14 December 2019 – The following essay is a verbatim copy of one I recently posted to a Global Business discussion site in response to a link emailed to me by Dr. Tiffany Jordan of Keiser University.
Thank you, TJ, for sending along a link to Steve Sjuggerud’s documentary on Chinese development. History teaches us that 5,000 years ago, China was one of two (maybe three, if you count Central America) population centers (the other was Egypt) where folks independently invented civilization. You can’t go far wrong by betting on people that smart!
The second factor in this story is that one out of six human beings on this planet is Chinese. With that many really smart people let loose to work together, they’re bound to push the limits of economic development. The last time that happened anywhere was in the 18th century when steam technology was let loose among the newly liberated populations of England, North America, and Europe. The resulting Industrial Revolution was a similar game changer. People from the countryside flocked to the cities to make the most of revolutionary technology, and made vast piles of wealth in the process. Sound familiar?
So, what could go wrong? The known preference of the Chinese people for long power distance is what could go wrong (Hofstede, 1993). Since Qin Shi Huang patched together the Chinese Empire in 221 BCE (Shi, 2014), the country has had a nearly unbroken record of authoritarian rule, which is why, after all this time, they’re still stuck with “emerging nation” status. The latest period of lax central control started in the mid-1970s, when Mao Zedong lost control of his Marxist People’s Republic (PRC), and good things started happening in China.
China is home to two philosophies at opposing ends of the power-distance spectrum: Taoist egalitarianism and Confucian formality (Carnogurská, 2014). Taoists insist (among other things) on individual self-rule. Confucionists insist on respect for authority (Zhou, 2011). You can guess which philosophy Xi Jinping’s power-grabbing PRC favors! It is no accident that the slowing of China’s economic expansion immediately followed Xi’s re-institution of central authority. The stark contrast can be seen in the difference between the miracle on the Chinese mainland and the even-bigger miracle that has been playing out in Hong Kong.
I’m always ambivalent, however, about investing in the Chinese “miracle.” Back in the early 1990s I was asked to duplicate my success helping expand an American electronics publication into Europe by doing the same thing in China. With images from Tiananmen-Square events fresh in my mind, I declined. Unlike my corporate bosses, I just didn’t trust the PRC leadership to play nice. That corporation is now out of the publishing business! I’d done the same thing in the 1970s when I declined the last Shah of Iran’s invitation to take our Boston-based Physics Department to Tehran University–just before theirrevolution broke out. (Whew!)
China is not Iran, and Xi Jinping is not Mohammad Reza Shah. Pres. Xi likes leading the fastest-growing economy on the planet, but is facing his big test with current events in Hong Kong. Will he figure a way to defuse that uprising, or will his unenlightened cronies in Beijing push him into a disasterous reprise of Tiananmen-Square? I’m not jumping onto the Chinese bandwagon until I see the result.
Carnogurská, M. (2014). Xunzi, an ingeniously critical synthesist of Chinese philosophy of the pre-Qin period. Journal of Sino – Western Communications, 6(1), 3-25.
Hofstede, G. (1993). Cultural constraints in management theories. Executive, 7(1), 81–94.
Shi, J. (2014). Incorporating all for one: The first emperor’s tomb mound. Early China, 37(1), 359-391.
Zhou, H. (2011). Confucianism and the legalism: A model of the national strategy of governance in ancient China. Frontiers of Economics in China, 6(4), 616-637.
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.
18 September 2019 – The following essay is taken verbatim from a posting I made to the discussion forum for a class in my Doctor of Business Administration program at Keiser University.
For those who were disappointed by my not posting to this blog last week, I apologize. Doctoral programs are very intensive and I’ve found myself overloaded with work. I’ve had to prioritize, and regular postings to this blog are one of the things I’ve had to cut back. When something crosses my desk that I think readers of this blog might find particularly interesting, I’ll try to take time to post it here and let folks know about it through my Linkedin and Facebook accounts.
In the essay below I suggest an extension to a method for understanding human motivation using applied mathematics techniques. What, you didn’t think that was possible? Read on!
