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!

Nationalism and Diversity

Flags of many countries
Nationalism can promote diversity – or not! Brillenstimmer/shutterstock

16 January 2019 – The poster child for rampant nationalism is Hitler’s National Socialist German Workers’ Party, commonly called the Nazi Party. I say “is” rather than “was” because, while resoundingly defeated by Allies of WW2 in 1945, the Nazi Party still has widespread appeal in Germany, and throughout the world.

These folks give nationalism a bad name, leading to the Oxford Living Dictionary, giving primacy to the following definition of nationalism: “Identification with one’s own nation and support for its interests, especially to the exclusion or detriment of the interests of other nations.” [Emphasis added.]

The Oxford Dictionary also offers a second definition of nationalism: “Advocacy of or support for the political independence of a particular nation or people.”

This second definition is a lot more benign, and one that I wish were more often used. I certainly prefer it!

Nationalism under the first definition has been used since time immemorial as an excuse to create closed, homogeneous societies. That was probably the biggest flaw of the Nazi state(s). Death camps, ethnic cleansing, slave labor, and most of the other evils of those regimes flowed directly from their attempts to build closed, homogeneous societies.

Under the second definition, however, nationalism can, and should, be used to create a more diverse society.

That’s a good thing, as the example of United States history clearly demonstrates. Most of U.S. success can be traced directly to the country’s ethnic, cultural and racial diversity. The fact that the U.S., with a paltry 5% of the world’s population, now has by far the largest economy; that it dominates the fields of science, technology and the humanities; that its common language (American English) is fast becoming the “lingua franca” of the entire world; and that it effectively leads the world by so many measures is directly attributed to the continual renewal of its population diversity by immigration. In any of these areas, it’s easy to point out major contributions from recent immigrants or other minorities.

This harkens back to a theory of cultural development I worked out in the 1970s. It starts with the observation that all human populations – no matter how large or how small – consist of individuals whose characteristics vary somewhat. When visualized on a multidimensional scatter plot, populations generally consist of a cluster with a dense center and fewer individuals farther out.

Globular cluster image
The Great Hercules Star Cluster.. Albert Barr/Shutterstock

This pattern is similar to the image of a typical globular star cluster in the photo at right. Globular star clusters exhibit this pattern in three dimensions, while human populations exist and can be mapped on a great many dimensions representing different characteristics. Everything from physical characteristics like height, weight and skin color, to non-physical characteristics like ethnicity and political ideology – essentially anything that can be measured – can be plotted as a separate dimension.

The dense center of the pattern consists of individuals whose characteristics don’t stray too far from the norm. Everyone, of course, is a little off average. For example, the average white American female is five-feet, four-inches tall. Nearly everyone in that population, however, is a little taller or shorter than exactly average. Very few are considerably taller or shorter, with more individuals closer to the average than farther out.

The population’s diversity shows up as a widening of the pattern. That is, diversity is a measure of how often individuals appear farther out from the center.

Darwin’s theory of natural selection posits that where the population center is depends on where is most appropriate for it to be depending on conditions. What is average height, for example, depends on a complex interplay of conditions, including nutrition, attractiveness to the opposite sex, and so forth.

Observing that conditions change with time, one expects the ideal center of the population should move about in the multidimensional characteristics space. Better childhood nutrition, for example, should push the population toward increased tallness. And, it does!

One hopes that these changes happen slowly with time, giving the population a chance to follow in response. If the changes happen too fast, however, the population is unable to respond fast enough and it goes extinct. So, wooly mammoths were unable to respond fast enough to a combination of environmental changes and increased predation by humans emigrating into North America after the last Ice Age, so they died out. No more wooly mammoths!

Assuming whatever changes occur happen slowly enough, those individuals in the part of the distribution better adapted to the new conditions do better than those on the opposite side. So, the whole population shifts with time toward characteristics that are better adapted.

Where diversity comes into this dynamic is by providing more individuals in the better-adapted part of the distribution. The faster conditions change, the more individuals you need at the edges of the population to help with the response. For example, if the climate gets warmer, it’s folks who like to wear skimpy outfits who thrive. Folks who insist on covering themselves up in heavy clothing, don’t do so well. That was amply demonstrated when Englishmen tried to wear their heavy Elizabethan outfits in the warmer North American weather conditions. Styles changed practically overnight!

Closed, homogeneous societies of the type the Nazis tried to create have low diversity. They try to suppress folks who differ from the norm. When conditions change, such societies have less of the diversity needed to respond, so they wither and die.

That’s why cultures need diversity, and the more diversity, the better.

We live in a chaotic universe. The most salient characteristic of chaotic systems is constant change. Without diversity, we can’t respond to that change.

That’s why when technological change sped up in the early Twentieth Century, it was the bohemians of the twenties developing into the beatniks of the fifties and the hippies of the sixties that defined the cultures of the seventies and beyond.

Jerry Garcia stamp image
spatuletail/shutterstock

Long live Ben and Jerry’s Cherry Garcia Ice Cream!

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.

Reaping the Whirlwind

Tornado
Powerful Tornado destroying property, with lightning in the background. Solarseven/Shutterstock.com

24 October 2018 – “They sow the wind, and they shall reap the whirlwind” is a saying from The Holy Bible‘s Old Testament Book of Hosea. I’m certainly not a Bible scholar, but, having been paying attention for seven decades, I can attest to saying’s validity.

The equivalent Buddhist concept is karma, which is the motive force driving the Wheel of Birth and Death. It is also wrapped up with samsara, which is epitomized by the saying: “What goes around comes around.”

Actions have consequences.

If you smoke a pack of Camels a day, you’re gonna get sick!

By now, you should have gotten the idea that “reaping the whirlwind” is a common theme among the world’s religions and philosophies. You’ve got to be pretty stone headed to have missed it.

Apparently the current President of the United States (POTUS), Donald J. Trump, has been stone headed enough to miss it.

POTUS is well known for trying to duck consequences of his actions. For example, during his 2016 Presidential Election campaign, he went out of his way to capitalize on Wikileaks‘ publication of emails stolen from Hillary Clinton‘s private email server. That indiscretion and his attempt to cover it up by firing then-FBI-Director James Comey grew into a Special Counsel Investigation, which now threatens to unmask all the nefarious activities he’s engaged in throughout his entire life.

Of course, Hillary’s unsanctioned use of that private email server while serving as Secretary of State is what opened her up to the email hacking in the first place! That error came back to bite her in the backside by giving the Russians something to hack. They then forwarded that junk to Wikileaks, who eventually made it public, arguably costing her the 2016 Presidential election.

Or, maybe it was her standing up for her philandering husband, or maybe lingering suspicions surrounding the pair’s involvement in the Whitewater scandal. Whatever the reason(s), Hillary, too, reaped the whirlwind.

