Author: masi

Teacher, artist, scientist, engineer, journalist, and author, C.G. Masi writes adventure/mystery novels having multi-layered plots with unconventional characters who apply intelligence, understanding of historical and social issues, and mastery of high technology to resolve the situations they confront. Masi has advanced degrees in astrophysics and business administration, with hundreds of published articles in magazines as diverse as American Iron and Review of Scientific Instruments. As an award-winning magazine editor, he has been involved in launches of four successful magazines. His non-fiction book, How to Set Up Your Motorcycle Workshop, is in its third edition.
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Falling Out of the Sky

B737 Max taking off
Thai Lion Air Boeing 737 Max 9 taking off from Don Mueang international airport in Bankok, Thailand. Komenton / Shutterstock.com

3 April 2019 – On 29 October 2018, Lion Air flight 610 crashed soon after takeoff from Soekarno–Hatta International Airport in Jakarta, Indonesia. This is not the sort of thing we report in this blog. It’s straight news and we leave that to straight-news media, but I’m diving into it because it involves technology I’m quite familiar with and I might be able to help readers make sense of what happened and judge the often-uninformed reactions to it.

I claim to have the background to understand what happened because I’ve been flying light planes since the 1990s. I also put two years into a post-graduate Aerospace Engineering Program at Arizona State University concentrating on fluid dynamics. That’s enough background to make some educated guesses at what happened to Lion Air 610 as well as in the almost identical crash of an Ethiopian Airlines Boeing 737 MAX in Addis Ababa, , Ethiopia on 10 March 2019.

First, both airliners were recently commissioned Boeing 737 MAX aircraft using standard-equipment installations of Boeing’s new Maneuvering Characteristics Augmentation System (MCAS).

How to Stall an Aircraft

In aerodynamics the word “stall” means something quite unlike what most people expect. Most people encounter the word in an automobile context, where it refers to “stalling the engine.” That happens when you overload an internal-combustion engine. That is pull more power out than the engine can produce at its current operating speed. When that happens, the engine simply stops.

It turns from a power-producing machine to a boat anchor in a heartbeat. Your car stops with a lurch and everyone behind you starts swearing and blowing their horns in an effort to make you feel even worse than you already do.

That’s not what happens when an airplane stalls. It’s not the aircraft’s engine that stalls, but it’s wings. There are similarities in that, like engines, wings stall when they’re overloaded and when stalled they start producing drag like a boat anchor, but that’s about where the similarities end.

When an aircraft stalls, nobody swears and blows their horn. Instead, they scream and die.

Why? Well, wings are supposed to lift the aircraft and support it in the air. If you’ve ever tried to carry a sheet of plywood on a windy day you’ve experience both lift and drag. If you let the sheet tip up a little bit so the wind catches it underneath, it tries to fly up out of your hands. That’s the lift an airplane gets by tipping its wings up into the air stream as it moves forward into the air.

The more you tip the sheet up, the more lift you get for the same airspeed. That is, until you reach a certain attack angle (the angle between the sheet and the wind). Stalling begins suddenly at an attack angle of about 15°. Then, all of a sudden, the force lifting the sheet changes from up and a little back to no up, and a lot of back!

That’s a wing stall.

The aircraft stops imitating a bird, and starts imitating a rock.

You suddenly get a visceral sense of the concept “down.”

‘Cause that’s where you go in a hurry!

At that point, all you can do is point the nose down (so the wing’s forward edge starts pointing in the direction you’re moving: down!

If you’ve got enough space underneath your aircraft so the wing starts flying again before you hit the ground, you can gently pull the aircraft’s nose back up to resume straight and level flight. If not, that’s when the screaming starts.

Wings stall when they’re going too slowly to generate the required lift at an angle of attack of 15°. At higher speeds, the wing can generate the needed lift with less angle of attack, and worries about stalling never come up.

So, now you know all you need to know (or want to know) about stalling an aircraft.

MCAS

Boeing’s MCAS is an anti-stall system. It’s beating heart is a bit of software running on the flight-control computer that monitors a number of sensor inputs, like airspeed and angle of attack. Basically, in simple terms, it knows exactly how much attack angle the wings can stand before stalling out. If it sees that for some reason, the attack angle is getting too high, it assumes the pilot has screwed up. It takes control and pushes the nose down.

It doesn’t have to actually “take control” because modern commercial aircraft are “fly by wire,” which means it’s the computer that actually moves the control surfaces to fly the plane. The pilot’s “yoke” (the little wheel he or she gets to twist and turn and move forward and back) and the rudder pedals he pushes to steer (push right, go right) just sends signals to the computer to tell it what he wants to have happen. In a sense, the pilot negotiates with the computer about what the airplane should do.

The pilot makes suggestions (through the yoke, pedals and throttle control – collectively called the “cockpit flight controls”); the computer then takes that information, combines it with all the other information provided by a plethora (Do you like that word? I do!) of additional sensors; thinks about it for a microsecond; then, finally, the computer tells the aircraft’s control surfaces to move smoothly to a position that it (the computer) thinks will make the aircraft do what it wants it to do.

That’s all well and good when the reason the attack angle got too high is just that something happened that broke the pilot’s concentration, and he (or she) actually screwed up. What about when the pilot actually wants to stall the aircraft?

For example, on landing.

To land a plane, you slow it way down, so the wing’s almost stalled. Then, you fly it really close to the ground so the wheels almost touch the runway. Then you stall the wing so the wheels touch the ground just as the wings lose lift. You hear a satisfying “squeak” as the wheels momentarily skid while spinning up to match the relative speed of the runway. Finally, the wheels gently settle down, taking up the weight of the aircraft. The flight crew (and a few passengers who’ve been paying attention) cheer the pilot for a job well done, and the pilot starts breathing again.

Anti-stall systems don’t do much good during a landing, when you’re trying to intentionally stall the wings at just the right time.

Similarly, the don’t do much good when you’re taking off, and the pilot’s just trying to get the wings unstalled to get the aircraft into the air in the first place.