Almost at random, I happened to pick up Chung’s (1969) paper from this week’s reading list first. Since it discussed an approach to questions of motivation that I find particularly interesting, I was inspired to jump in and discuss my reaction to it immediately.
The approach Chung took was to use applied mathematics (AM) techniques for analyzing motivation. Anyone not steeped in AM methods could be excused for being surprised that the field could have anything to say about motivation. On the surface, motivation might seem completely qualitative, so how could mathematical techniques be at all useful for analyzing it?
In fact, quantification of anything that you can rank is possible. For example, Zheng & Jiang, (2017) discussed methods of quantifying species diversity in ecosystems. The fact that you can say this ecosystem is more diverse than that ecosystem means that ecosystem diversity is quantifiable.
Similarly, the fact that you can say that such-and-such a person is more motivated to do something than some other person indicates that motivation is quantifiable as well. Before proposing his Markov-chain model, Chung (1969) discussed five other analytical methods for studying motivation based on Maslow’s hierarchy, all of which descriptions he started by describing some method of quantifying motivation.
It happens that I am quite familiar with the mathematics Chung (1969) used. It is called linear algebra, and is a staple technique for analyzing theoretical physics problems. I started my career as an astrophysicist, so Chung’s paper is right in my intellectual wheelhouse. Reading it stimulated me to think: “Yeah, but what about …?”
What Chung’s analysis left out was how human motivation is subject to chaotic exogenous forces. I’ve more than once used the following thought experiment to illustrate this phenomenon. Imagine Albert Einstein scratching away at General Relativity Theory on the blackboard in his office. I mention Einstein particularly because he was known to be fond of thought experiments, so including him in this one seems appropriate. So, Einstein is totally absorbed in his work puzzling out GRT. Maslow would say that he is motivated at the “self-actualization” level. Suddenly, our hero realizes that it’s lunch time because his body signals a physiological need for a ham sandwich. An exogenous event (lunchtime) has modified Einstein’s needs state.
In Chung’s (1969) analysis, Einstein’s transition matrix P has suddenly switched from having element values that cause Einstein’s needs vector N to remain stable at Maslow’s level five to values that cause his needs to switch to level one at the next transition. At that point, Einstein puts down his chalk and roots around in his briefcase for the ham sandwich he knows his wife put in there this morning.
So, how would we handle this situation from a linear algebra standpoint? Using Chung’s (1969) notation, the transition from the ith state to the (i+1)th state is given by Equation 1:
Ni+1 = Ni P (1)
I’ve modified the notation slightly by writing vectors in regular italic typeface and matrices in bold italic typeface. That satisfies my need to have vectors and matrices sybolized in different typefaces. It’s a stability thing for me, so it’s down at Maslow’s level two (Chung, 1969) in my personal hierarchy of needs.
What we need now is to modify the transition matrix by applying another matrix that isolates the effect of the exogenous event. If we add a subscript 0 to specify the original transition matrix, and multiply it by a new matrix X that accounts specifically for the exogenous event, we get a new transition matrix given by Equation 2:
P = P0 X (2)
Finally, Equation 1 becomes Equation 3.
Ni+1 = Ni P0 X (3)
What is left to do is to develop methods of determining numerical values for the elements of these vectors and matrices in specific situations. This addition shows how to extend Chung’s (1969) Markov-chain model to situations where life events modify an individual’s motivational outlook. Such events can be anything from time reaching the lunch hour to the individual becoming a parent.
Chung, K. H. (1969). A Markov Chain Model of Human Needs: An Extension of Maslow’s Need Theory. Academy of Management Journal, 12(2), 223–234.
Zheng, L. & Jiang, J. (2017) A New Diversity Estimator. Journal of Statistical Distributions and Applications, 4(1), 1-13.
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.
28 August 2019 – The short answer is, to quote Pooh Bear in A.A. Milne’s Winnie-the-Pooh, “You never can tell with bees!” Or, with advancing technology, for that matter. Last week, however, the Analytics Team at Autolist published results of a survey of 1,567 current car shoppers that might shed some light on the question of whether electric vehicles (EVs) can fully replace vehicles with internal combustion engines (ICEs).