In his turn, Russian President Vladimir Putin sowed the wind by tasking operatives to do the hacking of Hillary’s email server. Now he’s reaping the whirlwind in the form of a laundry list sanctions by western governments and Special Counsel Investigation indictments against the operatives he sent to do the hacking.

Again, POTUS showed his stone-headedness about the Bible verse by cuddling up to nearly every autocrat in the world: Vlad Putin, Kim Jong Un, Xi Jinping, … . The list goes on. Sensing waves of love emanating from Washington, those idiots have become ever more extravagant in their misbehavior.

The latest example of an authoritarian regime rubbing POTUS’ nose in filth is the apparent murder and dismemberment of Saudi Arabian journalist Jamal Khashoggi when he briefly entered the Saudi embassy in Turkey on personal business.

The most popular theory of the crime lays blame at the feet of Mohammad Bin Salman Al Saud (MBS), Crown Prince of Saudi Arabia and the country’s de facto ruler. Unwilling to point his finger at another would-be autocrat, POTUS is promoting a Saudi cover-up attempt suggesting the murder was done by some unnamed “rogue agents.”

Actually, that theory deserves some consideration. The idea that MBS was emboldened (spelled S-T-U-P-I-D) enough to have ordered Kashoggi’s assassination in such a ham-fisted way strains credulity. We should consider the possibility that ultra-conservative Wahabist factions within the Saudi government, who see MBS’ reforms as a threat to their historical patronage from the oil-rich Saudi monarchy, might have created the incident to embarrass MBS.

No matter what the true story is, the blow back is a whirlwind!

MBS has gone out of his way to promote himself as a business-friendly reformer. This reputation has persisted despite repeated instances of continued repression in the country he controls.

The whirlwind, however, is threatening MBS’ and the Saudi monarchy’s standing in the international community. Especially, international bankers, led by JP Morgan Chase’s Jamie Dimon, and a host of Silicon Valley tech companies are running for the exits from Saudi Arabia’s three-day Financial Investment Initiative conference that was scheduled to start Tuesday (23 October 2018).

That is a major embarrassment and will likely derail MBS’ efforts to modernize Saudi Arabia’s economy away from dependence on oil revenue.

It appears that these high-powered executives are rethinking the wisdom of dealing with the authoritarian Saudi regime. They’ve decided not to sow the wind by dealing with the Saudis because they don’t want to reap the whirlwind likely to result!

Update

Since this manuscript was drafted it’s become clear that we’ll never get the full story about the Kashoggi incident. Both regimes involved (Turkey and Saudi Arabia) are authoritarians with no incentive to be honest about this story. While Saudi Arabia seems to make a pretense of press freedom, this incident shows their true colors (i.e, color them repressive). Turkey hasn’t given even a passing nod to press freedom for years. It’s like two rival foxes telling the dog about a hen house break in.

On the “dog” side, we’re stuck with a POTUS who attacks press freedom on a daily basis. So, who’s going to ferret out the truth? Maybe the Brits or the French, but not the U.S. Executive Branch!

Climate Models Bat Zero

Climate models vs. observations
Whups! Since the 1970s, climate models have overestimated global temperature rise by … a lot! Cato Institute

The articles discussed here reflect the print version of The Wall Street Journal, rather than the online version. Links to online versions are provided. The publication dates and some of the contents do not match.

10 October 2018 – Baseball is well known to be a game of statistics. Fans pay as much attention to statistical analysis of performance by both players and teams as they do to action on the field. They hope to use those statistics to indicate how teams and individual players are likely to perform in the future. It’s an intellectual exercise that is half the fun of following the sport.

While baseball statistics are quoted to three decimal places, or one part in a thousand, fans know to ignore the last decimal place, be skeptical of the second decimal place, and recognize that even the first decimal place has limited predictive power. It’s not that these statistics are inaccurate or in any sense meaningless, it’s that they describe a situation that seems predictable, yet is full of surprises.

With 18 players in a game at any given time, a complex set of rules, and at least three players and an umpire involved in the outcome of every pitch, a baseball game is a highly chaotic system. What makes it fun is seeing how this system evolves over time. Fans get involved by trying to predict what will happen next, then quickly seeing if their expectations materialize.

The essence of a chaotic system is conditional unpredictability. That is, the predictability of any result drops more-or-less drastically with time. For baseball, the probability of, say, a player maintaining their batting average is fairly high on a weekly basis, drops drastically on a month-to-month basis, and simply can’t be predicted from year to year.

Folks call that “streakiness,” and it’s one of the hallmarks of mathematical chaos.

Since the 1960s, mathematicians have recognized that weather is also chaotic. You can say with certainty what’s happening right here right now. If you make careful observations and take into account what’s happening at nearby locations, you can be fairly certain what’ll happen an hour from now. What will happen a week from now, however, is a crapshoot.

This drives insurance companies crazy. They want to live in a deterministic world where they can predict their losses far into the future so that they can plan to have cash on hand (loss reserves) to cover them. That works for, say, life insurance. It works poorly for losses do to extreme-weather events.

That’s because weather is chaotic. Predicting catastrophic weather events next year is like predicting Miami Marlins pitcher Drew Steckenrider‘s earned-run-average for the 2019 season.

Laugh out loud.

Notes from 3 October

My BS detector went off big-time when I read an article in this morning’s Wall Street Journal entitled “A Hotter Planet Reprices Risk Around the World.” That headline is BS for many reasons.

Digging into the article turned up the assertion that insurance providers were using deterministic computer models to predict risk of losses due to future catastrophic weather events. The article didn’t say that explicitly. We have to understand a bit about computer modeling to see what’s behind the words they used. Since I’ve been doing that stuff since the 1970s, pulling aside the curtain is fairly easy.

I’ve also covered risk assessment in industrial settings for many years. It’s not done with deterministic models. It’s not even done with traditional mathematics!

The traditional mathematics you learned in grade school uses real numbers. That is numbers with a definite value.

Like Pi.

Pi = 3.1415926 ….

We know what Pi is because it’s measurable. It’s the ratio of a circle’s circumference to its diameter.

Measure the circumference. Measure the diameter. Then divide one by the other.

The ancient Egyptians performed the exercise a kazillion times and noticed that, no matter what circle you used, no matter how big it was, whether you drew it on papyrus or scratched it on a rock or laid it out in a crop circle, you always came out with the same number. That number eventually picked up the name “Pi.”

Risk assessment is NOT done with traditional arithmetic using deterministic (real) numbers. It’s done using what’s called “fuzzy logic.”

Fuzzy logic is not like the fuzzy thinking used by newspaper reporters writing about climate change. The “fuzzy” part simply means it uses fuzzy categories like “small,” “medium” and “large” that don’t have precisely defined values.

While computer programs are perfectly capable of dealing with fuzzy logic, they won’t give you the kind of answers cost accountants are prepared to deal with. They won’t tell you that you need a risk-reserve allocation of $5,937,652.37. They’ll tell you something like “lots!”

