personal workshop crafting magical things is unimaginable today. Marlowe’s Dr. Faustus character, who had mastered all earthly knowledge, is now laughable. No one person is capable of making a major contribution to today’s technology on their own.

The solution is to perform the work of technological research and development in teams with diverse skill sets.

In the sciences, theoreticians with strong mathematical backgrounds partner with engineers capable of designing machines to test the theories, and technicians with the skills needed to fabricate the machines and make them work.

Chaotic Universe

The second idea I want to deal with in this essay is that we live in a chaotic Universe.

Chaos is a property of complex systems. These are systems consisting of many interacting moving parts that show predictable behavior on short time scales, but eventually foil the most diligent attempts at long-term prognostication.

A pendulum, by contrast, is a simple system consisting of, basically, three moving parts: a massive weight, or “pendulum bob,” that hangs by a rod or string (the arm) from a fixed support. Simple systems usually do not exhibit chaotic behavior.

The solar system, consisting of a huge, massive star (the Sun), eight major planets and a host of minor planets, is decidedly not a simple system. Its behavior is borderline chaotic. I say “borderline” because the solar system seems well behaved on short time scales (e.g., millennia), but when viewed on time scales of millions of years does all sorts of unpredictable things.

For example, approximately four and a half billion years ago (a few tens of millions of years after the system’s initial formation) a Mars-sized planet collided with Earth, spalling off a mass of material that coalesced to form the Moon, then ricochetted out of the solar system. That’s the sort of unpredictable event that happens in a chaotic system if you wait long enough.

The U.S. economy, consisting of millions of interacting individuals and companies, is wildly chaotic, which is why no investment strategy has ever been found to work reliably over a long time.

Putting It Together

The way these two ideas (diversity is good, and we live in a chaotic Universe) work together is that collaborating in diverse groups is the only way to successfully navigate life in a chaotic Universe.

An individual human being is so powerless that attempting anything but the smallest task is beyond his or her capacity. The only way to do anything of significance is to collaborate with others in a diverse team.

In the late 1980s my wife and I decided to build a house. To begin with, we had to decide where to build the house. That required weeks of collaboration (by our little team of two) to combine our experiences of different communities in the area where we were living, develop scenarios of what life might be like living in each community, and finally agree on which we might like the best. Then we had to find an architect to work with our growing team to design the building. Then we had to negotiate with banks for construction loans, bridge loans, and ultimate mortgage financing. Our architect recommended adding a prime contractor who had connections with carpenters, plumbers, electricians and so forth to actually complete the work. The better part of a year later, we had our dream house.

There’s no way I could have managed even that little project – building one house – entirely on my own!

In 2015, I ran across the opportunity to produce a short film for a film festival. I knew how to write a script, run a video camera, sew a costume, act a part, do the editing, and so forth. In short, I had all the skills needed to make that 30-minute film.

Did that mean I could make it all by my onesies? Nope! By the time the thing was completed, the list of cast and crew counted over a dozen people, each with their own job in the production.

By now, I think I’ve made my point. The take-home lesson of this essay is that if you want to accomplish anything in this chaotic Universe, start by assembling a diverse team, and the more diverse, the better!

The Scientific Method

Scientific Method Diagram
The scientific method assumes uncertainty.

9 January 2019 – This week I start a new part-time position on the faculty at Florida Gulf Coast University teaching two sections of General Physics laboratory. In preparation, I dusted off a posting to this blog from last Summer that details my take on the scientific method, which I re-edited to present to my students. I thought readers of this blog might profit by my posting the edited version. The original posting contrasted the scientific method of getting at the truth with the method used in the legal profession. Since I’ve been banging on about astrophysics and climate science, specifically, I thought it would be helpful to zero in again on how scientists figure out what’s really going on in the world at large. How do we know what we think we know?


While high-school curricula like to teach the scientific method as a simple step-by-step program, the reality is very much more complicated. The version they teach you in high school is a procedure consisting of five to seven steps, which pretty much look like this:

  1. Observation
  2. Hypothesis
  3. Prediction
  4. Experimentation
  5. Analysis
  6. Repeat

I’ll start by explaining how this program is supposed to work, then look at why it doesn’t actually work that way. It has to do with why the concept is so fuzzy that it’s not really clear how many steps should be included.

The Stepwise Program

It all starts with observation of things that go on in the World.

Newton’s law of universal gravitation started with the observation that when left on their own, most things fall down. That’s Newton’s falling-apple observation. Generally, the observation is so common that it takes a genius to ask the question: “why?”

Once you ask the question “why,” the next thing that happens is that your so-called genius comes up with some cockamamie explanation, called an “hypothesis.” In fact, there are usually several possible explanations that vary from the erudite to the thoroughly bizarre.

Through bitter experience, scientists have come to realize that no hypothesis is too whacko to be considered. It’s often the most outlandish hypothesis that proves to be right!

