30 October 2019 – The essay below was posted to the Keiser University DBA 710 Week 8 Discussion Forum. It is reproduced here in the hope that readers of this blog will find this peek into state-of-the-art management research interesting.
This posting is a bit off topic for Week 8, but it reviews a paper that didn’t cross my desk in time to be included in last week’s discussions, where it would have been more appropriate. In fact, the copy of the paper I received was a manuscript version of a paper accepted by the journal Organizational Psychology Review that is at the printer now.
The paper, written by an Australian-German team, covers recent developments in measuring variables apropos management of decision teams in various situations (Klonek, Gerpott, Lehmann-Willenbrock & Parker, in press). As we saw last week, there is a lot of work to be done on metrology of leadership and management variables. The two main metrology-tool classifications are case studies (Pettigrew, 1990) and surveys (Osei-Kyei & Chan, 2018). Both involve time lags that make capturing data in real time and assuring its freedom from bias impossible (Klonek, Gerpott, Lehmann-Willenbrock & Parker, in press). Decision teams, however, present a dynamic environment where decision-making processes evolve over time (Lu, Gao & Szymanski, 2019). To adequately study such processes requires making time resolved measurements quickly enough to follow these dynamic changes.
Recent technological advances change that situation. Wireless sensor systems backed by advanced data-acquisition software make in possible to unobtrusively monitor team members’ activities in real time (Klonek, Gerpott, Lehmann-Willenbrock & Parker, in press). The paper describes how management scholars can use these tools to capture useful information for making and testing management theories. It provides a step-by-step breakdown of the methodology, including determining the appropriate time-resolution target, choosing among available metrology tools, capturing data, organizing data, and interpreting data. It covers working on time scales from milliseconds to months, and mixed time scales. Altogether, the paper provides invaluable information for anyone intending to link management theory and management practice in an empirical way (Bartunek, 2011).
Bartunek, J. M. (2011). What has happened to Mode 2? British Journal of Management, 22(3), 555–558.
Klonek, F.E., Gerpott, F., Lehmann-Willenbrock, N., & Parker, S. (in press). Time to go wild: How to conceptualize and measure process dynamics in real teams with high resolution? Organizational Psychology Review.
Lu, X., Gao, J. & Szymanski, B. (2019) The evolution of polarization in the legislative branch of government. Journal of the Royal Society Interface, 16: 20190010.
Osei-Kyei, R., & Chan, A. (2018). Evaluating the project success index of public-private partnership projects in Hong Kong. Construction Innovation, 18(3), 371-391.
Pettigrew, A. M. (1990). Longitudinal Field Research on Change: Theory and Practice. Organization Science, 1(3), 267–292.
4 September 2019 – I’m in the early stages of a long-term research project for my Doctor of Business Administration (DBA) degree. Hopefully, this research will provide me with a dissertation project, but I don’t have to decide that for about a year. And, in the chaotic Universe in which we live a lot can, and will, happen in a year.
I might even learn something!
And, after learning something, I might end up changing the direction of my research. Then again, I might not. To again (as I did last week ) quote Winnie the Pooh: “You never can tell with bees!”
No, this is not an appropriate forum for publishing academic research results. For that we need peer-reviewed scholarly journals. There are lots of them out there, and I plan on using them. Actually, if I’m gonna get the degree, I’m gonna have to use them!
This is, however, an appropriate forum for summarizing some of my research results for a wider audience, who might just have some passing interest in them. The questions I’m asking affect a whole lot of people. In fact, I dare say that they affect almost everyone. They certainly can affect everyone’s thinking as they approach teamwork at home and at work, as well as how they consider political candidates asking for their votes.
For example, a little over a year from now, you’re going to have the opportunity to vote for who you want running the United States Government’s Executive Branch as well as a few of the people you’ll hire (or re-hire) to run the Legislative Branch. Altogether, those guys form a fairly important decision-making team. A lot of folks have voiced disapprobation with how the people we’ve hired in the past have been doing those jobs. My research has implications for what questions you ask of the bozos who are going to be asking for your votes in the 2020 elections.
One of the likely candidates for President has shown in words and deeds over the past two years (actually over the past few decades, if you care to look that far into his past) that he likes to make decisions all by his lonesome. In other words, he likes to have a decision team numbering exactly one member: himself.
Those who have paid attention to this column (specifically the posting of 17 July) can easily compute the diversity score for a team like that. It’s exactly zero.
When looking at candidates for the Legislative Branch, you’ll likely encounter candidates who’re excessively proud to promise that they’ll consult that Presidential candidate’s whims regarding anything, and support whatever he tells them he wants. Folks who paid attention to that 17 July posting will recognize that attitude as one of the toxic group-dynamics phenomena that destroy a decision team’s diversity score. If we elect too many of them to Congress and we vote Bozo #1 back into the Presidency, we’ll end up with another four years of the effective diversity of the U.S. Government decision team being close to or exactly equal to zero.
Preliminary results from my research – looking at results published by other folks asking what diversity or lack thereof does to the results of projects they make decisions for – indicates that decision teams with zero effective diversity are dumber than a box of rocks. Nobody’s done the research needed to make that statement look anything like Universal Truth, but several researchers have looked at outcomes of a lot of projects. They’ve all found that more diverse teams do better.
Anyway, what this research project is all about is studying the effect of team-member diversity on decision-team success. For that to make sense, it’s important to define two things: diversity and success. Even more important is to make them measurable.