You can’t take “lots” to the bank.

The next problem is imagining that global climate models could have any possible relationship to catastrophic weather events. Catastrophic weather events are, by definition, catastrophic. To analyze them you need the kind of mathermatics called “catastrophe theory.”

Catastrophe theory is one of the underpinnings of chaos. In Steven Spielberg’s 1993 movie Jurassic Park, the character Ian Malcolm tries to illustrate catastrophe theory with the example of a drop of water rolling off the back of his hand. Whether it drips off to the right or left depends critically on how his hand is tipped. A small change creates an immense difference.

If a ball is balanced at the edge of a table, it can either stay there or drop off, and you can’t predict in advance which will happen.

That’s the thinking behind catastrophe theory.

The same analysis goes into predicting what will happen with a hurricane. As I recall, at the time Hurricane Florence (2018) made landfall, most models predicted it would move south along the Appalachian Ridge. Another group of models predicted it would stall out to the northwest.

When push came to shove, however, it moved northeast.

What actually happened depended critically on a large number of details that were too small to include in the models.

How much money was lost due to storm damage was a result of the result of unpredictable things. (That’s not an editing error. It was really the second order result of a result.) It is a fundamentally unpredictable thing. The best you can do is measure it after the fact.

That brings us to comparing climate-model predictions with observations. We’ve got enough data now to see how climate-model predictions compare with observations on a decades-long timescale. The graph above summarizes results compiled in 2015 by the Cato Institute.

Basically, it shows that, not only did the climate models overestimate the temperature rise from the late 1970s to 2015 by a factor of approximately three, but in the critical last decade, when the computer models predicted a rapid rise, the actual observations showed that it nearly stalled out.

Notice that the divergence between the models and the observations increased with time. As I’ve said, that’s the main hallmark of chaos.

It sure looks like the climate models are batting zero!

I’ve been watching these kinds of results show up since the 1980s. It’s why by the late 1990s I started discounting statements like the WSJ article’s: “A consensus of scientists puts blame substantially on emissios greenhouse gasses from cars, farms and factories.”

I don’t know who those “scientists” might be, but it sounds like they’re assigning blame for an effect that isn’t observed. Real scientists wouldn’t do that. Only politicians would.

Clearly, something is going on, but what it is, what its extent is, and what is causing it is anything but clear.

In the data depicted above, the results from global climate modeling do not look at all like the behavior of a chaotic system. The data from observations, however, do look like what we typically get from a chaotic system. Stuff moves constantly. On short time scales it shows apparent trends. On longer time scales, however, the trends tend to evaporate.

No wonder observers like Steven Pacala, who is Frederick D. Petrie Professor in Ecology and Evolutionary Biology at Princeton University and a board member at Hamilton Insurance Group, Ltd., are led to say (as quoted in the article): “Climate change makes the historical record of extreme weather an unreliable indicator of current risk.”

When you’re dealing with a chaotic system, the longer the record you’re using, the less predictive power it has.

Duh!

Another point made in the WSJ article that I thought was hilarious involved prediction of hurricanes in the Persian Gulf.

According to the article, “Such cyclones … have never been observed in the Persian Gulf … with new conditions due to warming, some cyclones could enter the Gulf in the future and also form in the Gulf itself.”

This sounds a lot like a tongue-in-cheek comment I once heard from astronomer Carl Sagan about predictions of life on Venus. He pointed out that when astronomers observe Venus, they generally just see a featureless disk. Science fiction writers had developed a chain of inferences that led them from that observation of a featureless disk to imagining total cloud cover, then postulating underlying swamps teeming with life, and culminating with imagining the existence of Venusian dinosaurs.

Observation: “We can see nothing.”

Conclusion: “There are dinosaurs.”

Sagan was pointing out that, though it may make good science fiction, that is bad science.

The WSJ reporters, Bradley Hope and Nicole Friedman, went from “No hurricanes ever seen in the Persian Gulf” to “Future hurricanes in the Persian Gulf” by the same sort of logic.

The kind of specious misinformation represented by the WSJ article confuses folks who have genuine concerns about the environment. Before politicians like Al Gore hijacked the environmental narrative, deflecting it toward climate change, folks paid much more attention to the real environmental issue of pollution.

Insurance losses from extreme weather events
Actual insurance losses due to catastrophic weather events show a disturbing trend.

The one bit of information in the WSJ article that appears prima facie disturbing is contained in the graph at right.

The graph shows actual insurance losses due to catastrophic weather events increasing rapidly over time. The article draws the inference that this trend is caused by human-induced climate change.

That’s quite a stretch, considering that there are obvious alternative explanations for this trend. The most likely alternative is the possibility that folks have been building more stuff in hurricane-prone areas. With more expensive stuff there to get damaged, insurance losses will rise.

Again: duh!

Invoking Occam’s Razor (choose the most believable of alternative explanations), we tend to favor the second explanation.

In summary, I conclude that the 3 October article is poor reporting that draws conclusions that are likely false.

Notes from 4 October

Don’t try to find the 3 October WSJ article online. I spent a couple of hours this morning searching for it, and came up empty. The closest I was able to get was a draft version that I found by searching on Bradley Hope’s name. It did not appear on WSJ‘s public site.

Apparently, WSJ‘s editors weren’t any more impressed with the article than I was.

The 4 October issue presents a corroboration of my alternative explanation of the trend in insurance-loss data: it’s due to a build up of expensive real estate in areas prone to catastrophic weather events.

In a half-page expose entitled “Hurricane Costs Grow as Population Shifts,” Kara Dapena reports that, “From 1980 to 2017, counties along the U.S. shoreline that endured hurricane-strength winds from Florence in September experienced a surge in population.”

In the end, this blog posting serves as an illustration of four points I tried to make last month. Specifically, on 19 September I published a post entitled: “Noble Whitefoot or Lying Blackfoot?” in which I laid out four principles to use when trying to determine if the news you’re reading is fake. I’ll list them in reverse of the order I used in last month’s posting, partly to illustrate that there is no set order for them:

  • Nobody gets it completely right  ̶  In the 3 October WSJ story, the reporters got snookered by the climate-change political lobby. That article, taken at face value, has to be stamped with the label “fake news.”
  • Does it make sense to you? ̶  The 3 October fake news article set off my BS detector by making a number of statements that did not make sense to me.
  • Comparison shopping for ideas  ̶  Assertions in the suspect article contradicted numerous other sources.
  • Consider your source  ̶  The article’s source (The Wall Street Journal) is one that I normally trust. Otherwise, I likely never would have seen it, since I don’t bother listening to anyone I catch in a lie. My faith in the publication was restored when the next day they featured an article that corrected the misinformation.

Doing Business with Bad Guys

Threatened with a gun
Authoritarians make dangerous business partners. rubikphoto/Shutterstock

3 October 2018 – Parents generally try to drum into their childrens’ heads a simple maxim: “People judge you by the company you keep.