For those times, you want the MCAS turned off! So you’ve gotta be able to do that, too. Or, if your pilot is too absent minded to shut it off when its not needed, you need it to shut off automatically.

When Things Go Wrong

So, what happened in those two airliner crashes?

Remember that the main input into the MCAS is an attack angle sensor? Attack angle sensors, like any other piece of technology can go bad, especially if it’s exposed to weather. And, airliners are exposed to weather 24/7 except when they’re brought into a hangar for repair.

The working hypothesis for what happened to both airliners is that the attack-angle sensors failed. They jammed in a position where they erroneously reported a high angle-of-attack to the MCAS, which jumped to the conclusion “pilot error,” and pushed the nose down. When the pilot(s) tried to pull the nose back up (because their windshield filled up with things that looked a lot like ground instead of sky), the MCAS said: “Nope! You’re going down, Jack!”

By the time the pilots figured out what was wrong and looked up how to shut the MCAS off, they’d actually hit the things that looked too much like ground.

Why didn’t the MCAS figure out there was something wrong with the sensor?

How’s it supposed to know?

The sensor says the nose is pointed up, so the computer takes it at it’s word. Computers aren’t really very smart, and tend to be quite literal. The sensor says the nose is pointed up, so the computer thinks the nose is pointed up, and tries to point it down (or at least less up). End of story. And, in the real world, it’s “end of aircraft” as well.

If the pilot(s) try to tell the computer to pull the nose up (by desperately pulling back on the yoke), it figures they’re screw-ups, anyway, and won’t listen.

Every try to argue with a computer? Been there, done that. It doesn’t work.

Mea Culpa

When I learned about the hypothesis of attack-angle-sensor failure causing the crashes that took nearly four hundred lives, I got this awful sick feeling that was a mixture of embarrassment and guilt. You see, a decade and a half ago my research project at ASU was an effort to develop a different style of attack-angle sensor. Several events and circumstances combined to make me abandon that research project and, in fact, the whole PhD. program it was a part of. In my defense, it was the start of a ten-year period in which I couldn’t get anything right!

But, if I’d stuck it out and developed that sensor it might have been installed on those airliners and might not have failed at all. Of course, it could have been installed and failed in some other spectacular way.

You see, the attack angle sensor that apparently was installed consisted of a little vane attached to one side of the aircraft’s nose. Just like the wind sock traditionally hung outside airports the world over, wind pressure makes the vane line up downstream of the wind direction. A little angle sensor attached to the vane reports the wind direction relative to the nose: the attack angle.

I got involved in trying to develop an alternative attack-angle sensor because I have a horror of relying on sensors that depend on mechanical movement to work. If you’re relying on mechanical movement, it means you’re relying on bearings, and bearings can corrode and wear out and fail. The sensor I was working on relied on differences in air pressure that depended on the direction the wind hit the sensor.

In actual fact, there were two attack-angle sensors attached to the doomed aircraft – one on each side of the nose – but the Boeing MCAS was paying attention to only one of them. That was Boeing’s second mistake (the first being not using the sensor I hadn’t developed, so I guess they can’t be blamed for it). If the MCAS had been paying attention to both sensors, it would have known something in its touchy-feely universe was wrong. It might have been a little more reluctant to override the pilots’ input.

The third mistake (I believe) Boeing made was to downplay the differences between the new “Max” version of the aircraft and the older version. They’d changed the engines, which (as any aerospace engineer knows) necessitates changes in everything else. Aircraft are so intricately balanced machines that every time you change one thing, everything else has to change – or at least has to be looked at to see if it needs to be changed.

The new engines had improved performance, which affects just about everything involving the aircraft’s handling characteristics. Boeing had apparently tried to make the more-powerful yet more fuel efficient aircraft handle like the old aircraft. There, of course, were differences, which the company tried to pretend would make no difference to the pilots. The MCAS was one of those things that was supposed to make the “Max” version handle just like the non-Max version.

So, when something went wrong in “Max” land, it caught the pilots, who had thousands of hours experience with non-Max aircraft, by surprise.

The latest reports are that Boeing, the FAA, and the airlines have realized what the problems are that caused these issues (I hope they understand them a lot better than I do, because, after all, it’s their job to!), and have worked out a number of fixes.

First, the MCAS will pay attention to two attack-angle sensors. At least then the flight-control computer will have an indication that something is wrong and tell the MCAS to go back in its corner and shut up ‘til the issue is sorted out.

Second, they’ll install a little blinking light that effectively tells the pilots “there’s something wrong, so don’t expect any help from the MCAS ‘til it gets sorted out.”

Third, they’ll make sure the pilots have a good, positive way of emphatically shut the MCAS off if it starts to argue with them in an emergency. And, they’ll make sure the pilots are trained to know when and how to use it.

My understanding is that these fixes are already part of the options that American commercial airlines have generally installed, which is supposedly why the FAA, the airlines and the pilots’ union have been dragging their feet about grounding Boeing’s 737 Max fleet. Let’s hope they’re not just blowing smoke (again)!

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Luddites’ Lament

Luddites attack
An owner of a factory defending his workshop against Luddites intent on destroying his mechanized looms between 1811-1816. Everett Historical/Shutterstock

27 March 2019 – A reader of last week’s column, in which I reported recent opinions voiced by a few automation experts at February’s Conference on the Future of Work held at at Stanford University, informed me of a chapter from Henry Hazlitt’s 1988 book Economics in One Lesson that Australian computer scientist Steven Shaw uploaded to his blog.

I’m not going to get into the tangled web of potential copyright infringement that Shaw’s posting of Hazlitt’s entire text opens up, I’ve just linked to the most convenient-to-read posting of that particular chapter. If you follow the link and want to buy the book, I’ve given you the appropriate link as well.