I’ve already posted about how to make both diversity and success measurable. On 17 July I posted a summary of how to quantify diversity. On 7 August I posted a summary of my research (so far) into quantifying project success as well. This week I’m posting a summary of how I plan to put it all together and finally get some answers about how diversity really affects project-development teams.
What I’m hoping to do with this research is to validate three hypotheses. The main hypothesis is that diversity (as measured by the Gini-Simpson index outlined in the 17 July posting) correlates positively with project success (as measured by the critical success index outlined in the 7 August posting). A secondary hypothesis is that four toxic group-dynamic phenomena reduce a team’s ability to maximize project success. A third hypothesis is that there are additional unknown or unknowable factors that affect project success. The ultimate goal of this research is to estimate the relative importance of these factors as determinants of project success.
Understanding the methodology I plan to use begins with a description of the information flows within an archetypal development project. I then plan on conducting an online survey to gather data on real world projects in order to test the hypothesis that it is possible to determine a mathematical function that describes the relationship between diversity and project success, and to elucidate the shape of such a function if it exists. Finally, the data can help gauge the importance of group dynamics to team-decision quality.
The figure above schematically shows the information flows through a development project. External factors determine project attributes. Personal attributes, such as race, gender, and age combine with professional attributes, such as technical discipline (e.g., electronics or mechanical engineering) and work experience to determine raw team diversity. Those attributes combine with group dynamics to produce an effective team diversity. Effective diversity affects both project planning and project execution. Additional inputs from stakeholder goals and goals of the sponsoring enterprise also affect the project plans. Those plans, executed by the team, determine the results of project execution.
The proposed research will gather empirical data through an online survey of experienced project managers. Following the example of researchers van Riel, Semeijn, Hammedi, & Henseler (2011), I plan to invite members of the Project Management Institute (PMI) to complete an online survey form. Participants will be asked to provide information about two projects that they have been involved with in the past – one they consider to be successful and one that they consider less successful. This is to ensure that data collected includes a range of project outcomes.
There will be four parts to the survey. The first part will ask about the respondent and the organization sponsoring the project. The second will ask about the project team and especially probe the various dimensions of team diversity. The third will ask about goals expressed for the project both by stakeholders and the organization, and how well those goals were met. Finally, respondents will provide information about group dynamics that played out during project team meetings. Questions will be asked in a form similar to that used by van Riel, Semeijn, Hammedi, & Henseler (2011): Respondents will rate their agreement with statements on a five- or seven-step Likert scale.
The portions of the survey that will be of most importance will be the second and third parts. Those will provide data that can be aggregated into diversity and success indices. While privacy concerns will make masking identities of individuals, companies and projects important, it will be critical to preserve links between individual projects and data describing those project results.
This will allow creating a two-dimensional scatter plot with indices of team diversity and project success as independent and dependent variables respectively. Regression analysis of the scatter plot will reveal to what extent the data bear out the hypothesis that team diversity positively correlates with project success. Assuming this hypothesis is correct, analysis of deviations from the regression curve (n-way ANOVA) will reveal the importance of different group dynamics effects in reducing the quality of team decision making. Finally, I’ll need to do a residual analysis to gauge the importance of unknown factors and stochastic noise in the data.
Altogether this research will validate the three hypotheses listed above. It will also provide a standard methodology for researchers who wish to replicate the work in order to verify or extend it. Of course, validating the link between team diversity and decision-making success has broad implications for designing organizations for best performance in all arenas of human endeavor.
de Rond, M., & Miller, A. N. (2005). Publish or perish: Bane or boon of academic life? Journal of Management Inquiry, 14(4), 321-329.
van Riel, A., Semeijn, J., Hammedi, W., & Henseler, J. (2011). Technology-based service proposal screening and decision-making effectiveness. Management Decision, 49(5), 762-783.
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 materRensselaer 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.
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.
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.
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:
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.
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 thewoman, 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.
6 June 2019 – Once upon a time in an MBA school far, far away, I took a Marketing 101 class. The instructor, whose name I can no longer be sure of, had a number of sayings that proved insightful, bordering on the oracular. (That means they were generally really good advice.) One that he elevated to the level of a mantra was: “Stick to the knitting.”
Really successful companies of all sizes hew to this advice. There have been periods of history where fast-growing companies run by CEOs with spectacularly big egos have equally spectacularly honored this mantra in the breach. With more hubris than brains, they’ve managed to over-invest themselves out of business.
Today’s tech industry – especially the FAANG companies (Facebook, Amazon, Apple, Netflix and Google) – is particularly prone to this mistake. Here I hope to concentrate on what the mantra means, and what goes wrong when you ignore it.
Okay, “stick to your knitting” is based on the obvious assumption that every company has some core expertise. Amazon, for example, has expertise in building and operating an online catalog store. Facebook has expertise in running an online forum. Netflix operates a bang-up streaming service. Ford builds trucks. Lockheed Martin makes state-of-the-art military airplanes.
General Electric, which has core expertise in manufacturing industrial equipment, got into real trouble when it got the bright idea of starting a finance company to extend loans to its customers for purchases of its equipment.
There is a business model, called the conglomerate that is based on explicitly ignoring the “knitting” mantra. It was especially popular in the 1960s. Corporate managers imagined that conglomerates could bring into play synergies that would make conglomerates more effective than single-business companies.
For a while there, this model seemed to be working. However, when business conditions began to change (specifically interest rates began to rise from an abnormally low level to more normal rates) their supposed advantages began melting like a birthday cake left outside in a rainstorm. These huge conglomerates began hemorrhaging money until vultures swooped in to pick them apart. Conglomerates are now a thing of the past.