Children (and we’re all children, no matter how mature and sophisticated we pretend to be) just as generally find it hard to follow that maxim. We all screw it up once in a while by succumbing to the temptation of some perceived advantage to be had by dealing with some unsavory character.

Large corporations and national governments are at least as likely to succumb to the prospect of making a fast buck or signing some treaty with peers who don’t entertain the same values we have (or at least pretend to have). Governments, especially, have a tough time in dealing with what I’ll call “Bad Guys.”

Let’s face it, better than half the nations of the world are run by people we wouldn’t want in our living rooms!

I’m specifically thinking about totalitarian regimes like the People’s Republic of China (PRC).

‘Way back in the last century, Mao Tse-tung (or Mao Zedong, depending on how you choose to mis-spell the anglicization of his name) clearly placed China on the “Anti-American” team, espousing a virulent form of Marxism and descending into the totalitarian authoritarianism Marxist regimes are so prone to. This situation continued from the PRC’s founding in 1949 through 1972, when notoriously authoritarian-friendly U.S. President Richard Nixon toured China in an effort to start a trade relationship between the two countries.

Greedy U.S. corporations quickly started falling all over themselves in an effort to gain access to China’s enormous potential market. Mesmerized by the statistics of more than a billion people spread out over China’s enormous land mass, they ignored the fact that those people were struggling in a subsistence-agriculture economy that had collapsed under decades of mis-managment by Mao’s authoritarian regime.

What they hoped those generally dirt-poor peasants were going to buy from them I never could figure out.

Unfortunately, years later I found myself embedded in the management of one of those starry-eyed multinational corporations that was hoping to take advantage of the developing Chinese electronics industry. Fresh off our success launching Test & Measurement Europe, they wanted to launch a new publication called Test & Measurement China. Recalling the then-recent calamity ending the Tiananmen Square protests of 1989, I pulled a Nancy Reagan and just said “No.”

I pointed out that the PRC was still run by a totalitarian, authoritarian regime, and that you just couldn’t trust those guys. You never knew when they were going to decide to sacrifice you on the altar of internal politics.

Today, American corporations are seeing the mistakes they made in pursuit of Chinese business, which like Robert Southey’s chickens, are coming home to roost. In 2015, Chinese Premier Li Keqiang announced the “Made in China 2025” plan to make China the World’s technology leader. It quickly became apparent that Mao’s current successor, Xi Jinping intends to achieve his goals by building on technology pilfered from western companies who’d naively partnered with Chinese firms.

Now, their only protector is another authoritarian-friendly president, Donald Trump. Remember it was Trump who, following his ill-advised summit with North Korean strongman Kim Jong Un, got caught on video enviously saying: “He speaks, and his people sit up at attention. I want my people to do the same.

So, now these corporations have to look to an American would-be dictator for protection from an entrenched Chinese dictator. No wonder they find themselves screwed, blued, and tattooed!

Governments are not immune to the PRC’s siren song, either. Pundits are pointing out that the PRC’s vaunted “One Belt, One Road” initiative is likely an example of “debt-trap diplomacy.”

Debt-trap diplomacy is a strategy similar to organized crime’s loan-shark operations. An unscrupulous cash-rich organization, the loan shark, offers funds to a cash-strapped individual, such as an ambitious entrepreneur, in a deal that seems too good to be true. It’s NOT true because the deal comes in the form of a loan at terms that nearly guarantee that the debtor will default. The shark then offers to write off the debt in exchange for the debtor’s participation in some unsavory scheme, such as money laundering.

In the debt-trap diplomacy version, the PRC stands in the place of the loan shark while some emerging-economy nation, such as, say, Malaysia, accepts the unsupportable debt. In the PRC/ Malaysia case, the unsavory scheme is helping support China’s imperial ambitions in the western Pacific.

Earlier this month, Malaysia wisely backed out of the deal.

It’s not just the post-Maoist PRC that makes a dangerous place for western corporations to do business, authoritarians all over the world treat people like Heart’s Barracuda. They suck you in with mesmerizing bright and shiny promises, then leave you twisting in the wind.

Yes, I’ve piled up a whole mess of mixed metaphors here, but I’m trying to drive home a point!

Another example of the traps business people can get into by trying to deal with authoritarians is afforded by Danske Bank’s Estonia branch and their dealings with Vladimir Putin‘s Russian kleptocracy. Danske Bank is a Danish financial institution with a pan-European footprint and global ambitions. Recent release of a Danske Bank internal report produced by the Danish law firm Bruun & Hjejle says that the Estonia branch engaged in “dodgy dealings” with numerous corrupt Russian officials. Basically, the bank set up a scheme to launder money stolen from Russian tax receipts by organized criminals.

The scandal broke in Russia in June of 2007 when dozens of police officers raided the Moscow offices of Hermitage Global, an activist fund focused on global emerging markets. A coverup by Kremlin authorities resulted in the death (while in a Russian prison) of Sergei Leonidovich Magnitsky, a Russian tax accountant who specialized in anti-corruption activities.

Magnitsky’s case became an international cause célèbre. The U.S. Congress and President Barack Obama enacted the Magnitsky Act at the end of 2012, barring, among others, those Russian officials believed to be involved in Magnitsky’s death from entering the United States or using its banking system.

Apparently, the purpose of the infamous Trump Tower meeting of June 9, 2016 was, on the Russian side, an effort to secure repeal of the Magnitsky Act should then-candidate Trump win the election. The Russians dangled release of stolen emails incriminating Trump-rival Hillary Clinton as bait. This activity started the whole Mueller Investigation, which has so far resulted in dozens of indictments for federal crimes, and at least eight guilty pleas or convictions.

The latest business strung up in this mega-scandal was the whole corrupt banking system of Cyprus, whose laundering of Russian oligarchs’ money amounted to over $20B.

The moral of this story is: Don’t do business with bad guys, no matter how good they make the deal look.

The Future Role of AI in Fact Checking

Reality Check
Advanced computing may someday help sort fact from fiction. Gustavo Frazao/Shutterstock

8 August 2018 – This guest post is contributed under the auspices of Trive, a global, decentralized truth-discovery engine. Trive seeks to provide news aggregators and outlets a superior means of fact checking and news-story verification.

by Barry Cousins, Guest Blogger, Info-Tech Research Group

In a recent project, we looked at blockchain startup Trive and their pursuit of a fact-checking truth database. It seems obvious and likely that competition will spring up. After all, who wouldn’t want to guarantee the preservation of facts? Or, perhaps it’s more that lots of people would like to control what is perceived as truth.

With the recent coming out party of IBM’s Debater, this next step in our journey brings Artificial Intelligence into the conversation … quite literally.

As an analyst, I’d like to have a universal fact checker. Something like the carbon monoxide detectors on each level of my home. Something that would sound an alarm when there’s danger of intellectual asphyxiation from choking on the baloney put forward by certain sales people, news organizations, governments, and educators, for example.

