You Want Me to Pay You … Why?

Fed Funds Rate goes negative
Negative rates burn wealth! ramcreations/Shutterstock

14 August 2019 – There’s been some hand wringing in the mass media recently about negative interest rates and what they mean. Before you can think about that, however, you have to know what negative rates are and how they actually work. Journalists Sam Goldfarb and Daniel Kruger pointed out in a Wall Street Journal article on Monday (8/12) that not so long ago negative interest rates were thought impossible.

Of course, negative interest rates were never really “impossible.” They used to be considered highly unlikely, however, because nobody in their right mind would be willing to pay someone else for taking money off their hands. I mean, would you do it?

But, the world has changed drastically over the past, say, quarter century. Today, so-called “investors” think nothing of buying stock in giant technology companies, such as Tesla, Inc. that have never made a dime of profit and have no prospects of doing so in the near future. Such “investors” are effectively giving away their money at negative interest rates.

Buying stock in an unprofitable enterprise makes sense if you believe that the enterprise will eventually become profitable. Or, and this is a commonly applied strategy, you believe the market value of the stock will rise in the future, when you can sell it to somebody else at a profit. This latter strategy is known as the “bigger fool theory.” This theory holds that doing something that stupid is a good idea as long as you believe you’ll be able to find a “bigger fool” to take your stock in the deadbeat enterprise off your hands before it collapses into bankruptcy.

That all works quite nicely for stocks, but makes less sense for bonds, which is what folks are talking about when they wring their hands over negative-interest-rate policy by central banks. The difference is that in the bond market, there really is no underlying enterprise ownership that might turn a profit in the future. A bond is just an agreement between a lender and a debtor.

This is where the two-fluid model of money I trotted out in this column on 19 June helps paint an understandable picture. Recall from that column that money appears from nowhere when two parties, a lender and a debtor, execute a loan contract. The cash (known as “credit” in the model) goes to the debtor while an equal amount of debt goes to the lender. Those are the two paired “fluids” that make up what we call “money,” as I explain in that column.

Fed Funds Rate

The Federal Reserve Bank is a system of banks run by the U.S. Treasury Department. One of the system’s functions is to ensure the U.S. money supply by holding excess money for other banks who have more than they need at the moment, and loaning it out to banks in need of cash. By setting the interest rate (the so-called Fed Funds Rate) at which these transactions occur, the Fed controls how much money flows through the economy. Lowering the rate allows money to flow faster. Raising it slows things down.

Actual paper money represents only a tiny fraction of U.S. currency. In actual fact, money is created whenever anybody borrows anything from anybody, even your average loan shark. The Federal Reserve System is how the U.S. Federal Government attempts to keep the whole mess under control.

By the way, the problem with cryptocurrencies is that they attempt to usurp that control, but that’s a rant for another day.

Think of money as blood coursing through the country’s economic body, carrying oxygen to the cells (you and me and General Motors) that they use to create wealth. That’s when the problem with negative interest rates shows up. When interest rates are positive, it means wealth is being created. When they’re negative, well you can imagine what that means!

Negative interest rates mean folks are burning up wealth to keep the economic ship sailing along. If you keep burning up wealth instead of creating it, eventually you go broke. Think Venezuela, or, on a smaller scale, Puerto Rico.

Negative Interest

Okay, so how do negative interest rates actually work?

A loan contract, or bond, is an agreement between a lender and a debtor to create some money (the two fluids, again). The idea behind any contract is that everybody gets something out of it that they want. In a conventional positive-interest-rate bond, the debtor gets credit that they can use to create wealth, like, maybe building a house. The lender gets a share in that wealth in the form of interest payments over and above the cash needed to retire the loan (as in pay back the principal).

Bonds are sold in an auction process. That is, the issuer offers to sell the bond for a face value (the principal) and pay it back plus interest at a certain rate in the future. In the real world, however, folks buy such bonds at a market price, which may or may not be equal to the principal.