There are companies, such as Berkshire Hathaway, whose core expertise is in evaluating and investing in other companies. Some of them are very successful, but that’s because they stick to their core expertise.
Berkshire Hathaway was originally a textile company that investor Warren Buffett took over when the textile industry was busy going overseas. As time went on, textiles became less important and, by 1985 this core part of the company was shut down. It had become a holding company for Buffett’s investments in other companies. It turns out that Buffett’s core competence is in handicapping companies for investment potential. That’s his knitting!
The difference between a holding company and a conglomerate is (and this is specifically my interpretation) a matter of integration. In a conglomerate, the different businesses are more-or-less integrated into the parent corporation. In a holding company, they are not.
Berkshire Hathaway is known for it’s insurance business, but if you want to buy, say, auto insurance from Berkshire Hathaway, you have to go to it’s Government Employees Insurance Company (GEICO) subsidiary. GEICO is a separate company that happens to be wholly owned by Berkshire Hathaway. That is, it has its own corporate headquarters and all the staff, fixtures and other resources needed to operate as an independent insurance company. It just happens to be owned, lock, stock and intellectual property by another corporate entity: Berkshire Hathaway.
GEICO’s core expertise is insurance. Berkshire Hathaway’s core expertise is finding good companies to invest in. Some are partially owned (e.g., 5.4% of Apple) some are wholly owned (e.g.,Acme Brick).
Despite Berkshire Hathaway’s holding positions in both Apple and Acme Brick, if you ask Warren Buffet if Berkshire Hathaway is a computer company or a brick company, he’d undoubtedly say “no.” Berkshire Hathaway is a diversified holding company.
It’s business is owning other businesses.
To paraphrase James Coburn’s line from Stanley Donen’s 1963 film Charade: “[Mrs. Buffett] didn’t raise no stupid children!”
Why Giant Corporations?
All this giant corporation stuff stems from a dynamic I also learned about in MBA school: a company grows or it dies. I ran across this dynamic during a financial modeling class where we used computers to predict results of corporate decisions in lifelike conditions. Basically, what happens is that unless the company strives to its utmost to maintain growth, it starts to shrink and then all is lost. Feedback effects take over and it withers and dies.
Observations since then have convinced me this is some kind of natural law. It shows up in all kinds of natural systems. I used to think I understood why, but I’m not so sure anymore. It may have something to do with chaos, and we live in a chaotic universe. I resolve to study this in more detail – later.
But, anyway … .
Companies that embrace this mantra (You grow or you die.) grow until they reach some kind of external limit, then they stop growing and – in some fashion or other – die.
Sometimes (and paradigm examples abound) external limits don’t kick in before some company becomes very big, indeed. Standard Oil Company may be the poster child for this effect. Basically, the company grew to monopoly status and, in 1911 the U.S. Federal Government stepped in and, using the 1890 Sherman Anti-Trust Act, forced its breakup into 33 smaller oil companies, many of which still exist today as some of the world’s major oil companies (e.g., Mobil, Amoco, and Chevron). At the time of its breakup, Standard Oil had a market capitalization of just under $11B and was the third most valuable company in the U.S. Compare that to the U.S. GDP of roughly $34B at the time.
The problem with companies that big is that they generate tons of free cash. What to do with it?
There are three possibilities:
You can reinvest it in your company;
You can return it to your shareholders; or
You can give it away.
Reinvesting free cash in your company is usually the first choice. I say it is the first choice because it is used at the earliest period of the company’s history – the period when growth is necessarily the only goal.
If done properly reinvestment can make your company grow bigger faster. You can reinvest by out-marketing your competition (by, say, making better advertisements) and gobbling up market share. You can also reinvest to make your company’s operations more effective or efficient. To grow, you also need to invest in adding production facilities.
At a later stage, your company is already growing fast and you’ve got state-of-the-art facilities, and you dominate your market. It’s time to do what your investors gave you their money for in the first place: return profits to them in the form of dividends. I kinda like that. It’s what the game’s all about, anyway.
Finally, most leaders of large companies recognize that having a lot of free cash laying around is an opportunity to do some good without (obviously) expecting a payback. I qualify this with the word “obviously” because on some level altruism does provide a return in some form.
Generally, companies engage in altruism (currently more often called “philanthropy”) to enhance their perception by the public. That’s useful when lawsuits rear their ugly heads or somebody in the organization screws up badly enough to invite public censure. Companies can enhance their reputations by supporting industry activities that do not directly enhance their profits.
So-called “growth companies,” however, get stuck in that early growth phase, and never transition to paying dividends. In the early days of the personal-computer revolution, tech companies prided themselves on being “growth stocks.” That is, investors gained vast wealth on paper as the companies’ stock prices went up, but couldn’t realized those gains (capital gains) unless they sold the stock. Or, as my father once did, by using the stock for collateral to borrow money.
In the end, wise investors eventually want their money back in the form of cash from dividends. For example, in the early 2000s, Microsoft and other technology companies were forced by their shareholders to start paying dividends for the first time.
What can go wrong
So, after all’s said and done, why’s my marketing professor’s mantra wise corporate governance?
To make money, especially the scads of money that corporations need to become really successful, you’ve gotta do something right. In fact, you gotta do something better than the other guys. When you know how to do something better than the other guys, that’s called expertise!