For example, the ancients tended to think in terms of objects somehow “wanting” to go downward as the least weird of explanations for gravity. The idea came from animism, which was the not-too-bizarre (to the ancients) idea that natural objects each have their own spirits, which animate their behavior: Rocks are hard because their spirits resist being broken; They fall down when released because their spirits somehow like down better than up.

What we now consider the most-correctest explanation (that we live in a four-dimensional space-time contiuum that is warped by concentrations of matter-energy so that objects follow paths that tend to converge with each other) wouldn’t have made any sense at all to the ancients. It, in fact, doesn’t make a whole lot of sense to anybody who hasn’t spent years immersing themselves in the subject of Einstein’s General Theory of Relativity.

Scientists then take all the hypotheses available, and use them to make predictions as to what happens next if you set up certain relevant situations, called “experiments.” An hypothesis works if its predictions match up with what Mommy Nature produces for results from the experiments.

Scientists then do tons of experiments testing different predictions of the hypotheses, then compare (the analysis step) the results, and eventually develop a warm, fuzzy feeling that one hypothesis does a better job of predicting what Mommy Nature does than do the others.

It’s important to remember that no scientist worth his or her salt believes that the currently accepted hypothesis is actually in any absolute sense “correct.” It’s just the best explanation among the ones we have on hand now.

That’s why the last step is to repeat the entire process ad nauseam.

While this long, drawn out process does manage to cover the main features of the scientific method, it fails in one important respect: it doesn’t boil the method down to its essentials.

Not boiling the method down to its essentials forces one to deal with all kinds of exceptions created by the extraneous, non-essential bits. There end up being more exceptions than rules. For example, the science-pedagogy website Science Buddies ends up throwing its hands in the air by saying: “In fact, there are probably as many versions of the scientific method as there are scientists!”

A More Holistic Approach

The much simpler explanation I’ve used for years to teach college students about the scientific method follows the diagram above. The pattern is quite simple, with only four components. It starts by setting up a set of initial conditions, and following two complementary paths through to the resultant results.

There are two ways to get from the initial conditions to the results. The first is to just set the whole thing up, and let Mommy Nature do her thing. The second is to think through your hypothesis (the model) to predict what it says Mommy Nature will come up with. If they match, you count your hypothesis as a success. If not, it’s wrong.

If you do that a bazillion times in a bazillion different ways, a really successful hypothesis (like General Relativity) will turn out right pretty much all of the time.

Generally, if you’ve got a really good hypothesis but your experiment doesn’t work out right, you’ve screwed up somewhere. That means what you actually set up as the initial conditions wasn’t what you thought you were setting up. So, Mommy Nature (who’s always right) doesn’t give you the result you thought you should get.

For example, I was once (at a University other than this one) asked to mentor another faculty member who was having trouble building an experiment to demonstrate what he thought was an exception to Newton’s Second Law of Motion. It was based on a classic experiment called “Atwood’s Machine.” He couldn’t get the machine to give the results he was convinced he should get.

I immediately recognized that he’d made a common mistake novice physicists often make. I tried to explain it to him, but he refused to believe me. Then, I left the room.

I walked away because, despite his conviction, Mommy Nature wasn’t going to do what he expected her to. He persisted in believing that there was something wrong with his experimental apparatus. It was his hypothesis, instead.

Anyway, the way this method works is that you look for patterns in what Mommy Nature does. Your hypothesis is just a description of some part of Mommy Nature’s pattern. Scientific geniuses are folks who are really, really good at recognizing the patterns Mommy Nature uses.

If your scientific hypothesis is wrong (meaning it gives wrong results), “So, What?”

Most scientific hypotheses are wrong! They’re supposed to be wrong most of the time.

Finding that some hypothesis is wrong is no big deal. It just means it was a dumb idea, and you don’t have to bother thinking about that dumb idea anymore.

Alien abductions get relegated to entertainment for the entertainment starved. Real scientists can go on to think about something else, like the kinds of conditions leading to development of living organisms and why we don’t see alien visitors walking down Fifth Avenue.

(FYI: the current leading hypothesis is that the distances from there to here are so vast that anybody smart enough to figure out how to make the trip has better things to do.)

For scientists “Gee, it looks like … ” is usually as good as it gets!

Reimagining Our Tomorrows

Cover Image
Utopia with a twist.

19 December 2018 – I generally don’t buy into utopias.

Utopias are intended as descriptions of a paradise. They’re supposed to be a paradise for everybody, and they’re supposed to be filled with happy people committed to living in their city (utopias are invariably built around descriptions of cities), which they imagine to be the best of all possible cities located in the best of all possible worlds.

Unfortunately, however, utopia stories are written by individual authors, and they’d only be a paradise for that particular author. If the author is persuasive enough, the story will win over a following of disciples, who will praise it to high Heaven. Once in a great while (actually surprisingly often) those disciples become so enamored of the description that they’ll drop everything and actually attempt to build a city to match the description.

When that happens, it invariably ends in tears.