If the market price is lower than the principal, then the effective rate of interest will be higher than the offered rate because what the actual market value is doesn’t affect the pay-back terms written on the loan agreement. If the market price is higher than the principal, the effective rate will be lower than the offered rate. If the market price is too much higher than the principal, the repayment won’t be enough to cover it, and the effective rate will be negative.

Everyone who’s ever participated in an auction knows that there are always amateurs around (or supposed professionals whose glands get the better of their brains so they act like amateurs) who get caught up in the auction dynamics and agree to pay more than they should for what’s offered. When it’s a bond auction, that’s how you get a negative interest rate by accident. Folks agree to pay up front more than they get back as principal plus interest for the loan.

Negative Interest Rate Policy (NIRP) is when a central bank (such as the U.S. Federal Reserve) runs out of options to control economic activity, and publicly says it’s going to borrow money from its customers at negative rates. The Fed’s customers (the large banks that deposit their excess cash with the Fed) have to put their excess cash somewhere, so they get stuck making the negative-interest-rate loans. That means they’re burning up the wealth their customers share with them when they pay their loans back.

If you’re the richest country in the world, you can get away with burning up wealth faster than you create it for a very long time. If, on the other hand, you’re, say, Puerto Rico, you can’t.

Measuring Project Success

 

 

Motorcycle ride
What counts as success depends on what your goals are. By Andrey Armyagov/Shutterstock

7 August 2019 – As part of my research into diversity in project teams, I’ve spent about a week digging into how it’s possible to quantify success. Most people equate personal success with income or wealth, and business success with profitability or market capitalization, but none of that really does it. Veteran project managers (like yours truly) recognize that it’s almost never about money. If you do everything else right, money just shows up sometimes. What it’s really all about is all those other things that go into making a success of some project.

So, measuring success is all about quantifying all those other things. Those other things are whatever is important to all the folks that your project affects. We call them stakeholders because they have a stake in the project’s outcome.

For example, some years ago it started becoming obvious to me that the boat tied up to the dock out back was doing me no good because I hardly ever took it out. I knew that I’d get to use a motorcycle every day if I had one, but I had that stupid boat instead. So, I conceived of a project to replace the boat with a motorcycle.

I wasn’t alone, however. Whether we had a boat or a motorcycle would make a difference to my wife, as well. She had a stake in whether we had a boat or a motorcycle, so she was also a stakeholder. It turned out that she would also prefer to have a motorcycle than a boat, so we started working on a project to replace the boat with a motorcycle.

So, the first thing to consider when planning a project is who the stakeholders are. The next thing to consider is what each stakeholder wants to get out of the project. In the case of the motorcycle project, what my wife wanted to get out of it was the fun of riding around southwest Florida visiting this, that and the other place. It turned out that the places she wanted to go were mostly easier to get to by motorcycle than by boat. So, her goal wasn’t just to have the motorcycle, it was to visit places she could get to by motorcycle. For her, getting to visit those places would fulfill her goal for the project.

See? There was no money involved. Only an intangible thing of being able to visit someplace.

The “intangible” part is what hangs people up when they want to quantify the value of something. It’s why people get hung up on money-related goals. Money is something everyone knows how to quantify. How do you quantify the value of “getting to go somewhere?”

A lot of people have tried a lot of schemes for “measuring” the “value” of some intangible thing, like getting where you want to go. It turns out, however, that it’s easy if you change your point of view just a little bit. Instead of asking how valuable it is to get there, you can ask something like: “What are the odds that I can get there?” Getting to some place five miles from the sea by boat likely isn’t going to happen, but getting there by motorcycle might be easy.

The way we quantify this is through what’s called a Likert scale. You make a statement, like “I can get there” and pick a number from, say, zero to five with zero being “It ain’t gonna happen” and five being “Easy k’neezie.”