For most of my life, we would simply have turned to academic literature for credible truth. There is now enough legitimate doubt to make us seek out a new model or, at a minimum, to augment that academic model.

I don’t want to be misunderstood: I’m not suggesting that all news and education is phony baloney. And, I’m not suggesting that the people speaking untruths are always doing so intentionally.

The fact is, we don’t have anything close to a recognizable database of facts from which we can base such analysis. For most of us, this was supposed to be the school system, but sadly, that has increasingly become politicized.

But even if we had the universal truth database, could we actually use it? For instance, how would we tap into the right facts at the right time? The relevant facts?

If I’m looking into the sinking of the Titanic, is it relevant to study the facts behind the ship’s manifest? It might be interesting, but would it prove to be relevant? Does it have anything to do with the iceberg? Would that focus on the manifest impede my path to insight on the sinking?

It would be great to have Artificial Intelligence advising me on these matters. I’d make the ultimate decision, but it would be awesome to have something like the Star Trek computer sifting through the sea of facts for that which is relevant.

Is AI ready? IBM recently showed that it’s certainly coming along.

Is the sea of facts ready? That’s a lot less certain.

Debater holds its own

In June 2018, IBM unveiled the latest in Artificial Intelligence with Project Debater in a small event with two debates: “we should subsidize space exploration”, and “we should increase the use of telemedicine”. The opponents were credentialed experts, and Debater was arguing from a position established by “reading” a large volume of academic papers.

The result? From what we can tell, the humans were more persuasive while the computer was more thorough. Hardly surprising, perhaps. I’d like to watch the full debates but haven’t located them yet.

Debater is intended to help humans enhance their ability to persuade. According to IBM researcher Ranit Aharanov, “We are actually trying to show that a computer system can add to our conversation or decision making by bringing facts and doing a different kind of argumentation.”

So this is an example of AI. I’ve been trying to distinguish between automation and AI, machine learning, deep learning, etc. I don’t need to nail that down today, but I’m pretty sure that my definition of AI includes genuine cognition: the ability to identify facts, comb out the opinions and misdirection, incorporate the right amount of intention bias, and form decisions and opinions with confidence while remaining watchful for one’s own errors. I’ll set aside any obligation to admit and react to one’s own errors, choosing to assume that intelligence includes the interest in, and awareness of, one’s ability to err.

Mark Klein, Principal Research Scientist at M.I.T., helped with that distinction between computing and AI. “There needs to be some additional ability to observe and modify the process by which you make decisions. Some call that consciousness, the ability to observe your own thinking process.”

Project Debater represents an incredible leap forward in AI. It was given access to a large volume of academic publications, and it developed its debating chops through machine learning. The capability of the computer in those debates resembled the results that humans would get from reading all those papers, assuming you can conceive of a way that a human could consume and retain that much knowledge.

Beyond spinning away on publications, are computers ready to interact intelligently?

Artificial? Yes. But, Intelligent?

According to Dr. Klein, we’re still far away from that outcome. “Computers still seem to be very rudimentary in terms of being able to ‘understand’ what people say. They (people) don’t follow grammatical rules very rigorously. They leave a lot of stuff out and rely on shared context. They’re ambiguous or they make mistakes that other people can figure out. There’s a whole list of things like irony that are completely flummoxing computers now.”

Dr. Klein’s PhD in Artificial Intelligence from the University of Illinois leaves him particularly well-positioned for this area of study. He’s primarily focused on using computers to enable better knowledge sharing and decision making among groups of humans. Thus, the potentially debilitating question of what constitutes knowledge, what separates fact from opinion from conjecture.

His field of study focuses on the intersection of AI, social computing, and data science. A central theme involves responsibly working together in a structured collective intelligence life cycle: Collective Sensemaking, Collective Innovation, Collective Decision Making, and Collective Action.

One of the key outcomes of Klein’s research is “The Deliberatorium”, a collaboration engine that adds structure to mass participation via social media. The system ensures that contributors create a self-organized, non-redundant summary of the full breadth of the crowd’s insights and ideas. This model avoids the risks of ambiguity and misunderstanding that impede the success of AI interacting with humans.

Klein provided a deeper explanation of the massive gap between AI and genuine intellectual interaction. “It’s a much bigger problem than being able to parse the words, make a syntax tree, and use the standard Natural Language Processing approaches.”

Natural Language Processing breaks up the problem into several layers. One of them is syntax processing, which is to figure out the nouns and the verbs and figure out how they’re related to each other. The second level is semantics, which is having a model of what the words mean. That ‘eat’ means ‘ingesting some nutritious substance in order to get energy to live’. For syntax, we’re doing OK. For semantics, we’re doing kind of OK. But the part where it seems like Natural Language Processing still has light years to go is in the area of what they call ‘pragmatics’, which is understanding the meaning of something that’s said by taking into account the cultural and personal contexts of the people who are communicating. That’s a huge topic. Imagine that you’re talking to a North Korean. Even if you had a good translator there would be lots of possibility of huge misunderstandings because your contexts would be so different, the way you try to get across things, especially if you’re trying to be polite, it’s just going to fly right over each other’s head.”

To make matters much worse, our communications are filled with cases where we ought not be taken quite literally. Sarcasm, irony, idioms, etc. make it difficult enough for humans to understand, given the incredible reliance on context. I could just imagine the computer trying to validate something that starts with, “John just started World War 3…”, or “Bonnie has an advanced degree in…”, or “That’ll help…”

A few weeks ago, I wrote that I’d won $60 million in the lottery. I was being sarcastic, and (if you ask me) humorous in talking about how people decide what’s true. Would that research interview be labeled as fake news? Technically, I suppose it was. Now that would be ironic.

Klein summed it up with, “That’s the kind of stuff that computers are really terrible at and it seems like that would be incredibly important if you’re trying to do something as deep and fraught as fact checking.”

Centralized vs. Decentralized Fact Model

It’s self-evident that we have to be judicious in our management of the knowledge base behind an AI fact-checking model and it’s reasonable to assume that AI will retain and project any subjective bias embedded in the underlying body of ‘facts’.

We’re facing competing models for the future of truth, based on the question of centralization. Do you trust yourself to deduce the best answer to challenging questions, or do you prefer to simply trust the authoritative position? Well, consider that there are centralized models with obvious bias behind most of our sources. The tech giants are all filtering our news and likely having more impact than powerful media editors. Are they unbiased? The government is dictating most of the educational curriculum in our model. Are they unbiased?

That centralized truth model should be raising alarm bells for anyone paying attention. Instead, consider a truly decentralized model where no corporate or government interest is influencing the ultimate decision on what’s true. And consider that the truth is potentially unstable. Establishing the initial position on facts is one thing, but the ability to change that view in the face of more information is likely the bigger benefit.