The Analytics Team asked survey respondents what were their biggest reasons to not buy an electric vehicle. By looking at the results, we can project when, how, and if e-vehicle technology can ever surmount car-shoppers’ objections.
The survey results were spectacularly unsurprising. The top three barriers to purchasing an electric vehicle were:
Concerns about lack of adequate range;
E-vehicles’ relatively high cost compared to similar gas vehicles; and
Concerns about charging infrastructure.
Anybody following the development of electric vehicles already knew that. Most folks could even peg the order of concern. What was somewhat surprising, though, is how little folks’ trepidation dropped off for less significant concerns. Approximately 42% of respondents cited adequate range as a concern. The score dropped only to about 14% for the ninth-most-concerning worry: being unhappy with choices of body style.
What that means for development of electric-vehicle technology is that resolving the top three issues won’t do the job. Resolving the top three issues would just elevate the next three issues to top-concern status for 25-30% of potential customers. That’s still way too high to allow fully replacing ICE-powered vehicles with EVs, as nine European countries (so far) have announced they want to do between 2020 and 2050.
Looking at what may be technologically feasible could give a glimpse of how sane or insane such ICE bans might be. What we can do is go down the list and speculate on how tough it will be to overcome each obstacle to full adoption. The Pareto chart above will show the “floor” to folks’ resistance if any of these issues remains unmet.
Top Three Issues
By inspection the Pareto chart shows natural breaks into three groups of three. The top three concerns (range, cost, and charging) all concern roughly 40% of respondents. That’s approximately the size of the political base that elected Donald Trump to be President of the United States in 2016.
I mention Trump’s political base to give perspective for how important a 40% rating really is. Just as 40% acceptance got Trump over the top in a head-to-head competition with Hillary Clinton, a 40% non-acceptance is enough to doom electric vehicles in a head-to-head competition with ICE-powered vehicles. So, what are the chances of technologically fixing those problems?
Lack of Range is just a matter of how much energy you can backpack onto an electric vehicle. The inputs to that calculation are how far you can drive on every Joule of energy (for comparison, 3,600 Joules equal one Watt-hour of energy) and how many Joules can you pack into a battery that an electric vehicle can reasonably carry around. I don’t have time to research these data points today, since I have only a few hours left to draft this essay, so I’m just not going to do it.
There are two ways, however, that we can qualitatively guesstimate the result. First, note that EV makers have already introduced models that they claim can go as far on one “fill up” (i.e., recharge) as is typical for ICE vehicles. That’s in the range of 200 to 300 miles. I can report that my sportscar goes pretty close to 200 miles on a tankful of gas, and that’s adequate for most of the commuting I’ve done over my career.
The second way to guesstimate the result is to watch progress of the Formula E electric-vehicle races. Formula E has been around for nearly a decade now (the first race was run in 2011), so we have some history to help judge the pace of technological developments.
The salient point that Formula E history makes is that battery range is improving. In previous events batteries couldn’t last a reasonable race distance. Unlike other forms of motor racing, where refueling takes just a few seconds, it takes too darn long to charge up an electric vehicle to make pit stops for refueling viable.
The solution was to have two cars for each racer. About half way through the race, the first car’s batteries would run out of juice, and the driver would have to jump into the second car to complete the race. This uncomfortable situation lasted through the last racing season (2018).
This year, however, I’m told that the rules have been changed to require racers to complete the entire race in one car on one battery charge. That tells us that e-technology has advanced enough to allow racers to complete a reasonable race distance at a reasonable race speed on one charge from a reasonable battery pack. That means e-vehicle developers have made significant progress on the range-limitation issue. Projecting into the future, we can be confident that range limits will soon become a non-issue.
High e-vehicle cost will also soon become a non-issue. History plainly shows that if folks are serious about mass-marketing anything, purchase prices will come down to a sustainable level. While Elon Musk’s Tesla hasn’t yet shown a profit while the company struggles to produce enough cars to fill even today’s meager electric-vehicle demand, there are some very experienced and professional automobile manufacturers also in the electric-vehicle game. Anyone who thinks those guys won’t be able to solve the mass-production-at-a-reasonable-cost problem for electric vehicles just hasn’t been paying attention over the past century and a quarter. They’re gonna do it, and they’ll do it very soon!