Companies, like people, have limitations. To imagine you don’t have limitations is hubris. To put hubris in perspective, recall that the ancients famously made it Lucifer’s cardinal sin. In fact, it was his only sin!
Folks who tell you that you can do anything are flat out conning your socks off.
If you’re lucky you can do one thing better than others. If you’re really lucky, you can do a few things better than others. If you try to do stuff outside your expertise, however, you’re gonna fail. A person can pick themselves up, dust themselves off, and try again – but don’t try to do the same thing again ‘cause you’ve already proved it’s outside your expertise. People can start over, but companies usually can’t.
One of my favorite sayings is:
Everything looks easy to someone who doesn’t know what they’re doing.
The rank amateur at some activity typically doesn’t know the complexities and pitfalls that an expert in the field has learned about through training and experience. That’s what we know as expertise. When anyone – or any company – wanders outside their field of expertise, they quickly fall foul of those complexities and pitfalls.
I don’t know how many times I’ve overheard some jamoke at an art opening say, “Oh, I could do that!”
Yeah? Then do it!
The artist has actually done it.
The same goes for some computer engineer who imagines that knowing how to program computers makes him (or her) smart, and because (s)he is so smart, (s)he could run, say, a magazine publishing house. How hard can it be?
29 May 2019 – Last week (specifically 23 May 2019) the Federal Reserve Board released the results of its annual Survey of Household Economics and Decisionmaking for CY2018. I’ve done two things for readers of this blog. First, I downloaded a PDF copy of the report to make available free of charge on my website at cgmasi.com alongside last year’s report for comparison. Second, I’m publishing an edited extract of the report’s executive summary below.
The report describes the results of the sixth annual Survey of Household Economics and Decisionmaking (SHED). In October and November 2018, the latest SHED polled a self-selected sample of over 11,000 individuals via an online survey.
Along with the survey-results report, the Board published the complete anonymized data in CSV, SAS, STATA formats; as well as a supplement containing the complete SHED questionnaire and responses to all questions in the order asked. The survey continues to use subjective measures and self-assessments to supplement and enhance objective measures.
Survey respondents reported that most measures of economic well-being and financial resilience in 2018 are similar to or slightly better than in 2017. Many families have experienced substantial gains since the survey began in 2013, in line with the nation’s ongoing economic expansion during that period.
Even so, another year of economic expansion and the low national unemployment rates did little to narrow the persistent economic disparities by race, education, and geography. Many adults are financially vulnerable and would have difficulty handling an emergency expense as small as $400.
In addition to asking adults whether they are working, the survey asks if they want to work more and what impediments they see to them working.
Overall Economic Well-Being
A large majority of individuals report that, financially, they are doing okay or living comfortably, and overall economic well-being has improved substantially since the survey began in 2013
When asked about their finances, 75% of adults say they are either doing okay or living comfortably. This result in 2018 is similar to 2017 and is 12%age points higher than 2013.
Adults with a bachelor’s degree or higher are significantly more likely to be doing at least okay financially (87%) than those with a high school degree or less (64%).
Nearly 8 in 10 whites are at least doing okay financially in 2018 versus two-thirds of blacks and Hispanics. The gaps in economic well-being by race and ethnicity have persisted even as overall wellbeing has improved since 2013.
Fifty-six percent of adults say they are better off than their parents were at the same age and one fifth say they are worse off.
Nearly two-thirds of respondents rate their local economic conditions as “good” or “excellent,” with the rest rating conditions as “poor” or “only fair.” More than half of adults living in rural areas describe their local economy as good or excellent, compared to two-thirds of those living in urban areas.
Changes in family income from month to month remain a source of financial strain for some individuals.
Three in 10 adults have family income that varies from month to month. One in 10 adults have struggled to pay their bills because of monthly changes in income. Those with less access to credit are much more likely to report financial hardship due to income volatility.
One in 10 adults, and over one-quarter of young adults under age 30, receive some form of financial support from someone living outside their home. This financial support is mainly between parents and adult children and is often to help with general expenses.
Most adults are working as much as they want to, an indicator of full employment; however, some remain unemployed or underemployed. Economic well-being is lower for those wanting to work more, those with unpredictable work schedules, and those who rely on gig activities as a main source of income.
One in 10 adults are not working and want to work, though many are not actively looking for work. Four percent of adults in the SHED are not working, want to work, and applied for a job in the prior 12 months. This is similar to the official unemployment rate of 3.8% in the fourth quarter of 2018.
Two in 10 adults are working but say they want to work more. Blacks, Hispanics, and those with less education are less likely to be satisfied with how much they are working.
Half of all employees received a raise or promotion in the prior year.
Unpredictable work schedules are associated with financial stress for some. One-quarter of employees have a varying work schedule, including 17% whose schedule varies based on their employer’s needs. One-third of workers who do not control their schedule are not doing okay financially, versus one-fifth of workers who set their schedule or have stable hours.
Three in 10 adults engaged in at least one gig activity in the prior month, with a median time spent on gig work of five hours. Perhaps surprisingly, little of this activity relies on technology: 3% of all adults say that they use a website or an app to arrange gig work.
Signs of financial fragility – such as difficulty handling an emergency expense – are slightly more common for those engaged in gig work, but markedly higher for those who do so as a main source of income.
Dealing with Unexpected Expenses
While self-reported ability to handle unexpected expenses has improved substantially since the survey began in 2013, a sizeable share of adults nonetheless say that they would have some difficulty with a modest unexpected expense.