The chapter is of immense value apropos the question of whether automation generally reduces the need for human labor, or creates more opportunities for humans to gain useful employment. Specifically, it looks at the results of a number of historic events where Luddites excoriated technology developers for taking away jobs from humans only to have subsequent developments prove them spectacularly wrong.

Hazlitt’s classic book is, not surprisingly for a classic, well documented, authoritative, and extremely readable. I’m not going to pretend to provide an alternative here, but to summarize some of the chapter’s examples in the hope that you’ll be intrigued enough to seek out the original.

Luddism

Before getting on to the examples, let’s start by looking at the history of Luddism. It’s not a new story, really. It probably dates back to just after cave guys first thought of specialization of labor.

That is, sometime in the prehistoric past, some blokes were found to be especially good at doing some things, and the rest of the tribe came up with the idea of letting, say, the best potters make pots for the whole tribe, and everyone else rewarding them for a job well done by, say, giving them choice caribou parts for dinner.

Eventually, they had the best flint knappers make the arrowheads, the best fletchers put the arrowheads on the arrows, the best bowmakers make the bows, and so on. Division of labor into different jobs turned out to be so spectacularly successful that very few of us rugged individualists, who pretend to do everything for ourselves, are few and far between (and are largely kidding ourselves, anyway).

Since then, anyone who comes up with a great way to do anything more efficiently runs the risk of having the folks who spent years learning to do it the old way land on him (or her) like a ton of bricks.

It’s generally a lot easier to throw rocks to drive the innovator away than to adapt to the innovation.

Luddites in the early nineteenth century were organized bands of workers who violently resisted mechanization of factories during the late Industrial Revolution. Named for an imaginary character, Ned Ludd, who was supposedly an apprentice who smashed two stocking frames in 1779 and whose name had become emblematic of machine destroyers. The term “Luddite” has come to mean anyone fanatically opposed to deploying advanced technology.

Of course, like religious fundamentalists, they have to pick a point in time to separate “good” technology from the “bad.” Unlike religious fanatics, who generally pick publication of a certain text to be the dividing line, Luddites divide between the technology of their immediate past (with which they are familiar) and anything new or unfamiliar. Thus, it’s a continually moving target.

In either case, the dividing line is fundamentally arbitrary, so the emotion of their response is irrational. Irrationality typically carries a warranty of being entirely contrary to facts.

What Happens Next

Hazlitt points out, “The belief that machines cause unemployment, when held with any logical consistency, leads to preposterous conclusions.” He points out that on the second page of the first chapter of Adam Smith’s seminal book Wealth of Nations, Smith tells us that a workman unacquainted with the use of machinery employed in sewing-pin-making “could scarce make one pin a day, and certainly could not make twenty,” but with the use of the machinery he can make 4,800 pins a day. So, zero-sum game theory would indicate an immediate 99.98 percent unemployment rate in the pin-making industry of 1776.

Did that happen? No, because economics is not a zero-sum game. Sewing pins went from dear to cheap. Since they were now cheap, folks prized them less and discarded them more (when was the last time you bothered to straighten a bent pin?), and more folks could afford to buy them in the first place. That led to an increase in sewing-pin sales as well as sales of things like sewing-patterns and bulk fine fabric sold to amateur sewers, and more employment, not less.

Similar results obtained in the stocking industry when new stocking frames (the original having been invented William Lee in 1589, but denied a patent by Elizabeth I who feared its effects on employment in hand-knitting industries) were protested by Luddites as fast as they could be introduced. Before the end of the nineteenth century the stocking industry was employing at least a hundred men for every man it employed at the beginning of the century.

Another example Hazlitt presents from the Industrial Revolution happened in the cotton-spinning industry. He says: “Arkwright invented his cotton-spinning machinery in 1760. At that time it was estimated that there were in England 5,200 spinners using spinning wheels, and 2,700 weavers—in all, 7,900 persons engaged in the production of cotton textiles. The introduction of Arkwright’s invention was opposed on the ground that it threatened the livelihood of the workers, and the opposition had to be put down by force. Yet in 1787—twenty-seven years after the invention appeared—a parliamentary inquiry showed that the number of persons actually engaged in the spinning and weaving of cotton had risen from 7,900 to 320,000, an increase of 4,400 percent.”

As these examples indicate, improvements in manufacturing efficiency generally lead to reductions in manufacturing cost, which, when passed along to customers, reduces prices with concommitent increases in unit sales. This is the price elasticity of demand curve from Microeconomics 101. It is the reason economics is decidedly not a zero-sum game.

If we accept economics as not a zero-sum game, predicting what happens when automation makes it possible to produce more stuff with fewer workers becomes a chancy proposition. For example, many economists today blame flat productivity (the amount of stuff produced divided by the number of workers needed to produce it) for lack of wage gains in the face of low unemployment. If that is true, then anything that would help raise productivity (such as automation) should be welcome.

Long experience has taught us that economics is a positive-sum game. In the face of technological advancement, it behooves us to expect positive outcomes while taking measures to ensure that the concomitant economic gains get distributed fairly (whatever that means) throughout society. That is the take-home lesson from the social dislocations that accompanied the technological advancements of the Early Industrial Revolution.

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Don’t Panic!

Panic button
Do not push the red button! Peter Hermes Furian/Shutterstock

20 March 2019 – The image at right visualizes something described in Douglas Adams’ Hitchiker’s Guide to the Galaxy. At one point, the main characters of that six-part “trilogy” found a big red button on the dashboard of a spaceship they were trying to steal that was marked “DO NOT PRESS THIS BUTTON!” Naturally, they pressed the button, and a new label popped up that said “DO NOT PRESS THIS BUTTON AGAIN!”

Eventually, they got the autopilot engaged only to find it was a stunt ship programmed to crash headlong into the nearest Sun as part of the light show for an interstellar rock band. The moral of this story is “Never push buttons marked ‘DO NOT PUSH THIS BUTTON.’”