Charging infrastructure is similarly just a matter of doing it. It didn’t take the retail-gasoline vendors long to build out infrastructure to feed ICE-powered cars. Solving the EV-charging problem is not much more difficult. You just plunk charging stations down on every corner to replace the gasoline filling stations you’re going to close down because you’ve made ICE vehicles illegal.
The top three issues don’t seem to pose any insurmountable obstacles, so we can move on to the second-tier issues of recharging time, insufficient public knowledge, and battery life. All of these concerned just under 30% of survey respondents.
Charging time is the Achilles heel for EV technology. Currently, it takes hours to recharge an electric-car’s batteries. Charging speed is a matter of power, and that’s a serious limitation. It’s the real charging-infrastructure problem!
It takes less than a minute to pump ten gallons of gasoline into my sportscar’s fuel tank. That ten gallons can deliver approximately 1.2x109 Joules of energy. That’s 1.2 billion Watt seconds!
To cram that much energy into a battery in one minute would take a power rate of 20 MW. That’s enough to power a medium-sized town of 26,000 people! Now, look at a typical gas station with eight gas pumps, and imagine each of those pumps pumping a medium-size-town’s worth of electric power into a waiting EV’s battery. Now, count the number of gas stations in your town.
That should give you some idea of the enormity of the charging-infrastructure problem that mass use of electric vehicles will create!
I’m not going to suggest any solutions to this issue. Luckily, since I don’t advocate for mass use of electric vehicles, I don’t have to solve this problem for people do. In the interest of addressing the rest of the issues, let’s pretend we’re liberal politicians and can wave our fairy wands to make the enormity of this issue magically disappear.
Inadequate public knowledge is a relative non-issue. Electric vehicles aren’t really difficult to understand. In fact, they should be simpler to operate than ICE vehicles. Especially since the prime mover EVs use is a motor rather than an engine.
Hardly anyone I know is conscious of the difference between a motor and an engine. Everyone knows it, but doesn’t think about it. Everyone knows that to run an ICE you have to crank it with a starter motor to get it running in the first place, and then you’ve got to constantly take care not to stall it. That knowledge becomes so ingrained by the time you get a driver’s license that you don’t even think about it.
Electric motors are not engines, though. They’re motors, which means they start all by themselves as soon as you feed them power. When you brake your electric car to a stop at a stop light, it just stops! You don’t have to then keep it chunking over at idle. Stopped is stopped.
When sitting at a stop light, or waiting for your spouse to load groceries into the boot, an EV uses no power ‘cause it’s stopped. When you’re ready to go, you push on the accelerator pedal, and it just goes. No more fiddling with clutch pedals or shifting gears or using any of the other mechanical skills manual-transmission cars force us to learn and automatic-transmission cars take care of for us automatically. The biggest thing we have to learn about driving EVs is how easy it is.
There isn’t much else to learn about EVs either. Gearheads will probably want to dig into things like regenerative braking and multipolar induction motors, but just folks won’t care. If the most important thing about your ICE-powered SUV is the number of cup holders, that will all be the same in your electric-powered SUV.
Overall battery life will be an issue for years going forward, but eventually that will become a non-issue, too. Overall battery life refers to the number of times your lithium-ion battery pack can be recharged before it swells up and bursts. Ten years from now we expect to have a better solution than lithium-ion batteries, but they aren’t all that bad a solution for now, anyway.
It was annoying when the relatively small lithium-ion battery pack in your Samsung smartphone burst into flames back in 2016, and you can imagine what’ll happen if the much larger battery pack in your Tesla does the same thing when sitting in the garage under your house. But, it’ll be less of a problem than when the battery packs in airliners started going up in smoke a few years ago. We got through that and we’ll get through this!