You do that for all the places you’re likely to want to go and calculate an average score. If you really want to complete the job, you normalize your score by weighting the scores for each destination with how often you’re likely to want to go there, then divide by five times the sum of the weights. That leaves you with an index ranging from zero to one.

You go through this process for all of the goals of all your stakeholders and average the indices to get a composite index. This is an example of how one uses fuzzy logic, which takes into account that most of the time you can’t really be sure of anything. The fuzzy part is using the Likert scale to estimate how likely it is that your fuzzy statement (in this case, “I can get there”) will be true.

When using fuzzy logic to quantify project success, the fuzzy statements are of the form: “Stakeholder X’s goal Y is met.” The value assigned to that statement is the degree to which it is true, or, said another way, the degree to which the goal has been met. That allows for the prospect that not all stakeholder goals will be fully met.

For example, how well my wife’s goal of “Getting to Miromar Outlets in Estero, FL from our place in Naples” would be met depended a whole lot on the characteristics of the motorcycle. If the motorcycle is like the 1988 FLST light-touring bike I used to have, the value would be five. We used to ride that thing all day for weeks at a time! If, on the other hand, it’s like that ol’ 1986 XLH chopper, she might make it, but she wouldn’t be happy at the end (literally ’cause the seat was uncomfortable)! The value in that condition would be one or two. Of course, since Miromar is land locked, the value of keeping the boat would be zero.

So, the steps to quantifying project success are:

  1. Determine all goals of all stakeholders;
  2. Assign a relative importance (weight) to each stakeholder goal;
  3. Use a Likert scale to quantify the degree to which each stakeholder goal has been met;
  4. Normalize the scores to work out an index for each stakeholder goal;
  5. Form a critical success index (CSI) for the project as an average of the indices for the stakeholder goals.

Before you complain about that being an awful lot of math to go through just to figure out how well your project succeeded, recognize that you go through it in a haphazard way every time you do anything. Even if it’s just going to the bathroom, you start out with a goal and finish deciding how well you succeeded. Thinking about these steps just gives you half a chance to reach the correct conclusion.

Do the Math

Applied Math teacher
Throughout history, applied mathematics has been the key to human development. By Elnur/Shutterstock

31 July 2019 – Over the millennia that philosophers have been doing their philosophizing, a recurring theme has been the quest to come up with some simple definition of what sets humans apart from so-called “lower” animals. This is not just idle curiosity. From Aristotle on, folks have realized that understanding what makes us human is key to making the most of our humanity. If we don’t know who we are, how can we figure out how to be better?

In recent decades, however, it’s become clear that this is a fool’s errand. Such a definition of humanity doesn’t exist. Instead, what sets humans apart is a suite of characteristics, such as two eyes in the front of a head that’s set up on a stalk over a main torso, with two legs down below and a couple of arms on each side ending with wiggly fingers and opposable thumbs; a brain able to use sophisticated language; and so forth. Not every human has all of them (for example, I had a friend in Arizona who’d managed to lose his right arm and shoulder without losing his humanity) and a lot of non-humans have some of them (for example, chimpanzees use tools a lot). What marks humans as humans is having most of these characteristics, and what marks non-humans as not human is lacking a lot of them.

On the other hand, there is one thing that most humans are capable of that most non-humans aren’t: humans are capable of doing the math.

Yeah, crows can count past two. I hear that pigeons are good at pattern recognition. But, I’m talking about mathematical reasoning more sophisticated than counting past seven. That’s something most humans can do, and most other animals can’t.

Everybody has their mathematical limitations.Experience indicates that one’s mathematical limitations are mostly an issue of motivation. At some point, just about everybody decides that it’s just not worth putting in the effort needed to learn any more math than they already know.

That’s because learning math is hard. It’s the biggest learning challenge most of us ever face. Most of us give up long before reaching the limits of our innate ability to puzzle it out.

Luckily, there are some who are willing to push the limits, and master mathematical puzzles that no human has solved before. That’s lucky because without people like them, human progress would quickly stop.