Per the author: “It is said that despite its many glaring (and occasionally fatal) inaccuracies, the Hitchhiker’s Guide to the Galaxy itself has outsold the Encyclopedia Galactica because it is slightly cheaper, and because it has the words ‘DON’T PANIC’ in large, friendly letters on the cover.”

Despite these references to the Hitchhiker’s Guide to the Galaxy, this posting has nothing to do with that book, the series, or the guide it describes, except that I’ve borrowed the words from the Guide’s cover as a title. I did that because those words perfectly express the take-home lesson of Bill Snyder’s 11 March 2019 article in The Robot Report entitled “Fears of job-stealing robots are misplaced, say experts.”

Expert Opinions

Snyder’s article reports opinions expressed at the the Conference on the Future of Work at Stanford University last month. It’s a topic I’ve shot my word processor off about on numerous occasions in this space, so I thought it would be appropriate to report others’ views as well. First, I’ll present material from Snyder’s article, then I’ll wrap up with my take on the subject.

“Robots aren’t coming for your job,” Snyder says, “but it’s easy to make misleading assumptions about the kinds of jobs that are in danger of becoming obsolete.”

“Most jobs are more complex than [many people] realize,” said Hal Varian, Google’s chief economist.

David Autor, professor of economics at the Massachusetts Institute of Technology points out that education is a big determinant of how developing trends affect workers: “It’s a great time to be young and educated, but there’s no clear land of opportunity for adults who haven’t been to college.”

“When predicting future labor market outcomes, it is important to consider both sides of the supply-and-demand equation,” said Varian, “demographic trends that point to a substantial decrease in the supply of labor are potentially larger in magnitude.”

His research indicates that shrinkage of the labor supply due to demographic trends is 53% greater than shrinkage of demand for labor due to automation. That means, while relatively fewer jobs are available, there are a lot fewer workers available to do them. The result is the prospect of a continued labor shortage.

At the same time, Snyder reports that “[The] most popular discussion around technology focuses on factors that decrease demand for labor by replacing workers with machines.”

In other words, fears that robots will displace humans for existing jobs miss the point. Robots, instead, are taking over jobs for which there aren’t enough humans to do them.

Another effect is the fact that what people think of as “jobs” are actually made up of many “tasks,” and it’s tasks that get automated, not entire jobs. Some tasks are amenable to automation while others aren’t.

“Consider the job of a gardener,” Snyder suggests as an example. “Gardeners have to mow and water a lawn, prune rose bushes, rake leaves, eradicate pests, and perform a variety of other chores.”

Some of these tasks, like mowing and watering, can easily be automated. Pruning rose bushes, not so much!

Snyder points to news reports of a hotel in Nagasaki, Japan being forced to “fire” robot receptionists and room attendants that proved to be incompetent.

There’s a scene in the 1997 film The Fifth Element where a supporting character tries to converse with a robot bartender about another character. He says: “She’s so vulnerable – so human. Do you you know what I mean?” The robot shakes its head, “No.”

Sometimes people, even misanthropes, would prefer to interact with another human than with a drink-dispensing machine.

“Jobs,” Varian points out, “unlike repetitive tasks, tend not to disappear. In 1950, the U.S. Census Bureau listed 250 separate jobs. Since then, the only one to be completely eliminated is that of elevator operator.”

“Excessive automation at Tesla was a mistake,” founder Elon Musk mea culpa-ed last year “Humans are underrated.”

Another trend Snyder points out is that automation-ready jobs, such as assembly-line factory workers, have already largely disappeared from America. “The 10 most common occupations in the U.S.,” he says, “include such jobs as retail salespersons, nurses, waiters, and other service-focused work. Notably, traditional occupations, such as factory and other blue-collar work, no longer even make the list.

Again, robots are mainly taking over tasks that humans are not available to do.

The final trend that Snyder presents, is the stark fact that birthrates in developed nations are declining – in some cases precipitously. “The aging of the baby boom generation creates demand for service jobs,” Varian points out, “but leaves fewer workers actively contributing labor to the economy.”

Those “service jobs” are just the ones that require a human touch, so they’re much harder to automate successfully.

My Inexpert Opinion

I’ve been trying, not entirely successfully, to figure out what role robots will actually have vis-a-vis humans in the future. I think there will be a few macroscopic trends. And, the macroscopic trends should be the easiest to spot ‘cause they’re, well, macroscopic. That means bigger. So, there easier to see. See?

As early as 2010, I worked out one important difference between robots and humans that I expounded in my novel Vengeance is Mine! Specifically, humans have a wider view of the Universe and have more of an emotional stake in it.

“For example,” I had one of my main characters pontificate at a cocktail party, “that tall blonde over there is an archaeologist. She uses ROVs – remotely operated vehicles – to map underwater shipwreck sites. So, she cares about what she sees and finds. We program the ROVs with sophisticated navigational software that allows her to concentrate on what she’s looking at, rather than the details of piloting the vehicle, but she’s in constant communication with it because she cares what it does. It doesn’t.”

More recently, I got a clearer image of this relationship and it’s so obvious that we tend to overlook it. I certainly missed it for decades.

It hit me like a brick when I saw a video of an autonomous robot marine-trash collector. This device is a small autonomous surface vessel with a big “mouth” that glides around seeking out and gobbling up discarded water bottles, plastic bags, bits of styrofoam, and other unwanted jetsam clogging up waterways.

The first question that popped into my mind was “who’s going to own the thing?” I mean, somebody has to want it, then buy it, then put it to work. I’m sure it could be made to automatically regurgitate the junk it collects into trash bags that it drops off at some collection point, but some human or humans have to make sure the trash bags get collected and disposed of. Somebody has to ensure that the robot has a charging system to keep its batteries recharged. Somebody has to fix it when parts wear out, and somebody has to take responsibility if it becomes a navigation hazard. Should that happen, the Coast Guard is going to want to scoop it up and hand its bedraggled carcass to some human owner along with a citation.

So, on a very important level, the biggest thing robots need from humans is ownership. Humans own robots, not the other way around. Without a human owner, an orphan robot is a pile of junk left by the side of the road!