A decentralized fact model without commercial or political interest would openly seek out corrections. It would critically evaluate new knowledge and objectively re-frame the previous position whenever warranted. It would communicate those changes without concern for timing, or for the social or economic impact. It quite simply wouldn’t consider or care whether or not you liked the truth.

The model proposed by Trive appears to meet those objectivity criteria and is getting noticed as more people tire of left-vs-right and corporatocracy preservation.

IBM Debater seems like it would be able to engage in critical thinking that would shift influence towards a decentralized model. Hopefully, Debater would view the denial of truth as subjective and illogical. With any luck, the computer would confront that conduct directly.

IBM’s AI machine already can examine tactics and style. In a recent debate, it coldly scolded the opponent with: “You are speaking at the extremely fast rate of 218 words per minute. There is no need to hurry.”

Debater can obviously play the debate game while managing massive amounts of information and determining relevance. As it evolves, it will need to rely on the veracity of that information.

Trive and Debater seem to be a complement to each other, so far.

Author BioBarry Cousins

Barry Cousins, Research Lead, Info-Tech Research Group specializing in Project Portfolio Management, Help/Service Desk, and Telephony/Unified Communications. He brings an extensive background in technology, IT management, and business leadership.

About Info-Tech Research Group

Info-Tech Research Group is a fast growing IT research and advisory firm. Founded in 1997, Info-Tech produces unbiased and highly relevant IT research solutions. Since 2010 McLean & Company, a division of Info-Tech, has provided the same, unmatched expertise to HR professionals worldwide.

Who’s NOT a Creative?

 

Compensting sales
Close-up Of A Business Woman Giving Cheque To Her Colleague At Workplace In Office. Andrey Popov/Shutterstock

25 July 2018 – Last week I made a big deal about the things that motivate creative people, such as magazine editors, and how the most effective rewards were non-monetary. I also said that monetary rewards, such as commissions based on sales results, were exactly the right rewards to use for salespeople. That would imply that salespeople were somehow different from others, and maybe even not creative.

That is not the impression I want to leave you with. I’m devoting this blog posting to setting that record straight.

My remarks last week were based on Maslow‘s and Herzberg‘s work on motivation of employees. I suggested that these theories were valid in other spheres of human endeavor. Let’s be clear about this: yes, Maslow’s and Herzberg’s theories are valid and useful in general, whenever you want to think about motivating normal, healthy human beings. It’s incidental that those researchers were focused on employer/employee relations as an impetus to their work. If they’d been focused on anything else, their conclusions would probably have been pretty much the same.

That said, there are a whole class of people for whom monetary compensation is the holy grail of motivators. They are generally very high functioning individuals who are in no way pathological. On the surface, however, their preferred rewards appear to be monetary.

Traditionally, observers who don’t share this reward system have indicted these individuals as “greedy.”

I, however, dispute that conclusion. Let me explain why.

When pointing out the rewards that can be called “motivators for editors,” I wrote:

“We did that by pointing out that they belonged to the staff of a highly esteemed publication. We talked about how their writings helped their readers excel at their jobs. We entered their articles in professional competitions with awards for things like ‘Best Technical Article.’ Above all, we talked up the fact that ours was ‘the premier publication in the market.'”

Notice that these rewards, though non-monetary. were more or less measurable. They could be (and indeed for the individuals they motivated) seen as scorecards. The individuals involved had a very clear idea of value attached to such rewards. A Nobel Prize in Physics is of greater value than, say, a similar award given by, say, Harvard University.

For example, in 1987 I was awarded the “Cahners Editorial Medal of Excellence, Best How-To Article.” That wasn’t half bad. The competition was articles written for a few dozen magazines that were part of the Cahners Publishing Company, which at the time was a big deal in the business-to-business magazine field.

What I considered to be of higher value, however, was the “First Place Award For Editorial Excellence for a Technical Article in a Magazine with Over 80,000 Circulation” I got in 1997 from the American Society of Business Press Editors, where I was competing with a much wider pool of journalists.

Economists have a way of attempting to quantify such non-monetary awards called utility. They arrive at values by presenting various options and asking the question: “Which would you rather have?”

Of course, measures of utility generally vary widely depending on who’s doing the choosing.

For example, an article in the 19 July The Wall Street Journal described a phenomenon the author seemed to think was surprising: Saudi-Arabian women drivers (new drivers all) showed a preference for muscle cars over more pedestrian models. The author, Margherita Stancati, related an incident where a Porche salesperson in Riyadh offered a recently minted woman driver an “easy to drive crossover designed to primarily attract women.” The customer demurred. She wanted something “with an engine that roars.”

So, the utility of anything is not an absolute in any sense. It all depends on answering the question: “Utility to whom?”

Everyone is motivated by rewards in the upper half of the Needs Pyramid. If you’re a salesperson, growth in your annual (or other period) sales revenue is in the green Self Esteem block. It’s well and truly in the “motivator” category, and has nothing to do with the Safety and Security “hygiene factor” where others might put it. Successful salespeople have those hygiene factors well-and-truly covered. They’re looking for a reward that tells them they’ve hit a home run. That is likely having a bigger annual bonus than the next guy.

The most obvious money-driven motivators accrue to the folks in the CEO ranks. Jeff Bezos, Elon Musk, and Warren Buffett would have a hard time measuring their success (i.e., hitting the Pavlovian lever to get Self Actualization rewards) without looking at their monetary compensation!

The Pyramid of Needs

Needs Pyramid
The Pyramid of Needs combines Maslow’s and Herzberg’s motivational theories.

18 July 2018 – Long, long ago, in a [place] far, far away. …

When I was Chief Editor at business-to-business magazine Test & Measurement World, I had a long, friendly though heated, discussion with one of our advertising-sales managers. He suggested making the compensation we paid our editorial staff contingent on total advertising sales. He pointed out that what everyone came to work for was to get paid, and that tying their pay to how well the magazine was doing financially would give them an incentive to make decisions that would help advertising sales, and advance the magazine’s financial success.

He thought it was a great idea, but I disagreed completely. I pointed out that, though revenue sharing was exactly the right way to compensate the salespeople he worked with, it was exactly the wrong way to compensate creative people, like writers and journalists.

Why it was a good idea for his salespeople I’ll leave for another column. Today, I’m interested in why it was not a good idea for my editors.

In the heat of the discussion I didn’t do a deep dive into the reasons for taking my position. Decades later, from the standpoint of a semi-retired whatever-you-call-my-patchwork-career, I can now sit back and analyze in some detail the considerations that led me to my conclusion, which I still think was correct.

We’ll start out with Maslow’s Hierarchy of Needs.

In 1943, Abraham Maslow proposed that healthy human beings have a certain number of needs, and that these needs are arranged in a hierarchy. At the top is “self actualization,” which boils down to a need for creativity. It’s the need to do something that’s never been done before in one’s own individual way. At the bottom is the simple need for physical survival. In between are three more identified needs people also seek to satisfy.