Even better, those people are often willing – even anxious – to explain what they’ve puzzled out to the rest of us. For example, we have geometry because a bunch of Egyptians puzzled out how to design pyramids, stone temples and other stuff they wanted to build, then proudly explained to their peers exactly how to do it. We have double-entry accounting because folks in the Near East wanted to keep track of what they had, figured out how to do it, and taught others to help. We’ve got calculus because Sir Isaac Newton and a bunch of his buddies figured out how to predict what the visible planets would do next, then taught it to a bunch of physics students.

It’s what we like to call “Applied Mathematics,” and it’s responsible for most of the progress people have made since the days of stone knives and bear skins. Throughout history, we’ve all stood around scratching our heads about things we couldn’t make sense of until some bright guy (or gal) worked out the right mathematics and applied it to the problem. Then, suddenly what had been unintelligible became understandable.

These days, what I think is the bleeding edge of applied mathematics is nonlinear dynamics and chaos. Maybe there’s some fuzzy logic thrown into the mix, too. Most of the math tools needed to understand (as in “make mathematical models using”) these things is pretty well in hand. What we need to do is apply such tools to the problems that today vex us.

A case in point is the Gini-Simpson Diversity Index I described in this blog two weeks ago. That is a small brick in the wall of a structure that I hope will someday help us avoid making so many dumb choices. Last week I ran across another brick in a paper written by a couple of computer science professors at my old alma mater Rensselaer Polytechnic Institute (aka RPI, or as we used to call it when I was there as a graduate student, “the Tute”). This bit of intellectual flotsam describes a mathematical model the authors use to predict how political polarization evolves in the U.S. Congress.

Polarization is one of four (at my last count) toxic group-dynamics phenomena that make collaborative decision making fail. Basically, the best decisions are made by groups that work together to reach a consensus. We get crappy decisions when the group’s dynamics break down.

The RPI model is a nonlinear differential equation describing an aspect of the dynamics of decision-making teams. Specifically, it quantifies conditions that determine whether decision teams evolve toward consensus or polarization. We see today what happens when Congress evolves toward polarization. The authors’ research shows that prior to about 1980 Congress evolved toward consensus. Seeing this dynamic at work mathematically gives us a leg up on figuring out why, and maybe doing something about it.

I’m not going to go into the mathematical model the RPI paper presents. The study of nonlinear dynamical systems is far outside the editorial focus of this column. At this point, I’m not going to talk about solutions the paper might suggest for toxic U.S. Government polarization, either. The theory is not well enough developed yet to provide meaningful suggestions.

The purpose of this posting is to point out that application of sophisticated mathematics is necessary for solving society’s most intractable problems. As I said above, not everybody is ready and willing to become expert in using such tools. That’s not necessary. What I hope you’ll walk away with today is an appreciation of applied mathematics’ importance for societal development, and a willingness to support STEM (science, technology, engineering and mathematics) education throughout our school system. Finally, I hope you’ll encourage students who show an interest to learn the techniques and follow STEM careers.

Misconceptions About Maslow’s Hierarchy of Needs

Maslow's Pyramid
Maslow’s pyramid of needs analyzes human needs and arranges them in a hierarchy. By Shutter_M/Shutterstock

24 July 2019 – Abraham Harold Maslow (1908-1970) was a 20th century psychologist famous for describing human motivation as an hierarchy of needs in a 1943 paper entitled “A Theory of Human Motivation” published in Psychological Review. He was a central figure in the founding of Humanistic Psychology, which concentrates on studying mentally healthy humans.

You have to remember that Maslow did his most important work in the middle of the 20th century. At that time there was great popular interest in the works of Sigmund Freud, who worked with the mentally ill, and B.F. Skinner who mainly studied lower animals. Indeed, the entire arts-and-letters school of Surrealism explicitly drew inspiration from Andre Breton’s interpretation of Freud’s work. Despite (or perhaps because of) this interest in Freud and Skinner’s work, there had been little, if any, study of mentally healthy people.