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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.
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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.”

Let’s 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 wouldn’t have a lot to talk about, and practically nothing to do. The things haven’t 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 shouldn’t 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. Let’s bite the bullet and admit we’re dealing with fuzzy-logic categories, and then move on.

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

First, it’s gotta be artificial. A cloned sheep is not a robot. Even designer germs are non-robots.
Second, it’s 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 it’s a traffic accident waiting to happen.)

Third, it’s 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), it’s 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 it’ll 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.

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Socialist Mythos

Pegasus
Like the mythical Pegasus, socialism is a beautiful idea beloved of children that cannot be realized in practice. Catmando/Shutterstock

27 February 2019 – Some ideas are just so beautiful that we try to hang on to them even after failure after failure shows them to be unrealizable. Especially for the naive, these ideas hold such fascination that they persist long after cooler inspection consigns them to the dust bin of fantasy. This essay looks at two such ideas that display features in common: the ancient Greek myth of the flying horse, Pegasus, and the modern myth of the socialist state.

Pegasus

The ancient myth of the flying horse Pegasus is an obvious example. There’s no physical reason for such a creature to be impossible. Actual horses are built far too robustly to take to the air on their own power, but a delicately built version of Equus ferus fitted with properly functioning wings could certainly be able to fly.

That’s not the objection. Certainly, other robust land animals have developed flying forms. Birds, of course, developed from what our ancestors believed to be great lumbering theropod dinosaurs. Bats belong to the same mammalian class as horses, and they fly very well, indeed.

The objection to the existence of Pegasus-like creatures comes from evolutionary history. Specifically, the history of land-based vertebrates.

You see, all land-based vertebrates on Earth evolved from a limited number of ray-finned fish species. In fact, the number of fish species contributing DNA to land-vertebrate animals is likely limited to one.

All land vertebrates have exactly the same basic body form – with modifications – that developed from features common to ray-finned fishes. Basically, they have:

  • One spine that extends into a tail,
  • One head appended to the forward (opposite the tail) end of the spine,
  • Two front appendages that developed from the fish’s pectoral fins, and
  • Two rear appendages that developed from the fish’s pelvic fins.

Not all land-based vertebrates have all these features. Some originally extant features (like the human tail and cetacean rear legs) atrophied nearly to non-existence. But, the listed features are the only ones land-based vertebrates have ever had. Of course, I’m also including such creatures as birds and dolphins that developed from land-based critters as they moved on to other habitats or back to the sea.

The reason I suggest that all land vertebrates likely hail from one fish species is that no land vertebrates have ever had anal, caudal or adipose appendages, thus we all seem to have developed from some fish species that lacked these fins.

“Aha!” you say, “cetaceans like dolphins and whales have tail fins!”

“Nope,” I rebut. “Notice that cetacean tail flukes are fleshy appendages extending horizontally from the tip of the animals’ tails, not bony appendages oriented vertically like a fish’s caudal fins.”

They developed independently and have similar shapes because of convergent evolution.

Okay, so we’ve discovered what’s wrong with Pegasus that is not wrong with bats, pterodactyls, and birds. All the real land-based vertebrate forms have four limbs, whereas the fanciful Pegasus has six (four legs and two wings). Six-limbed Pegasus can’t exist because there aren’t any similar prior forms for it to have evolved from.

So, Pegasus is a beautiful idea that simply can’t be existent on Earth.

Well, you could have some sort of flying-horse-like creature that evolved on some other planet, then caught a convenient flying saucer to pop over to Earth, but they wouldn’t be native, and likely wouldn’t look at all earthlike.

Socialist State

So, what has all this got to do with socialism?

Well, as I’ve intimated, both are beautiful ideas that people are pretty fond of. Notwithstanding its popularity, Pegasus is not possible (as a native Earth creature) for a very good reason. Socialism is also a beautiful idea that people (at least great swaths of the population) are pretty fond of. Socialism is, however, also not possible as a stable form of society for a very good reason.

The reason socialism is not possible as a stable form of society goes back to our old friend, the Tragedy of the Commons. If you aren’t intimately familiar with this concept, follow the link to a well-written article by Margaret E. Banyan, Adjunct Assistant Professor in the Southwest Florida Center for Public and Social Policy at Florida Gulf Coast University, which explains the Tragedy, its origins, and ways that have been proposed to ameliorate its effects.

Anyway, economist Milton Friedman summarized the Tragedy of the Commons with the phrase: “When everybody owns something, nobody owns it … .”

The Tragedy of the Commons speaks directly to why true socialism is impossible, or at least not tenable as a stable, permanent system. Let’s start with what the word “socialism” actually means. According to Merriam-Webster, socialism is:

“any of various economic and political theories advocating collective or governmental ownership and administration of the means of production and distribution of goods.”

Other dictionaries largely agree, so we’ll work with this definition.

So, you can see where the Tragedy of the Commons connects to socialism. The beautiful idea relates to the word “collective.”

We know that human beings evolved as territorial animals, but we’d like to imagine a utopia where we’ve gotten past this primitive urge. Without territoriality, one could imagine a world where conflict would cease to exist. Folks would just get along because nobody’d say “Hey, that’s mine. Keep your mitts off!”

The problem with such a world is the Tragedy of the Commons as described by Friedman: if everybody owns the means of production, then nobody owns it.

There are two potential outcomes

  • Scenario 1 is the utter destruction of whatever resource is held in common as described at the start of Banyan’s essay.
  • Scenario 2 is what happened to the first recorded experiment with democracy in ancient Athens: somebody steps up to the plate and takes over management of the resource for everybody. For Athens it was a series of dictator kings ending with Alexander the Great. In effect, to save the resource from destruction, some individual moves in to “own” it.

In scenario 1, the resource is destroyed along with the socialist society that collectively owns it.Everyone either starves or leaves. Result: no more socialism.