Maslow pointed out that people seek to satisfy these needs from the bottom to the top. For example, nobody worries about security arrangements at their gated community (second level) while having a heart attack that threatens their survival (bottom level).

Overlaid on Maslow’s hierarchy is Frederick Herzberg’s Two-Factor Theory, which he published in his 1959 book The Motivation to Work. Herzberg’s theory divides Maslow’s hierarchy into two sections. The lower section is best described as “hygiene factors.” They are also known as “dissatisfiers” or “demotivators” because if they’re not met folks get cranky.

Basically, a person needs to have their hygiene factors covered in order have a level of basic satisfaction in life. Not having any of these needs satisfied makes them miserable. Having them satisfied doesn’t motivate them at all. It makes ’em fat, dumb and happy.

The upper-level needs are called “motivators.” Not having motivators met drives an individual to work harder, smarter, etc. It energizes them.

My position in the argument with my ad-sales friend was that providing revenue sharing worked at the “Safety and Security” level. Editors were (at least in my organization) paid enough that they didn’t have to worry about feeding their kids and covering their bills. They were talented people with a choice of whom they worked for. If they weren’t already being paid enough, they’d have been forced to go work for somebody else.

Creative people, my argument went, are motivated by non-monetary rewards. They work at the upper “motivator” levels. They’ve already got their physical needs covered, so to motivate them we have to offer rewards in the “motivator” realm.

We did that by pointing out that they belonged to the staff of a highly esteemed publication. We talked about how their writings helped their readers excel at their jobs. We entered their articles in professional competitions with awards for things like “Best Technical Article.” Above all, we talked up the fact that ours was “the premier publication in the market.”

These were all non-monetary rewards to motivate people who already had their basic needs (the hygiene factors) covered.

I summarized my compensation theory thusly: “We pay creative people enough so that they don’t have to go do something else.”

That gives them the freedom to do what they would want to do, anyway. The implication is that creative people want to do stuff because it’s something they can do that’s worth doing.

In other words, we don’t pay creative people to work. We pay them to free them up so they can work. Then, we suggest really fun stuff for them to work at.

What does this all mean for society in general?

First of all, if you want there to be a general level of satisfaction within your society, you’d better take care of those hygiene factors for everybody!

That doesn’t mean the top 1%. It doesn’t mean the top 80%, either. Or, the top 90%. It means everybody!

If you’ve got 99% of everybody covered, that still leaves a whole lot of people who think they’re getting a raw deal. Remember that in the U.S.A. there are roughly 300 million people. If you’ve left 1% feeling ripped off, that’s 3 million potential revolutionaries. Three million people can cause a lot of havoc if motivated.

Remember, at the height of the 1960s Hippy movement, there were, according to the most generous estimates, only about 100,000 hipsters wandering around. Those hundred-thousand activists made a huge change in society in a very short period of time.

Okay. If you want people invested in the status quo of society, make sure everyone has all their hygiene factors covered. If you want to know how to do that, ask Bernie Sanders.

Assuming you’ve got everybody’s hygiene factors covered, does that mean they’re all fat, dumb, and happy? Do you end up with a nation of goofballs with no motivation to do anything?

Nope!

Remember those needs Herzberg identified as “motivators” in the upper part of Maslow’s pyramid?

The hygiene factors come into play only when they’re not met. The day they’re met, people stop thinking about who’ll be first against the wall when the revolution comes. Folks become fat, dumb and happy, and stay that way for about an afternoon. Maybe an afternoon and an evening if there’s a good ballgame on.

The next morning they start thinking: “So, what can we screw with next?”

What they’re going to screw with next is anything and everything they damn well please. Some will want to fly to the Moon. Some will want to outdo Michaelangelo‘s frescos for the ceiling of the Sistine Chapel. They’re all going to look at what they think was the greatest stuff from the past, and try to think of ways to do better, and to do it in their own way.

That’s the whole point of “self actualization.”

The Renaissance didn’t happen because everybody was broke. It happened because they were already fat, dumb and happy, and looking for something to screw with next.

POTUS and the Peter Principle

Will Rogers & Wiley Post
In 1927, Will Rogers wrote: “I never met a man I didn’t like.” Here he is (on left) posing with aviator Wiley Post before their ill-fated flying exploration of Alaska. Everett Historical/Shutterstock

11 July 2018 – Please bear with me while I, once again, invert the standard news-story pyramid by presenting a great whacking pile of (hopfully entertaining) detail that leads eventually to the point of this column. If you’re too impatient to read it to the end, leave now to check out the latest POTUS rant on Twitter.

Unlike Will Rogers, who famously wrote, “I never met a man I didn’t like,” I’ve run across a whole slew of folks I didn’t like, to the point of being marginally misanthropic.

I’ve made friends with all kinds of people, from murderers to millionaires, but there are a few types that I just can’t abide. Top of that list is people that think they’re smarter than everybody else, and want you to acknowledge it.

I’m telling you this because I’m trying to be honest about why I’ve never been able to abide two recent Presidents: William Jefferson Clinton (#42) and Donald J. Trump (#45). Having been forced to observe their antics over an extended period, I’m pleased to report that they’ve both proved to be among the most corrupt individuals to occupy the Oval Office in recent memory.

I dislike them because they both show that same, smarmy self-satisfied smile when contemplating their own greatness.

Tricky Dick Nixon (#37) was also a world-class scumbag, but he never triggered the same automatic revulsion. That is because, instead of always looking self satisfied, he always looked scared. He was smart enough to recognize that he was walking a tightrope and, if he stayed on it long enough, he eventually would fall off.

And, he did.

I had no reason for disliking #37 until the mid-1960s, when, as a college freshman, I researched a paper for a history class that happened to involve digging into the McCarthy hearings of the early 1950s. Seeing the future #37’s activities in that period helped me form an extremely unflattering picture of his character, which a decade later proved accurate.

During those years in between I had some knock-down, drag-out arguments with my rabid-Nixon-fan grandmother. I hope I had the self control never to have said “I told you so” after Nixon’s fall. She was a nice lady and a wonderful grandma, and wouldn’t have deserved it.

As Abraham Lincoln (#16) famously said: “You can fool all the people some of the time, and some of the people all the time, but you cannot fool all the people all the time.”

Since #45 came on my radar many decades ago, I’ve been trying to figure out what, exactly, is wrong with his brain. At first, when he was a real-estate developer, I just figured he had bad taste and was infantile. That made him easy to dismiss, so I did just that.

Later, he became a reality-TV star. His show, The Apprentice, made it instantly clear that he knew absolutely nothing about running a business.

No wonder his companies went bankrupt. Again, and again, and again….

I’ve known scads of corporate CEOs over the years. During the quarter century I spent covering the testing business as a journalist, I got to spend time with most of the corporate leaders of the world’s major electronics manufacturing companies. Unsurprisingly, the successful ones followed the best practices that I learned in MBA school.