Humanistic Psychologists felt these earlier studies were of limited value to understanding the healthy human mind. Maslow chose to study the workings of healthy human minds from all social strata, but he was especially interested in studying high achievers. For this reason those of us interested in organizational behavior find his humanists of particular interest. We kinda hope our organizations are populated with, and run by, mentally healthy humans, rather than Freud’s neurotics or Skinner’s lab rats!

Maslow’s emphasis on studying high achievers likely gave rise to the first misconception I want to talk about today: the idea that his work gives cover to elitist views. This elitist theory assumes that everyone strives to reach the self-actualization level at the top of the so-called “Pyramid of Needs” used to illustrate Maslow’s hierarchy, but that only an elite fraction of individuals reach it. Lesser individuals are doomed to wallowing in more squalid existences at lower levels.

The second misconception I want to treat today is a similar notion that people start out at the lower levels and climb slowly up to the top as their incomes rise. This theory substitutes a ladder for the pyramid image to visualize Maslow’s hierarchy. People are imagined to climb slowly up this ladder as both their income and social status increase. This, again, gives cover for elitist views as well as laissez-faire economics.

Maslow’s Conception

What Maslow’s Hierarchy really describes is a priority system that determines what people are motivated to do next. It has little to do with their talents, income or social status. To illustrate what I mean, I like to use the following thought experiment. This thought experiment involves Albert Einstein and it’s particularly appropriate because the Grizzled Genius loved thought experiments.

Albert Einstein’s greatest joy was becoming immersed in translating his imaginings about the physical universe into mathematical equations. This is an example of what Maslow called “peak experiences.” Maslow believed these were periods when self-actualized people (those engaged in satisfying their self-actualization need) are happiest and most productive.

Once in a while, however, Einstein would become hungry. Hunger is, however, one of those pesky physiological needs down at the bottom of Maslow’s Hierarchy. There’s nothing aspirational about hunger. It’s what Fredrick Herzberg called a “hygiene factor” or “demotivator.” Such needs are the opposite of aspirational.

If you’ve got an unsatisfied demotivator need, you become unhappy until you can satisfy it. If, for example, you’re hungry, or have a toothache, or need to pee, it becomes hard to concentrate on anything else. Your only thought is (depending on the nature of the unmet physiological need) to go to the bathroom, or the dentist, or, as in Einstein’s case, go find lunch.

The moral of this story is that people don’t sit somewhere for extended periods of time on a shelf labeled with one of Maslow’s categories. Rich people don’t float in a blissful self-actualizing state. Poor people don’t wallow in a miasma of permanently unmet physiological needs. People constantly move up and down the pyramid depending on what the most pressing unmet need of the moment is.

The hierarchy is therefore actually an inverted priority list. Physiological needs are more important than safety needs. When something frightens you – a safety need – the first thing that happens is you feel an urge to pee to take care of a physiological need to prepare your body for running like a scared rabbit. When you see a fast-moving Chevy bearing down on you, you immediately forget pride in that (esteem level) achievement award you just got.

Elitist Fallacy

A combination of confusion about how Maslow’s heirarchy works and his preference for studying high achievers has led many people to imagine his work gives cover for elitist views. If you’re predisposed to imagine that rich people, smart people, or those of high social status are somehow innately “better” than denizens of what 19th century novelist Edward Bulwer-Lytton called “the great unwashed,” then you’re an elitist. An elitist can derive great comfort by misinterpreting Maslow’s work. You can imagine there being a cadre of elite people destined to spend their lives in some ethereal existence where all lower needs are completely satisfied and life’s only pursuit is self actualization.

The poster child for elitism is 16th century theologian John Calvin. In Calvin’s version of Protestant theology everyone was tainted with original sin and doomed to an eternity in Hell. That was a pretty common view at the time of the Protestant Reformation. Calvin added an elitist element by hypothesizing that there was a limited number of individuals (the elect) whom God had chosen for salvation.