That’s because, while utopian stories invariably describe city plans that would be paradise to their authors, great swaths of the population would find living in them to be horrific.

Even Thomas More, the sixteenth century philosopher, politician and generally overall smart guy who’s credited with giving us the word “utopia” in the first place, was wise enough to acknowledge that the utopia he described in his most famous work, Utopia, wouldn’t be such a fun place for the slaves he had serving his upper-middle class citizens, who were the bulwark of his utopian society.

Even Plato’s Republic, which gave us the conundrum summarized in Juvenal’s Satires as “Who guards the guards?,” was never meant as a workable society. Plato’s work, in general, was meant to teach us how to think, not what to think.

What to think is a highly malleable commodity that varies from person to person, society to society, and, most importantly, from time to time. Plato’s Republic reflected what might have passed as good ideas for city planning in 380 BC Athens, but they wouldn’t have passed muster in More’s sixteenth-century England. Still less would they be appropriate in twenty-first-century democracies.

So, I approached Joe Tankersley’s Reimagining Our Tomorrows with some trepidation. I wouldn’t have put in the effort to read the thing if it wasn’t for the subtitle: “Making Sure Your Future Doesn’t SUCK.”

That subtitle indicated that Tankersley just might have a sense of humor, and enough gumption to put that sense of humor into his contribution to Futurism.

Futurism tends to be the work of self-important intellectuals out to make a buck by feeding their audience on fantasies that sound profound, but bear no relation to any actual or even possible future. Its greatest value is in stimulating profits for publishers of magazines and books about Futurism. Otherwise, they’re not worth the trees killed to make the paper they’re printed on.

Trees, after all and as a group, make a huge contribution to all facets of human life. Like, for instance, breathing. Breathing is of incalculable value to humans. Trees make an immense contribution to breathing by absorbing carbon dioxide and pumping out vast quantities of oxygen, which humans like to breathe.

We like trees!

Futurists, not so much.

Tankersley’s little (168 pages, not counting author bio, front matter and introduction) opus is not like typical Futurist literature, however. Well, it would be like that if it weren’t more like the Republic in that it’s avowed purpose is to stimulate its readers to think about the future themselves. In the introduction that I purposely left out of the page count he says:

I want to help you reimagine our tomorrows; to show you that we are living in a time when the possibility of creating a better future has never been greater.”

Tankersley structured the body of his book in ten chapters, each telling a separate story about an imagined future centered around a possible solution to an issue relevant today. Following each chapter is an “apology” by a fictional future character named Archibald T. Patterson III.

Archie is what a hundred years ago would have been called a “Captain of Industry.” Today, we’d refer to him as an uber-rich and successful entrepreneur. Think Elon Musk or Bill Gates.

Actually, I think he’s more like Warren Buffet in that he’s reasonably introspective and honest with himself. Archie sees where society has come from, how it got to the future it got to, and what he and his cohorts did wrong. While he’s super-rich and privileged, the futures the stories describe were made by other people who weren’t uber-rich and successful. His efforts largely came to naught.

The point Tankersley seems to be making is that progress comes from the efforts of ordinary individuals who, in true British fashion, “muddle through.” They see a challenge and apply their talents and resources to making a solution. The solution is invariably nothing anyone would foresee, and is nothing like what anyone else would come up with to meet the same challenge. Each is a unique response to a unique challenge by unique individuals.

It might seem naive, this idea that human development comes from ordinary individuals coming up with ordinary solutions to ordinary problems all banded together into something called “progress,” but it’s not.

For example, Mark Zuckerberg developed Facebook as a response to the challenge of applying then-new computer-network technology to the age-old quest by late adolescents to form their own little communities by communicating among themselves. It’s only fortuitous that he happened on the right combination of time (the dawn of a radical new technology), place (in the midst of a huge cadre of the right people well versed in using that radical new technology) and marketing to get the word out to those right people wanting to use that radical new technology for that purpose. Take away any of those elements and there’d be no Facebook!

What if Zuckerberg hadn’t invented Facebook? In that event, somebody else (Reid Hoffman) would have come up with a similar solution (Linkedin) to the same challenge facing a similar group (technology professionals).

Oh, my! They did!

History abounds with similar examples. There’s hardly any advancement in human culture that doesn’t fit this model.

The good news is that Tankersley’s vision for how we can re-imagine our tomorrows is right on the money.

The bad news is … there isn’t any bad news!

Robots Revisited

Engineer with SCARA robots
Engineer using monitoring system software to check and control SCARA welding robots in a digital manufacturing operation. PopTika/Shutterstock

12 December 2018 – I was wondering what to talk about in this week’s blog posting, when an article bearing an interesting-sounding headline crossed my desk. The article, written by Simone Stolzoff of Quartz Media was published last Monday (12/3/2018) by the World Economic Forum (WEF) under the title “Here are the countries most likely to replace you with a robot.”