Some of the CEOs I got to know were goofballs. Most, however, were absolutely brilliant. The successful ones all had certain things in common.

Chief among the characteristics of successful corporate executives is that they make the people around them happy to work for them. They make others feel comfortable, empowered, and enthusiastically willing to cooperate to make the CEO’s vision manifest.

Even Commendatore Ferrari, who I’ve heard was Hell to work for and Machiavellian in interpersonal relationships, made underlings glad to have known him. I’ve noticed that ‘most everybody who’s ever worked for Ferrari has become a Ferrari fan for life.

As far as I can determine, nobody ever sued him.

That’s not the impression I got of Donald Trump, the corporate CEO. He seemed to revel in conflict, making those around him feel like dog pooh.

Apparently, everyone who’s ever dealt with him has wanted to sue him.

That worked out fine, however, for Donald Trump, the reality-TV star. So-called “reality” TV shows generally survive by presenting conflict. The more conflict the better. Everybody always seems to be fighting with everybody else, and the winners appear to be those who consistently bully their opponents into feeling like dog pooh.

I see a pattern here.

The inescapable conclusion is that Donald Trump was never a successful corporate executive, but succeeded enormously playing one on TV.

Another characteristic I should mention of reality TV shows is that they’re unscripted. The idea seems to be that nobody knows what’s going to happen next, including the cast.

That leaves off the necessity for reality-TV stars to learn lines. Actual movie stars and stage actors have to learn lines of dialog. Stories are tightly scripted so that they conform to Aristotle’s recommendations for how to write a successful plot.

Having written a handful of traditional motion-picture scripts as well as having produced a few reality-TV episodes, I know the difference. Following Aristotle’s dicta gives you the ability to communicate, and sometimes even teach, something to your audience. The formula reality-TV show, on the other hand, goes nowhere. Everybody (including the audience) ends up exactly where they started, ready to start the same stupid arguments over and over again ad nauseam.

Apparently, reality-TV audiences don’t want to actually learn anything. They’re more focused on ranting and raving.

Later on, following a long tradition among theater, film and TV stars, #45 became a politician.

At first, I listened to what he said. That led me to think he was a Nazi demagogue. Then, I thought maybe he was some kind of petty tyrant, like Mussolini. (I never considered him competent enough to match Hitler.)

Eventually, I realized that it never makes any sense to listen to what #45 says because he lies. That makes anything he says irrelevant.

FIRST PRINCIPAL: If you catch somebody lying to you, stop believing what they say.

So, it’s all bullshit. You can’t draw any conclusion from it. If he says something obviously racist (for example), you can’t conclude that he’s a racist. If he says something that sounds stupid, you can’t conclude he’s stupid, either. It just means he’s said something that sounds stupid.

Piling up this whole load of B.S., then applying Occam’s Razor, leads to the conclusion that #45 is still simply a reality-TV star. His current TV show is titled The Trump Administration. Its supporting characters are U.S. senators and representatives, executive-branch bureaucrats, news-media personalities, and foreign “dignitaries.” Some in that last category (such as Justin Trudeau and Emmanuel Macron) are reluctant conscripts into the cast, and some (such as Vladimir Putin and Kim Jong-un) gleefully play their parts, but all are bit players in #45’s reality TV show.

Oh, yeah. The largest group of bit players in The Trump Administration is every man, woman, child and jackass on the planet. All are, in true reality-TV style, going exactly nowhere as long as the show lasts.

Politicians have always been showmen. Of the Founding Fathers, the one who stands out for never coming close to becoming President was Benjamin Franklin. Franklin was a lot of things, and did a lot of things extremely well. But, he was never really a P.T.-Barnum-like showman.

Really successful politicians, such as Abraham Lincoln, Franklin Roosevelt (#32), Bill Clinton, and Ronald Reagan (#40) were showmen. They could wow the heck out of an audience. They could also remember their lines!

That brings us, as promised, to Donald Trump and the Peter Principle.

Recognizing the close relationship between Presidential success and showmanship gives some idea about why #45 is having so much trouble making a go of being President.

Before I dig into that, however, I need to point out a few things that #45 likes to claim as successes that actually aren’t:

  • The 2016 election was not really a win for Donald Trump. Hillary Clinton was such an unpopular candidate that she decisively lost on her own (de)merits. God knows why she was ever the Democratic Party candidate at all. Anybody could have beaten her. If Donald Trump hadn’t been available, Elmer Fudd could have won!
  • The current economic expansion has absolutely nothing to do with Trump policies. I predicted it back in 2009, long before anybody (with the possible exception of Vladimir Putin, who apparently engineered it) thought Trump had a chance of winning the Presidency. My prediction was based on applying chaos theory to historical data. It was simply time for an economic expansion. The only effect Trump can have on the economy is to screw it up. Being trained as an economist (You did know that, didn’t you?), #45 is unlikely to screw up so badly that he derails the expansion.
  • While #45 likes to claim a win on North Korean denuclearization, the Nobel Peace Prize is on hold while evidence piles up that Kim Jong-un was pulling the wool over Trump’s eyes at the summit.

Finally, we move on to the Peter Principle.

In 1969 Canadian writer Raymond Hull co-wrote a satirical book entitled The Peter Principle with Laurence J. Peter. It was based on research Peter had done on organizational behavior.

Peter was (he died at age 70 in 1990) not a management consultant or a behavioral psychologist. He was an Associate Professor of Education at the University of Southern California. He was also Director of the Evelyn Frieden Centre for Prescriptive Teaching at USC, and Coordinator of Programs for Emotionally Disturbed Children.

The Peter principle states: “In a hierarchy every employee tends to rise to his level of incompetence.”

Horrifying to corporate managers, the book went on to provide real examples and lucid explanations to show the principle’s validity. It works as satire only because it leaves the reader with a choice either to laugh or to cry.

See last week’s discussion of why academic literature is exactly the wrong form with which to explore really tough philosophical questions in an innovative way.

Let’s be clear: I’m convinced that the Peter principle is God’s Own Truth! I’ve seen dozens of examples that confirm it, and no counter examples.

It’s another proof that Mommy Nature has a sense of humor. Anyone who disputes that has, philosophically speaking, a piece of paper taped to the back of his (or her) shirt with the words “Kick Me!” written on it.

A quick perusal of the Wikipedia entry on the Peter Principle elucidates: “An employee is promoted based on their success in previous jobs until they reach a level at which they are no longer competent, as skills in one job do not necessarily translate to another. … If the promoted person lacks the skills required for their new role, then they will be incompetent at their new level, and so they will not be promoted again.”

I leave it as an exercise for the reader (and the media) to find the numerous examples where #45, as a successful reality-TV star, has the skills he needed to be promoted to President, but not those needed to be competent in the job.