If faced with an unexpected expense of $400, 61% of adults say they would cover it with cash, savings, or a credit card paid off at the next statement – a modest improvement from the prior year. Similar to the prior year, 27% would borrow or sell something to pay for the expense, and 12% would not be able to cover the expense at all.
Seventeen percent of adults are not able to pay all of their current month’s bills in full. Another 12% of adults would be unable to pay their current month’s bills if they also had an unexpected $400 expense that they had to pay.
One-fifth of adults had major, unexpected medical bills to pay in the prior year. One-fourth of adults skipped necessary medical care in 2018 because they were unable to afford the cost.
Banking and Credit
Most adults have a bank account and are able to obtain credit from mainstream sources. However, sub- stantial gaps in banking and credit services exist among minorities and those with low incomes.
Six percent of adults do not have a bank account. Fourteen percent of blacks and 11% of Hispanics are unbanked versus 4% of whites. Thirty-five percent of blacks and 23% of Hispanics have an account but also use alternative financial services, such as money orders and check cashing services, compared to 11% of whites.
More than one-fourth of blacks are not confident that a new credit card application would be approved if they applied—over twice the rate among whites.
Those who never carry a credit card balance are much more likely to say that they would pay an unexpected $400 expense with cash or its equivalent (88%) than those who carry a balance most or all of the time (40%) or who do not have a credit card (27%).
Thirteen percent of adults with a bank account had at least one problem accessing funds in their account in the prior year. Problems with a bank website or mobile app (7%) and delays in when funds were available to use (6%) are the most common problems. Those with volatile income and low savings are more likely to experience such problems.
Housing and Neighborhoods
Satisfaction with one’s housing and neighborhood is generally high, although notably less so in low-income communities. While 8 in 10 adults living in middle- and upper-income neighborhoods are satisfied with the overall quality of their community, 6 in 10 living in low- and moderate-income neighborhoods are satisfied.
People’s satisfaction with their housing does not vary much between more expensive and less expensive cities or between urban and rural areas.
Over half of renters needed a repair at some point in the prior year, and 15% of renters had moderate or substantial difficulty getting their landlord to complete the repair. Black and Hispanic renters are more likely than whites to have difficulties getting repairs done.
Three percent of non-homeowners were evicted, or moved because of the threat of eviction, in the prior two years. Evictions are slightly more common in urban areas than in rural areas.
Economic well-being rises with education, and most of those holding a post-secondary degree think that attending college paid off.
Two-thirds of graduates with a bachelor’s degree or more feel that their educational investment paid off financially, but 3 in 10 of those who started but did not complete a degree share this view.
Among young adults who attended college, more than twice as many Hispanics went to a for-profit institution as did whites. For young black attendees, this rate was five times the rate of whites.
Given what they know now, half of those who attended a private for-profit institution say that they would attend a different school if they had a chance to go back and make their college choices again. By comparison, about one-quarter of those who attended public or private not-for-profit institutions would want to attend a different school.
Student Loans and Other Education Debt
Over half of young adults who attended college took on some debt to pay for their education. Most borrowers are current on their payments or have successfully paid off their loans.
Among those making payments on their student loans, the typical monthly payment is between $200 and $299 per month.
Over one-fifth of borrowers who attended private for-profit institutions are behind on student loan payments, versus 8% who attended public institutions and 5% who attended private not-for-profit institutions.
Many adults are struggling to save for retirement. Even among those who have some savings, people commonly lack financial knowledge and are uncomfortable making investment decisions.
Thirty-six percent of non-retired adults think that their retirement saving is on track, but one-quarter have no retirement savings or pension whatsoever. Among non-retired adults over the age of sixty, 45% believe that their retirement saving is on track.
Six in 10 non-retirees who hold self-directed retirement savings accounts, such as a 401(k) or IRA, have little or no comfort in managing their investments.
On average, people answer fewer than three out of five financial literacy questions correctly, with lower scores among those who are less comfortable managing their retirement savings.
The forgoing is an edited extract from the Report’s Executive Summary. A PDF version of the entire report is available on my website at cgmasi.com [ http://cgmasi.com ] along with a PDF version of the 2017 report, which was published in May of 2018 and based on a similar survey conducted in late 2017. Reports dating back to the first survey done in late 2013 are available from the Federal Reserve Board’s website linked to above.
I’m not going to get into the tangled web of potential copyright infringement that Shaw’s posting of Hazlitt’s entire text opens up, I’ve just linked to the most convenient-to-read posting of that particular chapter. If you follow the link and want to buy the book, I’ve given you the appropriate link as well.
The chapter is of immense value apropos the question of whether automation generally reduces the need for human labor, or creates more opportunities for humans to gain useful employment. Specifically, it looks at the results of a number of historic events where Luddites excoriated technology developers for taking away jobs from humans only to have subsequent developments prove them spectacularly wrong.
Hazlitt’s classic book is, not surprisingly for a classic, well documented, authoritative, and extremely readable. I’m not going to pretend to provide an alternative here, but to summarize some of the chapter’s examples in the hope that you’ll be intrigued enough to seek out the original.
Before getting on to the examples, let’s start by looking at the history of Luddism. It’s not a new story, really. It probably dates back to just after cave guys first thought of specialization of labor.
That is, sometime in the prehistoric past, some blokes were found to be especially good at doing some things, and the rest of the tribe came up with the idea of letting, say, the best potters make pots for the whole tribe, and everyone else rewarding them for a job well done by, say, giving them choice caribou parts for dinner.