It’s called predestination and those folks got tickets into the elite ranks through no merit of their own. There was nothing anybody could do to beg, borrow, or steal their way in. God decided, while making the Universe in the first place, who was in and who was out based on nothing but His whimsey. (Sexist pronoun used specifically to make a point about Calvinism.)

Of course, the requirements of natural selection logically lead to everyone having a desire to be part of an elite. We all want to be different, like the Dada-esque avant garde group King Missile. That’s how DNA measures its success. Only elite DNA gets to have long-term survival.

So, elitism has a lot of natural appeal. This natural appeal accounts for all kinds of rampant racism and xenophobia. Misunderstanding Maslow’s heirarchy provides a pseudoscientific rationale for elitism. To the elitist, the fact that this view is completely mistaken makes no nevermind.

I hope that by now I have disposed of the elitist fallacy.

Economic Ladder Fallacy

Hoping that I’ve disposed of the idea that Maslow’s work gives cover to elitism, I’ll turn to the fallacy of imagining his hierarchy as an economic ladder. This puppy is a natural outgrowth of the Pyramid of Needs image. The top (self actualization) level of the pyramid is imagined as “higher” than the bottom (physiological) level.

This image actually works from the viewpoint that “lower” needs take precedence over “higher” needs in the same way that a building’s supporting foundation takes precedence over the walls and roof. Without a foundation, there’s nothing to support walls or a roof in the same way that without fulfilling physiological needs, there’s no motivation for, say, self actualization.

Think of it this way: dead people, whose physiological needs are all unmet, hardly ever want to run for President.

So, how do you reach something high? You use a ladder!

That’s the thinking that transforms the Pyramid of Needs into some kind of ladder.

If you’re a strict materialist (and way too many Americans are strict materialists) the “high” you care about reaching is wealth. Folks who haven’t understood last month’s posting entitled “The Fluidity of Money” often confuse income with wealth, so there’s some appeal to thinking about Maslow’s Hierarchy of Needs as a metaphor for income levels. That completes the economic-ladder fallacy.

With this fallacy, folks imagine that everyone starts out at the bottom of the ladder and, with time, hard work and luck, climbs their way to the top. There are obvious problems matching income levels with needs levels, but if you’re sufficiently intellectually lazy, you can unfocus your mind’s eye enough to render these problems invisible.

I especially get a kick out of efforts to use the idea of Engel curves (from economics) to make this ladder fallacy work. Engel curves map the desireability (measured as the demand side of the economics law of supply and demand) of a given good or product against a given consumer’s income level. If the good in question is, for example, a used Mazda Miata, the desirability may be high when the consumer has a low-to-moderate income, but low if that particular consumer has enough income to pay for a new Ferrari SF90 Stradale. If you want to, it is obvious you can somehow conflate Engel curves with the ladder idea of Maslow’s Heirarchy of Needs.

The problem with this thinking is, first, that the Ladder doesn’t make a lot of sense as a visualization for Maslow’s Heirarchy, since the latter is formost a priority-setting scheme; second, that Maslow’s Hierarchy has little connection to income; and, third, that Engel curves present an incomplete view of what makes a product desirable.

The elitist fallacy and the economic-ladder fallacy are not the only fallacies people, with their infinite capacity to generate cockamamie theories, can concoct in connection to Maslow’s work. They are just two that have come up recently in articles I’ve had occasion to read. I think analyzing them can also help clarify how the Hierarchy of Needs applies to understanding human behavior.

Besides, I’ve had a bit of fun knocking them around, and I hope you have, too.

Computing Diversity

Decision Team
Diversity of membership in decision-making teams leads to better outcomes. By Rawpixel.com/Shutterstock

17 July 2019 – It’s come to my attention that a whole lot of people don’t know how to calculate a diversity score, or even that such a thing exists! How can there be so much discussion of diversity and so little understanding of what the word means? In this post I hope to give you a peek behind the curtain, and maybe shed some light on what diversity actually is and how it is measured.