Third-rate issues concerned 15-20% of survey respondents. They include issues around electric-motor reliability, battery materials, and vehicle designs. While they concerned relatively fewer respondents, enough people said they worried about them that they have to be addressed before EVs can fully replace ICE-powered vehicles.
Reliability concerned 20% of survey respondents. It shouldn’t. Electric motors have been around since William Sturgeon built the first practical one in 1832. They’ve proved to be extremely reliable with only two parts to wear out: the commutator brushes and the bearings. Unlike ICE power units, they need practically no regular maintenance. With modern solid-state power electronics taking the place of the old commutators, the only things left to wear out are the bearings, which take less punishment than the load-carrying wheel bearings all cars have.
Battery materials are a concern, but when viewed in perspective they shouldn’t be. Yes, lithium burns vigorously when exposed to air, and is especially flammable when exposed to water. But, gasoline burns just as vigorously when ignited by even a spark.
A tankful of gasoline can be responsible for a horrendous fire if ignited in an accident. Lithium ion batteries can cause similar mayhem, but are no more likely to do so than any other energy-storage medium.
Body size/style should not, to my mind, even be on the list. Electric-powered vehicles present fewer design constraints to coach builders than those with ICE power plants. In fact, it’s possible to design an EV chassis such that you can put any body on it that you can think of. Especially if you design that chassis with individually driven wheels, there are no drive-shaft and power-train issues to deal with.
Looking at the nine EV issues that survey respondents said would give them pause when considering the purchase of an electric vehicle rather than an ICE-powered vehicle, the only one not inevitably amenable to technological solution is the scale of the charging infrastructure. All of the others we can expect to be disposed of in short order as soon as we collectively decide we want to do it.
That charging infrastructure issue poses two problems: recharging time and recharging cost. The ten-gallon fuel tank in my sportscar typically gets me through about a week. That’s because I do relatively little commuting. I drive a round trip of about 60 miles to teach classes in Fort Myers twice a week. The rest of my driving is short local trips that burn up more than their fair share of gasoline because they’re stop-and-go driving.
In the past, I’ve had more difficult commute schedules that would have burned up a tankful of gas a day. Commuting more than 200 miles a day is almost unheard of. So, having to sit at a recharging station for hoursto top up batteries in the middle of a commute would be an unusual concern for a commuter. They would top up the batteries at home overnight.
Road trips, however, are another story. On a typical road trip, most people plan to burn up two tankfuls of fuel a day in two 4-5-hour stints. That’s why most vehicles have fuel tanks capable of taking them 200-300 miles. That’s about how far you can drive in a 4-5-hour stint. So, you drive out the tank, then stop for a while, which includes spending a minute or so refilling the tank. Then you’re ready to go on the next stint.
With an electric vehicle, however, which has to sit still for hours to recharge, that just doesn’t work. Instead of taking two days to drive to Virginia to visit my daughter, the trip would take most of a week. Electric vehicles are simply not suitable for road trips unless and until we solve the problem of supplying enough electric power to an EV’s battery to supply a small town!
Then, there’s the expense. If you’re going to recharge your EV once a week (or top it off from your wall outlet every night), you’ve gotta pay for that energy at the going rate. That 1.2 billion Joules translates into 333 kiloWatt hours added to your light bill every week. At a typical U.S. electricity rate of $0.12/kWh, that’s about $40. That may not seem like much, but compare it to the $25 I typically pay for a tankful of gas.
In conclusion, it looks like EVs will eventually do fine as dedicated commuter vehicles. They’ll cost a little more to run, but not enough to break most budgets. For road trips, however, they won’t work out well.
Thus, the answer to the question: “Can electric vehicles fully replace gas guzzlers?” is probably “No.” They’re fine for intra-city commuting, or commuting out to the suburbs, but unless Americans want to entirely forgo the possibility of taking road trips, ICE-powered vehicles will be needed for the foreseeable future.
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.
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.
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:
Determine all goals of all stakeholders;
Assign a relative importance (weight) to each stakeholder goal;
Use a Likert scale to quantify the degree to which each stakeholder goal has been met;
Normalize the scores to work out an index for each stakeholder goal;
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.
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.
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.
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.
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.
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.