In scenario 2, the resource is saved by being claimed by some individual. That individual sets up rules for how to apportion use of the resource, which is, in effect, no longer collectively owned. Result: dictatorship and, no more socialism.

Generally, all socialist states eventually degenerate into dictatorships via scenario 2. They invariably keep the designation “socialist,” but their governments are de facto authoritarian, not socialist. This is why I say socialism is a beautiful idea that is, in the long term, impossible. Socialist states can be created, but they very quickly come under authoritarian rule.

The Democracy Option

The Merriam-Webster definition admits of one more scenario, and that’s what we use in democratically governed nations, which are generally not considered socialist states: government ownership of some (but not all) resources.

If we have a democracy, there are all kinds of great things we can have governmentally owned, but not collectively owned. Things that everybody needs and everybody uses and everybody has to share, like roads, airspace, forests, electricity grids, and national parks. These are prime candidates for government ownership.

Things like wives, husbands, houses, and bicycles (note there’s been a big bicycle-sharing SNAFU recently reported in China) have historically been shown best to not be shared!

So, in a democracy, lots of stuff can be owned by the government, rather than by individuals or “everybody.”

A prime example is airspace. I don’t mean the air itself. I mean airspace! That is the space in the air over anyplace in the United States, or virtually the entire world. One might think it’s owned by everybody, but that just ain’t so.

You just try floating off at over 500 feet above ground level (AGL) in any type of aircraft and see where it gets you. Ya just can’t do it legally. You have to get permission from the Federal Government (in the form of a pilot’s license), which involves a great whacking pile of training, examinations, and even background checks. That’s because everybody does NOT own airspace above 500 feet AGL (and great, whacking swaths of the stuff lower down, too), the government does. You, personally, individually or collectively, don’t own a bit of it and have no rights to even be there without permission from its real owner, the Federal Government.

Another one is the Interstate Highway System. Try walking down Interstate 75 in, say, Florida. Assuming you survive long enough without getting punted off the roadway by a passing Chevy, you’ll soon find yourself explaining what the heck you think you’re doing to the nearest representative (spelled C-O-P) of whatever division of government takes ownership of that particular stretch of roadway. Unless you’ve got a really good excuse (e.g., “I gotta pee real bad!”) they’ll immediately escort you off the premises via the nearest exit ramp.

Ultimately, the only viable model of socialism is a limited one that combines individual ownership of some resources that are not shared, with government ownership of other resources that are shared. Democracy provides a mechanism for determining which is what.

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Binary Thinking

binary thinking image
Binary thinking leads to artificial dichotomies and lack of cooperation. vs148/Shutterstock

13 February 2019 – Most mentally adult human beings recognize that binary thinking seldom proves useful in real-world situations. Our institutions, however, seem to be set up to promote binary thinking. And, that accounts for most of today’s societal dysfunction.

Lets start with what binary thinking really is. We’ve all heard disparaging remarks about “seeing things in black and white.” Simplistic thinking tends to categorize things into two starkly divided categories: good vs. evil, left vs. right, and, of course, dark vs. light. That latter category gives rise to the “black and white” metaphor.

“Binary thinking” refers to this simplistic strategy of dividing whatever we’re thinking about into two (hence the word “binary”) categories.

In many situations, binary thinking makes sense. For example, in team sports it makes sense to divide outcomes of contests into Team 1 wins and Team 2 loses.

Ultimately, every decision process degenerates into a selection between two choices. We do one and not the other. Even with multiple choices, we make the ultimate decision to pick one of the options to win after relegating all the others into the “loser” category.

If you think about it, however, those are always (or almost always) artificial situations. Mommy Nature seldom presents us with clear options. You aren’t presented with a clear choice between painting your house red or blue. House paint comes in a wide variety of hues that are blends of five primary colors: red, blue, yellow, black and white.

Even people aren’t really strictly divided into men and women. It’s a multidimensional mix of male-associated and female-associated traits that each blend from one extreme to another. The strict division into male and female is a dichotomy that we, as a society, impose on the world. Even existence or absence of a penis is a situation where there are numerous examples of intermediate forms.

The fact that we see binary choices everywhere is a fiction we impose on the Universe for our own convenience. That is, it’s easier and often more satisfying to create artificial dichotomies just so we don’t have to think about the middle.

But, the middle is where most of what goes on happens.

More than once I’ve depicted the expected distribution of folks holding views along the conservative/liberal spectrum by an image like that below, with those holding conservative views in red on the right and those with liberal views in blue to the left. That’s what I mean by my oft-repeated metaphor of the Red Team and Blue Team. It’s an extreme example of what statisticians call a “bimodal distribution.” That is a graph of numbers of examples plotted along a vertical axis with some linearly varying characteristic on a one-dimensional horizontal axis, that has two peaks.

Gaussian and bimodal distributions
Expected continuous spectral distribution of folks holding conservative vs. liberal views contrasted with the bimodal distribution imagined according to how the two main political parties behave.

The actual distribution we should expect from basic statistics is a single-mode distribution with a broad peak in the middle.

The two main political parties, however, act as if they imagine the distribution of political views to be bimodal, with one narrow peak ‘way over on the (liberal) left, and another narrow peak ‘way over on the (conservative) right. That picture leads to a binary view where you (the voter) are expected to be either on the left or the right.

With that view, campaigning becomes a two-team contest where the Democratic Party (Blue Team) hopes to attract voters over to their liberal view, making the blue peak larger than the red peak. The Republican Party, in turn, hopes to attract voters to their conservative agenda, making the red peak larger than the blue one.

What voters want, of course, is for the politicians to reflect the preferences they actually have. Since voters’ views can be expected to have a standard distribution with one (admittedly quite broad) peak more or less centered in the middle, Congress should be made up of folks with views falling in a broad peak more-or-less centered in the middle, with the vast majority advocating a moderate agenda. That would work out well because with that kind of distribution, compromise would be relatively easy to come by and laws would be passed that most people could find palatable, things would get done, and so forth.