This topic is of particular interest to me at present because momentum is building to make a study of diversity in business-decision making the subject of my doctoral dissertation in Business Administration. Specifically, I’m looking at how decision-making teams (such as boards of directors) can benefit from membership diversity, and what can go wrong.

Estimating Diversity

The dictionary definition of diversity is: “the condition of having or being composed of differing elements.”

So, before we can quantify the diversity of any group, we’ve got to identify what makes different elements different. This, by the way, is all basic set theory. In different contexts what we mean by “different” may vary. When we’re talking about group decision making in a business context, it gets pretty complicated.

A group may be subdivided, or “stratified” along various dimensions. For example, a team of ten people sitting around a table trying to figure out what to do next about, say, a new product could be subdivided in various ways. One way to stratify such a group is by age. You’d have so many individuals in their 20’s, so many might be in their 30’s, and so forth up to the oldest group being aged 50 or more. Another (perhaps more useful) way to subdivide them is by specialty. There may be so many software engineers, so many hardware engineers, so many marketers, and so forth. These days stratifying teams by gender, ethnicity, educational level or political persuasion could be important. What counts as diversity depends on what the team is trying to decide.

The moral of this story is that a team might score high in diversity along one dimension and very poorly along another. I’m not going to say any more about diversity’s multidimensional nature in this essay, however. We have other fish to fry today.

For now, let’s assume a one-dimensional diversity index. What we pick for a dimension makes little difference to the mathematics we use. Let’s just imagine a medium-sized group of, say, ten individuals and stratify them according to the color of tee-shirts they happen to be wearing at the moment.

What the color of their tee-shirts could possibly mean for the group’s decisions about new-product development I can’t imagine, and probably wouldn’t care anyway. It does, however, give us a way to stratify the sample. Let’s say their shirt colors fall out as in Table 1. So, we’ve got five categories of team members stratified by tee-shirt color.Table 1: Tee-Shirt Colors

NOTE: The next bit is mathematically rigorous enough to give most people nosebleeds. You can skip over it if you want to, as I’m going to follow it with a more useful quick-and-dirty estimation method.

The Gini–Simpson diversity index, which I consider to be the most appropriate for evaluating diversity of decision-making teams, has a range of zero to one, with zero being “everybody’s the same” and one being “everybody’s different.” We start by asking: “What is the probability that two members picked at random have the same color tee shirt?”

If you’ve taken my statistical analysis course, you’ll likely loathe remembering that the probability of picking two things from a stratified data set, and having them both fall into the same category is:

Equation 1

Where λ is the probability we want, N is the number of categories (in this case 5), and P is the probability that, given the first pick falling into a certain category (i) the second pick will be in the same category. The superscript 2 just indicates that we’re taking members two at a time. Basically P is the number of members in category i divided by the total number of members in all categories. Thus, if the first pick has a blue tee-shirt, then P is 3/10 = 0.3.

This probability is high when diversity is low, and low when diversity is high. The Gini-Simpson index makes more intuitive sense by simply subtracting that probability from unity (1.0) to get something that is low when diversity is low, and high when diversity is high.

NOTE: Here’s where we stop with the fancy math.

Guesstimating Diversity

Veteran business managers (at least those not suffering from pathological levels of OCD) realize that the vast majority of business decisions – in fact most decisions in general – are not made after extensive detailed mathematical analysis like what I presented in the previous section. In fact, humans have an amazing capacity for making rapid decisions based on what’s called “fuzzy logic.”

Fuzzy logic recognizes that in many situations, precise details may be difficult or impossible to obtain, and may not make a significant difference to the decision outcome, anyway. For example, deciding whether to step out to cross a street could be based on calculations using precise measurements of an oncoming car’s speed, distance, braking capacity, and the probability that the driver will detect your presence in time to apply the brakes to avoid hitting you.

But, it’s usually not.