I generally look askance at organizations with grandiose names that include the word “World,” figuring that they likely are long on megalomania and short on substance. Further, this one lists the inimitable (thank God there’s only one!) Al Gore on its Board of Trustees.

On the other hand, David Rubenstein is also on the WEF board. Rubenstein usually seems to have his head screwed on straight, so that’s a positive sign for the organization. Therefore, I figured the article might be worth reading and should be judged on its own merits.

The main content is summarized in two bar graphs. The first lists the ratio of robots to thousands of manufacturing workers in various countries. The highest scores go to South Korea and Singapore. In fact, three of the top four are Far Eastern countries. The United States comes in around number seven.Figure 1

The second applies a correction to the graphed data to reorder the list by taking into account the countries’ relative wealth. There, the United States comes in dead last among the sixteen countries listed. East Asian countries account for all of the top five.

Figure 2The take-home-lesson from the article is conveniently stated in its final paragraph:

The upshot of all of this is relatively straightforward. When taking wages into account, Asian countries far outpace their western counterparts. If robots are the future of manufacturing, American and European countries have some catching up to do to stay competitive.

This article, of course, got me started thinking about automation and how manufacturers choose to adopt it. It’s a subject that was a major theme throughout my tenure as Chief Editor of Test & Measurement World and constituted the bulk of my work at Control Engineering.

The graphs certainly support the conclusions expressed in the cited paragraph’s first two sentences. The third sentence, however, is problematical.

That ultimate conclusion is based on accepting that “robots are the future of manufacturing.” Absolute assertions like that are always dangerous. Seldom is anything so all-or-nothing.

Predicting the future is epistemological suicide. Whenever I hear such bald-faced statements I recall Jim Morrison’s prescient statement: “The future’s uncertain and the end is always near.”

The line was prescient because a little over a year after the song’s release, Morrison was dead at age twenty seven, thereby fulfilling the slogan expressed by John Derek’s “Nick Romano” character in Nicholas Ray’s 1949 film Knock on Any Door: “Live fast, die young, and leave a good-looking corpse.”

Anyway, predictions like “robots are the future of manufacturing” are generally suspect because, in the chaotic Universe in which we live, the future is inherently unpredictable.

If you want to say something practically guaranteed to be wrong, predict the future!

I’d like to offer an alternate explanation for the data presented in the WEF graphs. It’s based on my belief that American Culture usually gets things right in the long run.

Yes, that’s the long run in which economist John Maynard Keynes pointed out that we’re all dead.

My belief in the ultimate vindication of American trends is based, not on national pride or jingoism, but on historical precedents. Countries that have bucked American trends often start out strong, but ultimately fade.

An obvious example is trendy Japanese management techniques based on Druckerian principles that were so much in vogue during the last half of the twentieth century. Folks imagined such techniques were going to drive the Japanese economy to pre-eminence in the world. Management consultants touted such principles as the future for corporate governance without noticing that while they were great for middle management, they were useless for strategic planning.

Japanese manufacturers beat the crap out of U.S. industry for a while, but eventually their economy fell into a prolonged recession characterized by economic stagnation and disinflation so severe that even negative interest rates couldn’t restart it.

Similar examples abound, which is why our little country with its relatively minuscule population (4.3% of the world’s) has by far the biggest GDP in the world. China, with more than four times the population, grosses less than a third of what we do.

So, if robotic adoption is the future of manufacturing, why are we so far behind? Assuming we actually do know what we’re doing, as past performance would suggest, the answer must be that the others are getting it wrong. Their faith in robotics as a driver of manufacturing productivity may be misplaced.

How could that be? What could be wrong with relying on technological advancement as the driver of productivity?

Manufacturing productivity is calculated on the basis of stuff produced (as measured by its total value in dollars) divided by the number of worker-hours needed to produce it. That should tell you something about what it takes to produce stuff. It’s all about human worker involvement.

Folks who think robots automatically increase productivity are fixating on the denominator in the productivity calculation. Making even the same amount of stuff while reducing the worker-hours needed to produce it should drive productivity up fast. That’s basic number theory. Yet, while manufacturing has been rapidly introducing all kinds of automation over the last few decades, productivity has stagnated.

We need to look for a different explanation.

It just might be that robotic adoption is another example of too much of a good thing. It might be that reliance on technology could prove to be less effective than something about the people making up the work force.

I’m suggesting that because I’ve been led to believe that work forces in the Far Eastern developing economies are less skillful, may have lower expectations, and are more tolerant of authoritarian governments.

Why would those traits make a difference? I’ll take them one at a time to suggest how they might.

The impression that Far Eastern populations are less skillful is not easy to demonstrate. Nobody who’s dealt with people of Asian extraction in either an educational or work-force setting would ever imagine they are at all deficient in either intelligence or motivation. On the other hand, as emerging or developing economies those countries are likely more dependent on workers newly recruited from rural, agrarian settings, who are likely less acclimated to manufacturing and industrial environments. On this basis, one may posit that the available workers may prove less skillful in a manufacturing setting.