Why don’t we have a situation like that? Why do we have this epidemic of binary thinking?

I believe that the answer comes from the two major parties becoming mesmerized in the 1980s by the principles of Marketing 101. The first thing they teach you in Marketing 101 is how to segment your customers. Translated into the one-dimensional left/right view so common in political thinking, that leads to imagining the bimodal distribution I’ve presented.

The actual information space characterizing voter preferences, however, is multidimensional. It’s not one single characteristic that can be represented on a one-dimensional spectrum. Every issue that comes up in political discourse represents a separate dimension, and any voter’s views appear as a point floating somewhere in that multidimensional space.

Nobody talks about this multidimensional space because it’s too complicated a picture to present in the evening news. Most political reporters don’t have the mathematical background to imagine it, let alone explain it. They’re lucky to get the basic one-dimensional spectrum picture across.

The second thing they teach you in Marketing 101 is product differentiation. Once you’ve got your customer base segmented, you pick a segment with the biggest population group, and say things to convince individuals in that group that your product (in this case, your candidate) matches the characteristics desired by that group, while the competition’s characteristics don’t.

If you think your chosen segment likes candidates wearing red T-shirts, you dress your candidate in a red T-shirt and point out that the competitor wears blue. In fact, you say things aimed at convincing voters that candidates wearing red T-shirts are somehow better (more likeable) than those awful bums wearing those ugly, nasty blue T-shirts. That way you try to attract voters to the imaginary red peak from the imaginary blue peak. If you’re successful, you win the election.

Of course, since voters actually expect your candidate to run the government after the election, what color T-shirt he or she wears is then immaterial. Since they were elected based on the color of their T-shirt, however, you end up with a legislature sitting around cheering for “Red!” or “Blue!” when voters want them to pass purple legislation.

An example of rabid binary thinking is the recent Democratic Party decision to have “zero tolerance” on race and gender issues. That thinking assumes that the blue peak on the left is filled with saintly heaven-bound creatures devoted to women’s and minorities’ rights, while the red peak on the right is full of mysogynistic racist bullies, and that there’s nobody in the middle.

That’s what “zero tolerance” means.

Liberals tried a similar stunt in the 1980s with “Political Correctness.” That fiasco worked for approximately zero time. It worked only until people realized that hardly anyone agreed with everything the PC folks liked. Since it was a binary choice – you were either politically correct or not – most folks opted for “not.” Very soon the jokes started, then folks started voting anti-PC.

What started out as a ploy by the left to bully everyone into joining their political base had the opposite effect. Most Americans don’t react well to bullying. They tend to turn on the bullies.

Instead of a cadre of Americans cowed into spouting politically correct rhetoric, we got a generation proudly claiming politically incorrect views.

You don’t hear much about political correctness, any more.

It’s quickly becoming clear that the binary thinking of the “zero tolerance” agenda will, like the PC cultural revolution, quickly lead to a “zero support” result.

Perhaps the Democratic Party should go back to school and learn Marketing 102. The first thing they teach you in Marketing 102 is “the customer is always right.”

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Americans are Ready for the Libertarian Party

Nick Sarwak Photo
Nicholas Sarwark is the Chairman of the Libertarian National Committee. Photo Courtesy Libertarian National Committee

13 February 2019 – The following is an invited guest post by Nicholas Sarwark, Chairman of the Libertarian National Committee

Republicans and Democrats often have a stranglehold on the U.S. political process, but Americans are ready for that to change.

According to a Morning Consult–Politico poll conducted in early February, more than half of all voters in the United States believe a third party is needed, and one third of all voters would be willing to vote for a third-party candidate in the 2020 presidential election. A Gallup poll from October showed that 57 percent of Americans think a strong third party is needed.

It’s no wonder why. Another Gallup poll from January revealed that only 35 percent of Americans trust the U.S. government to handle domestic problems, a number that increases to only 41 percent for international troubles. Those are the lowest figures in more than 20 years. A running Gallup poll showed that in January, 29 percent of Americans view government itself as the biggest problem facing the country.

This widespread dissatisfaction with U.S. government is consistent with the increasing prevalence of libertarian views among the general public. Polling shows that more than a quarter of Americans have political views that can be characterized as libertarian.

All of this suggests that the Libertarian Party should be winning more and bigger electoral races than ever. In fact, that’s exactly what’s happening. Out of the 833 Libertarian candidates who ran in 2018, 55 were elected to public office in 11 states.

One of those officials elected is Jeff Hewitt, who in November won a seat on the board of supervisors in Riverside County, Calif. while finishing up eight years on the Calimesa city council—three as mayor. Before being elected to the city council, he had served six years on the city’s planning commission. Hewitt recently gave the Libertarian Party’s 2019 State of the Union address, explaining how Libertarians would restrain runaway government spending, withdraw from never-ending wars abroad, end the surveillance state, protect privacy and property rights, end mass incarceration and the destructive “war on drugs,” and welcome immigrants who expand our economy and enrich our culture.

Journalist Gustavo Arellano attended Hewitt’s swearing-in ceremony on January 8. In his feature story for the Los Angeles Times, he remarked, “Riverside County Supervisor Jeff Hewitt just might be the strangest Libertarian of them all: a politician capable of winning elections who could move the party from the fringes into the mainstream.”

During Hewitt’s time as mayor of Calimesa, he severed ties with the bloated pensions and overstaffing of the state-run fire department. He replaced it with a local alternative that costs far less and has been much more effective at protecting endangered property. This simple change also eliminated two layers of administrative costs at the county and state levels.

Now Hewitt is poised to bring libertarian solutions to an even larger region, in his new position with Riverside County, which has more residents than the populations of 15 different states. This rise from local success is a model that can be replicated around the country, suggested Fullerton College political science professor Jodi Balma, quoted in the L.A. Times article as saying that Hewitt’s success shows how Libertarian candidates can “build a pipeline to higher office” with successful local races that show the practical value of Libertarian Party ideas on a small scale, then parlaying those experiences into winning state and federal office.