Eventually, they had the best flint knappers make the arrowheads, the best fletchers put the arrowheads on the arrows, the best bowmakers make the bows, and so on. Division of labor into different jobs turned out to be so spectacularly successful that very few of us rugged individualists, who pretend to do everything for ourselves, are few and far between (and are largely kidding ourselves, anyway).
Since then, anyone who comes up with a great way to do anything more efficiently runs the risk of having the folks who spent years learning to do it the old way land on him (or her) like a ton of bricks.
It’s generally a lot easier to throw rocks to drive the innovator away than to adapt to the innovation.
Luddites in the early nineteenth century were organized bands of workers who violently resisted mechanization of factories during the late Industrial Revolution. Named for an imaginary character, Ned Ludd, who was supposedly an apprentice who smashed two stocking frames in 1779 and whose name had become emblematic of machine destroyers. The term “Luddite” has come to mean anyone fanatically opposed to deploying advanced technology.
Of course, like religious fundamentalists, they have to pick a point in time to separate “good” technology from the “bad.” Unlike religious fanatics, who generally pick publication of a certain text to be the dividing line, Luddites divide between the technology of their immediate past (with which they are familiar) and anything new or unfamiliar. Thus, it’s a continually moving target.
In either case, the dividing line is fundamentally arbitrary, so the emotion of their response is irrational. Irrationality typically carries a warranty of being entirely contrary to facts.
What Happens Next
Hazlitt points out, “The belief that machines cause unemployment, when held with any logical consistency, leads to preposterous conclusions.” He points out that on the second page of the first chapter of Adam Smith’s seminal book Wealth of Nations, Smith tells us that a workman unacquainted with the use of machinery employed in sewing-pin-making “could scarce make one pin a day, and certainly could not make twenty,” but with the use of the machinery he can make 4,800 pins a day. So, zero-sum game theory would indicate an immediate 99.98 percent unemployment rate in the pin-making industry of 1776.
Did that happen? No, because economics is not a zero-sum game. Sewing pins went from dear to cheap. Since they were now cheap, folks prized them less and discarded them more (when was the last time you bothered to straighten a bent pin?), and more folks could afford to buy them in the first place. That led to an increase in sewing-pin sales as well as sales of things like sewing-patterns and bulk fine fabric sold to amateur sewers, and more employment, not less.
Similar results obtained in the stocking industry when new stocking frames (the original having been invented William Lee in 1589, but denied a patent by Elizabeth I who feared its effects on employment in hand-knitting industries) were protested by Luddites as fast as they could be introduced. Before the end of the nineteenth century the stocking industry was employing at least a hundred men for every man it employed at the beginning of the century.
Another example Hazlitt presents from the Industrial Revolution happened in the cotton-spinning industry. He says: “Arkwright invented his cotton-spinning machinery in 1760. At that time it was estimated that there were in England 5,200 spinners using spinning wheels, and 2,700 weavers—in all, 7,900 persons engaged in the production of cotton textiles. The introduction of Arkwright’s invention was opposed on the ground that it threatened the livelihood of the workers, and the opposition had to be put down by force. Yet in 1787—twenty-seven years after the invention appeared—a parliamentary inquiry showed that the number of persons actually engaged in the spinning and weaving of cotton had risen from 7,900 to 320,000, an increase of 4,400 percent.”
As these examples indicate, improvements in manufacturing efficiency generally lead to reductions in manufacturing cost, which, when passed along to customers, reduces prices with concommitent increases in unit sales. This is the price elasticity of demand curve from Microeconomics 101. It is the reason economics is decidedly not a zero-sum game.
If we accept economics as not a zero-sum game, predicting what happens when automation makes it possible to produce more stuff with fewer workers becomes a chancy proposition. For example, many economists today blame flat productivity (the amount of stuff produced divided by the number of workers needed to produce it) for lack of wage gains in the face of low unemployment. If that is true, then anything that would help raise productivity (such as automation) should be welcome.
Long experience has taught us that economics is a positive-sum game. In the face of technological advancement, it behooves us to expect positive outcomes while taking measures to ensure that the concomitant economic gains get distributed fairly (whatever that means) throughout society. That is the take-home lesson from the social dislocations that accompanied the technological advancements of the Early Industrial Revolution.
20 March 2019 – The image at right visualizes something described in Douglas Adams’ Hitchiker’s Guide to the Galaxy. At one point, the main characters of that six-part “trilogy” found a big red button on the dashboard of a spaceship they were trying to steal that was marked “DO NOT PRESS THIS BUTTON!” Naturally, they pressed the button, and a new label popped up that said “DO NOT PRESS THIS BUTTON AGAIN!”
Eventually, they got the autopilot engaged only to find it was a stunt ship programmed to crash headlong into the nearest Sun as part of the light show for an interstellar rock band. The moral of this story is “Never push buttons marked ‘DO NOT PUSH THIS BUTTON.’”
Per the author: “It is said that despite its many glaring (and occasionally fatal) inaccuracies, the Hitchhiker’s Guide to the Galaxy itself has outsold the Encyclopedia Galactica because it is slightly cheaper, and because it has the words ‘DON’T PANIC’ in large, friendly letters on the cover.”
Despite these references to the Hitchhiker’s Guide to the Galaxy, this posting has nothing to do with that book, the series, or the guide it describes, except that I’ve borrowed the words from the Guide’s cover as a title. I did that because those words perfectly express the take-home lesson of Bill Snyder’s 11 March 2019 article in The Robot Report entitled “Fears of job-stealing robots are misplaced, say experts.”