It’s a weak argument, but it exists.

The idea that people making up Far-Eastern work forces have lower expectations than those in more developed economies is on firmer footing. Workers in Canada, the U.S. and Europe have very high expectations for how they should be treated. Wages are higher. Benefits are more generous. Upward mobility perceptions are ingrained in the cultures.

For developing economies, not so much.

Then, we come to tolerance of authoritarian regimes. Tolerance of authoritarianism goes hand-in-hand with tolerance for the usual authoritarian vices of graft, lack of personal freedom and social immobility. Only those believing populist political propaganda think differently (which is the danger of populism).

What’s all this got to do with manufacturing productivity?

Lack of skill, low expectations and patience under authority are not conducive to high productivity. People are productive when they work hard. People work hard when they are incentivized. They are incentivized to work when they believe that working harder will make their lives better. It’s not hard to grasp!

Installing robots in a plant won’t by itself lead human workers to believe that working harder will make their lives better. If anything, it’ll do the opposite. They’ll start worrying that their lives are about to take a turn for the worse.

Maybe that has something to do with why increased automation has failed to increase productivity.

Teaching News Consumption and Critical Thinking

Teaching media literacy
Teaching global media literacy to children should be started when they’re young. David Pereiras/Shutterstock

21 November 2018 – Regular readers of this blog know one of my favorite themes is critical thinking about news. Another of my favorite subjects is education. So, they won’t be surprised when I go on a rant about promoting teaching of critical news consumption habits to youngsters.

Apropos of this subject, last week the BBC launched a project entitled “Beyond Fake News,” which aims to “fight back” against fake news with a season of documentaries, special reports and features on the BBC’s international TV, radio and online networks.

In an article by Lucy Mapstone, Press Association Deputy Entertainment Editor for the Independent.ie digital network, entitled “BBC to ‘fight back’ against disinformation with Beyond Fake News project,” Jamie Angus, director of the BBC World Service Group, is quoted as saying: “Poor standards of global media literacy, and the ease with which malicious content can spread unchecked on digital platforms mean there’s never been a greater need for trustworthy news providers to take proactive steps.”

Angus’ quote opens up a Pandora’s box of issues. Among them is the basic question of what constitutes “trustworthy news providers” in the first place. Of course, this is an issue I’ve tackled in previous columns.

Another issue is what would be appropriate “proactive steps.” The BBC’s “Beyond Fake News” project is one example that seems pretty sound. (Sorry if this language seems a little stilted, but I’ve just finished watching a mid-twentieth-century British film, and those folks tended to talk that way. It’ll take me a little while to get over it.)

Another sort of “proactive step” is what I’ve been trying to do in this blog: provide advice about what steps to take to ensure that the news you consume is reliable.

A third is providing rebuttal of specific fake-news stories, which is what pundits on networks like CNN and MSNBC try (with limited success, I might say) to do every day.

The issue I hope to attack in this blog posting is the overarching concern in the first phrase of the Angus quote: “Poor standards of global media literacy, … .”

Global media literacy can only be improved the same way any lack of literacy can be improved, and that is through education.

Improving global media literacy begins with ensuring a high standard of media literacy among teachers. Teachers can only teach what they already know. Thus, a high standard of media literacy must start in college and university academic-education programs.

While I’ve spent decades teaching at the college level, so I have plenty of experience, I’m not actually qualified to teach other teachers how to teach. I’ve only taught technical subjects, and the education required to teach technical subjects centers on the technical subjects themselves. The art of teaching is (or at least was when I was at university) left to the student’s ability to mimic what their teachers did, informal mentoring by fellow teachers, and good-ol’ experience in the classroom. We were basically dumped into the classroom and left to sink or swim. Some swam, while others sank.

That said, I’m not going to try to lay out a program for teaching teachers how to teach media literacy. I’ll confine my remarks to making the case that it needs to be done.

Teaching media literacy to schoolchildren is especially urgent because the media-literacy projects I keep hearing about are aimed at adults “in the wild,” so to speak. That is, they’re aimed at adult citizens who have already completed their educations and are out earning livings, bringing up families, and participating in the political life of society (or ignoring it, as the case may be).

I submit that’s exactly the wrong audience to aim at.

Yes, it’s the audience that is most involved in media consumption. It’s the group of people who most need to be media literate. It is not, however, the group that we need to aim media-literacy education at.

We gotta get ‘em when they’re young!

Like any other academic subject, the best time to teach people good media-consumption habits is before they need to have them, not afterwards. There are multiple reasons for this.

First, children need to develop good habits before they’ve developed bad habits. It saves the dicey stage of having to unlearn old habits before you can learn new ones. Media literacy is no different. Neither is critical thinking.

Most of the so-called “fake news” appeals to folks who’ve never learned to think critically in the first place. They certainly try to think critically, but they’ve never been taught the skills. Of course, those critical-thinking skills are a prerequisite to building good media-consumption habits.