That practical value is immense, as Libertarian Laura Ebke showed when, as a Nebraska state legislator, she almost single-handedly brought statewide occupational-licensure reform to nearly unanimous 45-to-1, tri-partisan approval. This legislation has cleared the way for countless Nebraskans to build careers in fields that were once closed off from effective competition behind mountains of regulatory red tape.

The American people have the third party they’re looking for. The Libertarian Party is already the third-largest political party in the United States, and it shares the public’s values of fiscal responsibility and social tolerance — the same values that drive the public’s disdain for American politicians and wasteful, destructive, ineffective government programs.

The Libertarian Party is also the only alternative party that routinely appears on ballots in every state.

As of December 17 we had secured ballot access for our 2020 Presidential ticket in 33 states and the District of Columbia — the best starting position since 1914 for any alternative party at this point in the election cycle. This will substantially reduce the burden for achieving nationwide ballot access that we have so often borne. After the 1992 midterm election, for example, we had ballot access in only 17 states — half as many as today. Full ballot access for the Libertarian Party means that voters of every state will have more choice.

The climate is ripe for Libertarian progress. The pieces are all here, ready to be assembled. All it requires is building awareness of the Libertarian Party — our ideas, our values, our practical reforms, and our electoral successes — in the minds and hearts of the American public.

Nicholas Sarwark is serving his third term as chair of the Libertarian National Committee, having first been elected in 2014. Prior to that, he has served as chair of the Libertarian Party of Maryland and as vice chair of the Libertarian Party of Colorado, where he played a key role in recruiting the state’s 42 Libertarian candidates in 2014 and supported the passage of Colorado’s historic marijuana legalization initiative in 2012. In 2018, he ran for mayor of Phoenix, Ariz.

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Luddites RULE!

LindaBucklin-Shutterstock
Momma said there’d be days like this! (Apologies to songwriters Luther Dixon and Willie Denson, and, of course, the Geico Caveman.) Linda Bucklin/Shutterstock

7 February 2019 – This is not the essay I’d planned to write for this week’s blog. I’d planned a long-winded, abstruse dissertation on the use of principal component analysis to glean information from historical data in chaotic systems. I actually got most of that one drafted on Monday, and planned to finish it up Tuesday.

Then, bright and early on Tuesday morning, before I got anywhere near the incomplete manuscript, I ran headlong into an email issue.

Generally, I start my morning by scanning email to winnow out the few valuable bits buried in the steaming pile of worthless refuse that has accumulated in my Inbox since the last time I visited it. Then, I visit a couple of social media sites in an effort to keep my name if front of the Internet-entertained public. After a couple of hours of this colossal waste of time, I settle in to work on whatever actual work I have to do for the day.

So, finding that my email client software refused to communicate with me threatened to derail my whole day. The fact that I use email for all my business communications, made it especially urgent that I determine what was wrong, and then fix it.

It took the entire morning and on into the early afternoon to realize that there was no way I was going to get to that email account on my computer, find out that nobody in the outside world (not my ISP, not the cable company that went that extra mile to bring Internet signals from that telephone pole out there to the router at the center of my local area network, or anyone else available with more technosavvy than I have) was going to be able to help. I was finally forced to invent a work around involving a legacy computer that I’d neglected to throw in the trash just to get on with my technology-bound life.

At that point the Law of Deadlines forced me to abandon all hope of getting this week’s blog posting out on time, and move on to completing final edits and distribution of that press release for the local art gallery.

That wasn’t the last time modern technology let me down. In discussing a recent Physics Lab SNAFU, Danielle, the laboratory coordinator I work with at the University said: “It’s wonderful when it works, but horrible when it doesn’t.”

Where have I heard that before?

The SNAFU Danielle was lamenting happened last week.

I teach two sections of General Physics Laboratory at Florida Gulf Coast University, one on Wednesdays and one on Fridays. The lab for last week had students dropping a ball, then measuring its acceleration using a computer-controlled ultrasonic detection system as it (the ball, not the computer) bounces on the table.

For the Wednesday class everything worked perfectly. Half a dozen teams each had their own setups, and all got good data, beautiful-looking plots, and automated measurements of position and velocity. The computers then automatically derived accelerations from the velocity data. Only one team had trouble with their computer, but they got good data by switching to an unused setup nearby.

That was Wednesday.

Come Friday the situation was totally different. Out of four teams, only two managed to get data that looked even remotely like it should. Then, one team couldn’t get their computer to spit out accelerations that made any sense at all. Eventually, after class time ran out, the one group who managed to get good results agreed to share their information with the rest of the class.

The high point of the day was managing to distribute that data to everyone via the school’s cloud-based messaging service.

Concerned about another fiasco, after this week’s lab Danielle asked me how it worked out. I replied that, since the equipment we use for this week’s lab is all manually operated, there were no problems whatsoever. “Humans are much more capable than computers,” I said. “They’re able to cope with disruptions that computers have no hope of dealing with.”

The latest example of technology Hell appeared in a story in this morning’s (2/7/2019) Wall Street Journal. Some $136 million of customers’ cryptocurrency holdings became stuck in an electronic vault when the founder (and sole employee) of cryptocurrency exchange QuadrigaCX, Gerald Cotten, died of complications related to Crohn’s disease while building an orphanage in India. The problem is that Cotten was so secretive about passwords and security that nobody, even his wife, Jennifer Robertson, can get into the reserve account maintained on his laptop.

“Quadriga,” according to the WSJ account, “would need control of that account to send those funds to customers.”

No lie! The WSJ attests this bizarre tale is the God’s own truth!

Now, I’ve no sympathy for cryptocurrency mavens, which I consider to be, at best, technoweenies gleefully leading a parade down the primrose path to technology Hell, but this story illustrates what that Hell looks like!

It’s exactly what the Luddites of the early 19th Century warned us about. It’s a place of nameless frustration and unaccountable loss that we’ve brought on ourselves.

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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!