Snyder’s article reports opinions expressed at the the Conference on the Future of Work at Stanford University last month. It’s a topic I’ve shot my word processor off about on numerous occasions in this space, so I thought it would be appropriate to report others’ views as well. First, I’ll present material from Snyder’s article, then I’ll wrap up with my take on the subject.
“Robots aren’t coming for your job,” Snyder says, “but it’s easy to make misleading assumptions about the kinds of jobs that are in danger of becoming obsolete.”
“Most jobs are more complex than [many people] realize,” said Hal Varian, Google’s chief economist.
David Autor, professor of economics at the Massachusetts Institute of Technology points out that education is a big determinant of how developing trends affect workers: “It’s a great time to be young and educated, but there’s no clear land of opportunity for adults who haven’t been to college.”
“When predicting future labor market outcomes, it is important to consider both sides of the supply-and-demand equation,” said Varian, “demographic trends that point to a substantial decrease in the supply of labor are potentially larger in magnitude.”
His research indicates that shrinkage of the labor supply due to demographic trends is 53% greater than shrinkage of demand for labor due to automation. That means, while relatively fewer jobs are available, there are a lot fewer workers available to do them. The result is the prospect of a continued labor shortage.
At the same time, Snyder reports that “[The] most popular discussion around technology focuses on factors that decrease demand for labor by replacing workers with machines.”
In other words, fears that robots will displace humans for existing jobs miss the point. Robots, instead, are taking over jobs for which there aren’t enough humans to do them.
Another effect is the fact that what people think of as “jobs” are actually made up of many “tasks,” and it’s tasks that get automated, not entire jobs. Some tasks are amenable to automation while others aren’t.
“Consider the job of a gardener,” Snyder suggests as an example. “Gardeners have to mow and water a lawn, prune rose bushes, rake leaves, eradicate pests, and perform a variety of other chores.”
Some of these tasks, like mowing and watering, can easily be automated. Pruning rose bushes, not so much!
Snyder points to news reports of a hotel in Nagasaki, Japan being forced to “fire” robot receptionists and room attendants that proved to be incompetent.
There’s a scene in the 1997 film The Fifth Element where a supporting character tries to converse with a robot bartender about another character. He says: “She’s so vulnerable – so human. Do you you know what I mean?” The robot shakes its head, “No.”
Sometimes people, even misanthropes, would prefer to interact with another human than with a drink-dispensing machine.
“Jobs,” Varian points out, “unlike repetitive tasks, tend not to disappear. In 1950, the U.S. Census Bureau listed 250 separate jobs. Since then, the only one to be completely eliminated is that of elevator operator.”
“Excessive automation at Tesla was a mistake,” founder Elon Muskmea culpa-ed last year “Humans are underrated.”
Another trend Snyder points out is that automation-ready jobs, such as assembly-line factory workers, have already largely disappeared from America. “The 10 most common occupations in the U.S.,” he says, “include such jobs as retail salespersons, nurses, waiters, and other service-focused work. Notably, traditional occupations, such as factory and other blue-collar work, no longer even make the list.
Again, robots are mainly taking over tasks that humans are not available to do.
The final trend that Snyder presents, is the stark fact that birthrates in developed nations are declining – in some cases precipitously. “The aging of the baby boom generation creates demand for service jobs,” Varian points out, “but leaves fewer workers actively contributing labor to the economy.”
Those “service jobs” are just the ones that require a human touch, so they’re much harder to automate successfully.
My Inexpert Opinion
I’ve been trying, not entirely successfully, to figure out what role robots will actually have vis-a-vis humans in the future. I think there will be a few macroscopic trends. And, the macroscopic trends should be the easiest to spot ‘cause they’re, well, macroscopic. That means bigger. So, there easier to see. See?
As early as 2010, I worked out one important difference between robots and humans that I expounded in my novel Vengeance is Mine! Specifically, humans have a wider view of the Universe and have more of an emotional stake in it.
“For example,” I had one of my main characters pontificate at a cocktail party, “that tall blonde over there is an archaeologist. She uses ROVs – remotely operated vehicles – to map underwater shipwreck sites. So, she cares about what she sees and finds. We program the ROVs with sophisticated navigational software that allows her to concentrate on what she’s looking at, rather than the details of piloting the vehicle, but she’s in constant communication with it because she cares what it does. It doesn’t.”
More recently, I got a clearer image of this relationship and it’s so obvious that we tend to overlook it. I certainly missed it for decades.
It hit me like a brick when I saw a video of an autonomous robot marine-trash collector. This device is a small autonomous surface vessel with a big “mouth” that glides around seeking out and gobbling up discarded water bottles, plastic bags, bits of styrofoam, and other unwanted jetsam clogging up waterways.
The first question that popped into my mind was “who’s going to own the thing?” I mean, somebody has to want it, then buy it, then put it to work. I’m sure it could be made to automatically regurgitate the junk it collects into trash bags that it drops off at some collection point, but some human or humans have to make sure the trash bags get collected and disposed of. Somebody has to ensure that the robot has a charging system to keep its batteries recharged. Somebody has to fix it when parts wear out, and somebody has to take responsibility if it becomes a navigation hazard. Should that happen, the Coast Guard is going to want to scoop it up and hand its bedraggled carcass to some human owner along with a citation.
So, on a very important level, the biggest thing robots need from humans is ownership. Humans own robots, not the other way around. Without a human owner, an orphan robot is a pile of junk left by the side of the road!