How can you get in the habit of thinking critically about news stories you consume unless you’ve been taught to think critically in the first place? I submit that the two skills are so intertwined that the best strategy is to teach them simultaneously.

And, it is most definitely a habit, like smoking, drinking alcohol, and being polite to pretty girls (or boys). It’s not something you can just tell somebody to do, then expect they’ll do it. They have to do it over and over again until it becomes habitual.

‘Nuff said.

Another reason to promote media literacy among the young is that’s when people are most amenable to instruction. Human children are pre-programmed to try to learn things. That’s what “play” is all about. Acquiring knowledge is not an unpleasant chore for children (unless misguided adults make it so). It’s their job! To ensure that children learn what they need to know to function as adults, Mommy Nature went out of her way to make learning fun, just as she did with everything else humans need to do to survive as a species.

Learning, having sex, taking care of babies are all things humans have to do to survive, so Mommy Nature puts systems in place to make them fun, and so drive humans to do them.

A third reason we need to teach media literacy to the young is that, like everything else, you’re better off learning it before you need to practice it. Nobody in their right mind teaches a novice how to drive a car by running them out in city traffic. High schools all have big, torturously laid out parking lots to give novice drivers a safe, challenging place to practice the basic skills of starting, stopping and turning before they have to perform those functions while dealing with fast-moving Chevys coming out of nowhere.

Similarly, you want students to practice deciphering written and verbal communications before asking them to parse a Donald-Trump speech!

The “Call to Action” for this editorial piece is thus, “Agitate for developing good media-consumption habits among schoolchildren along with the traditional Three Rs.” It starts with making the teaching of media literacy part of K-12 teacher education. It also includes teaching critical thinking skills and habits at the same time. Finally, it includes holding K-12 teachers responsible for inculcating good media-consumption habits in their students.

Yes, it’s important to try to bring the current crop of media-illiterate adults up to speed, but it’s more important to promote global media literacy among the young.

Computers Are Revolting!

Will Computers Revolt? cover
Charles Simon’s Will Computers Revolt? looks at the future of interactions between artificial intelligence and the human race.

14 November 2018 – I just couldn’t resist the double meaning allowed by the title for this blog posting. It’s all I could think of when reading the title of Charles Simon’s new book, Will Computers Revolt? Preparing for the Future of Artificial Intelligence.

On one hand, yes, computers are revolting. Yesterday my wife and I spent two hours trying to figure out how to activate our Netflix account on my new laptop. We ended up having to change the email address and password associated with the account. And, we aren’t done yet! The nice lady at Netflix sadly informed me that in thirty days, their automated system would insist that we re-log-into the account on both devices due to the change.

That’s revolting!

On the other hand, the uprising has already begun. Computers are revolting in the sense that they’re taking power to run our lives.

We used to buy stuff just by picking it off the shelf, then walking up to the check-out counter and handing over a few pieces of green paper. The only thing that held up the process was counting out the change.

Later, when credit cards first reared their ugly heads, we had to wait a few minutes for the salesperson to fill out a sales form, then run our credit cards through the machine. It was all manual. No computers involved. It took little time once you learned how to do it, and, more importantly, the process was pretty much the same everywhere and never changed, so once you learned it, you’d learned it “forever.”

Not no more! How much time do you, I, and everyone else waste navigating the multiple pages we have to walk through just to pay for anything with a credit or debit card today?

Even worse, every store has different software using different screens to ask different questions. So, we can’t develop a habitual routine for the process. It’s different every time!

Not long ago the banks issuing my debit-card accounts switched to those %^&^ things with the chips. I always forget to put the thing in the slot instead of swiping the card across the magnetic-stripe reader. When that happens we have to start the process all over, wasting even more time.

The computers have taken over, so now we have to do what they tell us to do.

Now we know who’ll be first against the wall when the revolution comes. It’s already here and the first against the wall is us!

Golem Literature in Perspective

But seriously folks, Simon’s book is the latest in a long tradition of works by thinkers fascinated by the idea that someone could create an artifice that would pass for a human. Perhaps the earliest, and certainly the most iconic, is the golem stories from Jewish folklore. I suspect (on no authority, whatsoever, but it does seem likely) that the idea of a golem appeared about the time when human sculptors started making statues in realistic human form. That was very early, indeed!

A golem is, for those who aren’t familiar with the term or willing to follow the link provided above to learn about it, an artificial creature fashioned by a human that is effectively what we call a “robot.” The folkloric golems were made of clay or (sometimes) wood because those were the best materials available at the time that the stories’ authors’ could have their artists work with. A well-known golem story is Carlo Collodi’s The Adventures of Pinocchio.

By the sixth century BCE, Greek sculptors had begun to produce lifelike statues. The myth of Pygmalion and Galatea appeared in a pseudo-historical work by Philostephanus Cyrenaeus in the third century BCE. Pygmalion was a sculptor who made a statue representing his ideal woman, then fell in love with it. Aphrodite granted his prayer for a wife exactly like the statue by bringing the statue to life. The wife’s name was Galatea.