If we had to make the decision by the detailed mathematical analysis of physical measurements, we’d hardly ever get across the street. We can’t judge speed or distance accurately enough, and have no idea whether the driver is paying attention. We don’t, in general, make these measurements, then apply detailed calculations using Gallilean Transformations to decide if now is a safe time to cross.

No, we have, with experience over time, developed a “gut feel” for whether it’s safe. We use fuzzy categories of “far” and “near,” and “slow” or “fast.” Even the terms “safe” and “unsafe” have imprecise meanings, gradually shifting from one to the other as conditions change. For example “safe to cross” means something quite different on a dry, sunny day in summertime, than when the pavement has a slippery sheen of ice.

Group decision making has a similar fuzzy component. We know that the decision team we’ve got is the decision team we’re going to use. It makes no difference whether it’s diversity score is 4.9 or 5.2, what we’ve got is what we’re going to use. Maybe we could make a half-percent improvement in the odds of making the optimal decision by spending six months recruiting and training a blind Hispanic woman with an MBA to join the team, but are we going to do it? Nope!

We’ll take our chances with the possibly sub-optimal decision made by the team we already have in place.

Hopefully we’re not trying to work out laws affecting 175 million American women with a team consisting of 500 old white guys, but, historically, that’s the team we’ve had. No wonder we’ve got so many sub-optimal laws!

Anyway, we don’t usually need to do the detailed Gini-Simpson Diversity Index calculation to guesstimate how diverse our decision committee is. Let’s look at some examples whose diversity indexes are easy to calculate. That will help us develop a “gut feel” for diversity that’ll be useful in most situations.

So, let’s assume we look around our conference room and see six identical white guys and six identical white women. It’s pretty easy to work out that the team’s diversity index is 0.5. The only way to stratify that group is by gender, and the two strata are the same size. If our first pick happens to be a woman, then there’s a 50:50 chance that the second pick will be a woman, too. One minus that probability (0.5) equals 0.5.

Now, let’s assume we still have twelve team members, but eleven of them are men and there’s only one token woman. If your first pick is the woman, the probability of picking a woman again is 1/12 = 0.8. (The Gini-Simpson formula lets you pick the same member twice.) If, on the other hand, your first pick is a man, the probability that the second pick will also be a man is 11/12 = 0.92. I plugged all this into an online Gini-Simpson-Index calculator (‘cause I’m lazy) and it returned a value of 26%. That’s a whole lot worse.

Let’s see what happens when we maximize diversity by making everyone different. That means we end up stratifying the members into twelve segments. After picking one member, the odds of the second pick being identical are 1/12 = 0.8 for every segment. The online calculator now gives us a diversity index of 91.7%. That’s a whole lot better!

What Could Possibly Go Wrong?

There are two main ways to screw up group diversity: group-think and group-toxicity. These are actually closely related group-dynamic phenomena. Both lower the effective diversity.

Group-think occurs when members are too accommodating. That is, when members strive too hard to reach consensus. They look around to see what other members want to do, and agree to it without trying to come up with their own alternatives. This produces sub-optimal decisions because the group fails to consider all possible alternatives.

Toxic group dynamics occurs when one or more members dominate the conversation either by being more vocal or more numerous. Members with more reticent personalities fail to speak up, thus denying the group their input. Whenever a member fails to speak up, they lower the group’s effective diversity.

A third phenomenon that messes up decision making for  high-diversity teams is that when individual members are too insistent that their ideas are the best, groups often fail to reach consensus at all. At that point more diversity makes reaching consensus harder. That’s the problem facing both houses of the U.S. Congress at the time of this writing.

These phenomena are present to some extent in every group discussion. It’s up to group leadership to suppress them. In the end, creating an effective decision-making team requires two elements: diversity in team membership, and effective team leadership. Membership diversity provides the raw material for effective team decision making. Effective leadership mediates group dynamics to make it possible to maximize the team’s effective diversity.

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.