That said, I want to inject a note of caution for anyone considering her advice about being a rebel. There’s an old saying: “The nail that sticks up the most is the first to get hammered down.” It’s true in carpentry and in life. Being a rebel is lonely, dangerous, and is no guarantee of success, financial or otherwise.
I speak from experience, having broken every rule available for as long as I can remember. When I was a child in the 1950s, I wanted to grow up to be a beatnik. I’ve always felt most comfortable amongst bohemians. My wife once complained (while we were sitting in a muscle car stopped by the highway waiting for the cop to give me a speeding ticket) about my “always living on the edge.” And, yes, I’ve been thrown out of more than one bar.
On the other hand, I’ve lived a long and eventful life. Most of the items on my bucket list were checked off long ago.
As I expected, the book’s theme is best summed up by a line from the blurb on its dust jacket: “ … the most successful among us break the rules.”
The book description goes on to say, “Rebels have a bad reputation. We think of them as trouble-makers. outcasts, contrarians: those colleagues, friends, and family members who complicate seemingly straight-forward decisions, create chaos, and disagree when everyone else is in agreement. But in truth, rebels are also those among us who change the world for the better with their unconventional outlooks. Instead of clinging to what is safe and familiar, and falling back on routines and tradition, rebels defy the status quo. They are masters of innovation and reinvention, and they have a lot to teach us.”
Considering the third paragraph above, I hope she’s right!
The 283-page (including notes and index) volume summarizes Gino’s decade-long study of rebels at organizations around the world, from high-end boutiques in Italy’s fashion capital (Milan), to the world’s best restaurant (Three-Michelin-star-rated Osteria Francescana), to a thriving fast-food chain (Pal’s), and an award-winning computer animation studio (Pixar).
Francesca Gino is a behavioral scientist and professor at Harvard Business School. She is the Tandon Family Professor of Business Administration in the school’s Negotiation, Organizations & Markets Unit. No slouch professionally, she has been honored as one of the world’s top 40 business professors under 40 by Poets & Quants and one of the world’s 50 most influential management thinkers by Thinkers50.
Enough with the “In Praise Of” stuff, though. Let’s look inside the book. It’s divided into eight chapters, starting with “Napoleon and the Hoodie: The Paradox of Rebel Status,” and ending with “Blackbeard, ‘Flatness,’ and the 8 Principles of Rebel Leadership.” Gino then adds a “Conclusion” telling the story of Risotto Cacio e Pepe (a rice-in-Parmigiano-Reggiano dish invented by Chef Massimo Bottura), and an “Epilogue: Rebel Action” giving advice on releasing your inner rebel.
Stylistically, the narrative uses the classic “Harvard Case Study” approach. That is, it’s basically a pile of stories, each of which makes a point about how rebel leaders Gino has known approach their work. In summary, the take-home lesson is that those leaders encourage their employees to unleash their “inner rebel,” thereby unlocking creativity, enthusiasm, and productivity that more traditional management styles suppress.
The downside of this style is that it sometimes is difficult for the reader to get their brain around the points that Gino is making. Luckily, her narrative style is interesting, easy to follow and compelling. Like all well-written prose she keeps the reader wondering “What happens next?” The episodes she presents are invariably unusual and interesting themselves. She regularly brings in her own exploits and keeps, as much as possible, to first-person active voice.
That is unusual for academic writers, who find it all too easy to slip into a pedantic third-person, passive-voice best reserved for works intended as sleep aids.
To give you a feel for what reading an HCS-style volume is like, I’ll describe what it’s like to study Quantum Dynamics. While the differences outnumber the similarities, the overall “feel” is similar.
The first impression students get of QD is that the subject is entirely anti-intuitive. That is, before you can learn anything about QD, you have to discard any lingering intuition about how the Universe works. That’s probably easier for someone who never learned Classical Physics in the first place. Ideas like “you can’t be in two places at the same time” simply do not apply in the quantum world.
Basically, to learn QD, you have to start with a generous dose of “willing suspension of disbelief.” You do that by studying stories about experiments performed in the late nineteenth century that simply didn’t work. At that time, the best minds in Physics spent careers banging their heads into walls as Mommy Nature refused to return results that Classical Physics imagined she had to. Things like the Michelson-Moreley experiment (and many other then-state-of-the-art experiments) gave results at odds with Classical Physics. There were enough of these screwy results that physicists began to doubt that what they believed to be true, was actually how the Universe worked. After listening to enough of these stories, you begins to doubt your own intuition.
Then, you learn to trust the mathematics that will be your only guide in QD Wonderland.
Finally, you spend a couple of years learning about a new set of ideas based on Through the Looking Glass concepts that stand normal intuition on its head. Piling up stories about all these counter-intuitive ideas helps you build up a new intuition about what happens in the quantum world. About that time, you start feeling confident that this new intuition helps you predict what will happen next.
The HCS style of learning does something similar, although usually not as extreme. Reading story after story about what hasn’t and what has worked for others in the business world, you begin to develop an intuition for applying the new ideas. You gain confidence that, in any given situation, you can predict what happens next.
What happens next is that when you apply the methods Gino advocates, you start building a more diverse corporate culture that attracts and retains the kinds of folks that make your company a leader in its field.
There’s an old one-line joke:
“I want to be different – like everybody else.”
We can’t all be different because then there wouldn’t be any sameness to be different from, but we can all be rebels. We can all follow the
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.
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!