The Talmud points out that Adam started out as a golem. Like Galatea, Adam was brought to life when the Hebrew God Yahweh gave him a soul.

These golem examples emphasize the idea that humans, no matter how holy or wise, cannot give their creations a soul. The best they can do is to create automatons.

Simon effectively begs to differ. He spends the first quarter of his text laying out the case that it is possible, and indeed inevitable, that automated control systems displaying artificial general intelligence (AGI) capable of thinking at or (eventually) well above human capacity will appear. He spends the next half of his text showing how such AGI systems could be created and making the case that they will eventually exhibit functionality indistinguishable from consciousness. He devotes the rest of his text to speculating about how we, as human beings, will likely interact with such hyperintelligent machines.

Spoiler Alert

Simon’s answer to the question posed by his title is a sort-of “yes.” He feels AGIs will inevitably displace humans as the most intelligent beings on our planet, but won’t exactly “revolt” at any point.

“The conclusion,” he says, “is that the paths of AGIs and humanity will diverge to such an extent that there will be no close relationship between humans and our silicon counterparts.”

There won’t be any violent conflict because robotic needs are sufficiently dissimilar to ours that there won’t be any competition for scarce resources, which is what leads to conflict between groups (including between species).

Robots, he posits, are unlikely to care enough about us to revolt. There will be no Terminator robots seeking to exterminate us because they won’t see us as enough of a threat to bother with. They’re more likely to view us much the way we view squirrels and birds: pleasant fixtures of the natural world.

They won’t, of course, tolerate any individual humans who make trouble for them the same way we wouldn’t tolerate a rabid coyote. But, otherwise, so what?

So, the !!!! What?

The main value of Simon’s book is not in its ultimate conclusion. That’s basically informed opinion. Rather, its value lies in the voluminous detail he provides in getting to that conclusion.

He spends the first quarter of his text detailing exactly what he means by AGI. What functions are needed to make it manifest? How will we know when it rears its head (ugly or not, as a matter of taste)? How will a conscious, self-aware AGI system act?

A critical point Simon makes in this section is the assertion that AGI will arise first in autonomous mobile robots. I thoroughly agree for pretty much the same reasons he puts forth.

I first started seriously speculating about machine intelligence back in the middle of the twentieth century. I never got too far – certainly not as far as Simon gets in this volume – but pretty much the first thing I actually did realize was that it was impossible to develop any kind of machine with any recognizable intelligence unless its main feature was having a mobile body.

Developing any AGI feature requires the machine to have a mobile body. It has to take responsibility not only for deciding how to move itself about in space, but figuring out why. Why would it, for example, rather be over there, rather than to just stay here? Note that biological intelligence arose in animals, not in plants!

Simultaneously with reading Simon’s book, I was re-reading Robert A. Heinlein’s 1966 novel The Moon is a Harsh Mistress, which is one of innumerable fiction works whose plot hangs on actions of a superintelligent sentient computer. I found it interesting to compare Heinlein’s early fictional account with Simon’s much more informed discussion.

Heinlein sidesteps the mobile-body requirement by making his AGI arise in a computer tasked with operating the entire infrastructure of the first permanent human colony on the Moon (more accurately in the Moon, since Heinlein’s troglodytes burrowed through caves and tunnels, coming up to the surface only reluctantly when circumstances forced them to). He also avoids trying to imagine the AGI’s inner workings, by glossing over with the 1950s technology he was most familiar with.

In his rather longish second section, Simon leads his reader through a thought experiment speculating about what components an AGI system would need to have for its intelligence to develop. What sorts of circuitry might be needed, and how might it be realized? This section might be fascinating for those wanting to develop hardware and software to support AGI. For those of us watching from our armchairs on the outside, though, not so much.

Altogether, Charles Simon’s Will Computers Revolt? is an important book that’s fairly easy to read (or, at least as easy as any book this technical can be) and accessible to a wide range of people interested in the future of robotics and artificial intelligence. It is not the last word on this fast-developing field by any means. It is, however, a starting point for the necessary debate over how we should view the subject. Do we have anything to fear? Do we need to think about any regulations? Is there anything to regulate and would any such regulations be effective?

Babies and Bath Water

A baby in bath water
Don’t throw the baby out with the bathwater. Switlana Symonenko/Shutterstock.com

31 October 2018 – An old catchphrase derived from Medieval German is “Don’t throw the baby out with the bathwater.” It expresses an important principle in systems engineering.

Systems engineering focuses on how to design, build, and manage complex systems. A system can consist of almost anything made up of multiple parts or elements. For example, an automobile internal combustion engine is a system consisting of pistons, valves, a crankshaft, etc. Complex systems, such as that internal combustion engine, are typically broken up into sub-systems, such as the ignition system, the fuel system, and so forth.

Obviously, the systems concept can be applied to almost everything, from microorganisms to the World economy. As another example, medical professionals divide the human body into eleven organ systems, which would each be sub-systems within the body, which is considered as a complex system, itself.

Most systems-engineering principles transfer seamlessly from one kind of system to another.

Perhaps the most best known example of a systems-engineering principle was popularized by Robin Williams in his Mork and Mindy TV series. The Used-Car rule, as Williams’ Mork character put it, quite simply states:

“If it works, don’t fix it!”

If you’re getting the idea that systems engineering principles are typically couched in phrases that sound pretty colloquial, you’re right. People have been dealing with systems for as long as there have been people, so most of what they discovered about how to deal with systems long ago became “common sense.”

Systems engineering coalesced into an interdisciplinary engineering field around the middle of the twentieth century. Simon Ramo is sometimes credited as the founder of modern systems engineering, although many engineers and engineering managers contributed to its development and formalization.

The Baby/Bathwater rule means (if there’s anybody out there still unsure of the concept) that when attempting to modify something big (such as, say, the NAFTA treaty), make sure you retain those elements you wish to keep while in the process of modifying those elements you want to change.

The idea is that most systems that are already in place more or less already work, indicating that there are more elements that are right than are wrong. Thus, it’ll be easier, simpler, and less complicated to fix what’s wrong than to violate another systems principle:

“Don’t reinvent the wheel.”

Sometimes, on the other hand, something is such an unholy mess that trying to pick out those elements that need to change from the parts you don’t wish to change is so difficult that it’s not worth the effort. At that point, you’re better off scrapping the whole thing (throwing the baby out with the bathwater) and starting over from scratch.

Several months ago, I noticed that a seam in the convertible top on my sports car had begun to split. I quickly figured out that the big brush roller at my neighborhood automated car wash was over stressing the more-than-a-decade-old fabric. Naturally, I stopped using that car wash, and started looking around for a hand-detailing shop that would be more gentle.

But, that still left me with a convertible top that had started to split. So, I started looking at my options for fixing the problem.

Considering the car’s advanced age, and that a number of little things were starting to fail, I first considered trading the whole car in for a newer model. That, of course, would violate the rule about not throwing the baby out with the bath water. I’d be discarding the whole car just because of a small flaw, which might be repaired.

Of course, I’d also be getting rid of a whole raft of potentially impending problems. Then, again, I might be taking on a pile of problems that I knew nothing about.

It turned out, however, that the best car-replacement option was unacceptable, so I started looking into replacing just the convertible top. That, too, turned out to be infeasible. Finally, I found an automotive upholstery specialist who described a patching scheme that would solve the immediate problem and likely last through the remaining life of the car. So, that’s what I did.

I’ve put you through listening to this whole story to illustrate the thought process behind applying the “don’t throw the baby out with the bathwater” rule.

Unfortunately, our current President, Donald Trump, seems to have never learned anything about systems engineering, or about babies and bathwater. He’s apparently enthralled with the idea that he can bully U.S. trading partners into giving him concessions when he negotiates with them one-on-one. That’s the gist of his love of bilateral trade agreements.

Apparently, he feels that if he gets into a multilateral trade negotiation, his go-to strategy of browbeating partners into giving in to him might not work. Multiple negotiating partners might get together and provide a united front against him.

In fact, that’s a reasonable assumption. He’s a sufficiently weak deal maker on his own that he’d have trouble standing up to a combination of, say, Mexico’s Nieto and Canada’s Trudeau banded together against him.

With that background, it’s not hard to understand why POTUS is looking at all U.S. treaties, which are mostly multilateral, and looking for any niddly thing wrong with them to use as an excuse to scrap the whole arrangement and start over. Obvious examples being the NAFTA treaty and the Iran Nuclear Accord.

Both of these treaties have been in place for some time, and have generally achieved the goals they were put in place to achieve. Howsoever, they’re not perfect, so POTUS is in the position of trying to “fix” them.

Since both these treaties are multilateral deals, to make even minor adjustments POTUS would have to enter multilateral negotiations with partners (such as Germany’s quantum-physicist-turned-politician, Angela Merkel) who would be unlikely to cow-tow to his bullying style. Robbed of his signature strategy, he’d rather scrap the whole thing and start all over, taking on partners one at a time in bilateral negotiations. So, that’s what he’s trying to do.

A more effective strategy would be to forget everything his ghostwriter put into his self-congratulatory “How-To” book The Art of the Deal, enumerate a list of what’s actually wrong with these documents, and tap into the cadre of veteran treaty negotiators that used to be available in the U.S. State Department to assemble a team of career diplomats capable of fixing what’s wrong without throwing the babies out with the bathwater.

But, that would violate his narcissistic world view. He’d have to admit that it wasn’t all about him, and acknowledge one of the first principles of project management (another discipline that he should have vast knowledge of, but apparently doesn’t):

Begin by making sure the needs of all stakeholders are built into any project plan.”