Height advantage in hiking

For an outdoorsy, not-so-tall girl, it’s not uncommon to wind up at the back of a pack of significantly taller, male hiking companions. Sweaty and panting, I watch their backpacks recede further away up the trail, and even the sweep guy might abandon his role to bolt around me. In an endurance situation, mental fatigue sends the foggy brain into rhythmic, ineffectual loops. Unable to do mental arithmetic while moving, one can only see that the negative space triangle formed by others’ legs is larger for taller people, and imagine that this reflects some advantage… but how much advantage?

Later, off the trail, pen and paper in hand, one can focus on calculating the magnitude of how much this height advantage adds up to, in terms of explaining how a physically fit person might lag so far behind:

Height of the taller person inches
Walking cadence steps per minute
Stride angle (angle between legs at full extension point) degrees
Height of the shorter person inches
The taller of the two hikers, being inches tall, has an assumed leg length (measured from hip joint pivot point) of inches. Given his stride angle of degrees, he takes steps that are inches long. At his walking cadence of steps per minute, he thus hikes at the rate of miles per hour.

Meanwhile, the shorter person has an assumed leg length of inches. Despite using the same stride angle and walking cadence as her companion (i.e., putting in the same amount of effort), each of her steps is smaller and she therefore covers ground more slowly...merely due to being shorter!

In order to keep up, the shorter person must work harder, by either:
(a) making her small steps more rapidly, at a faster cadence of steps per minute; or
(b) matching her companion's same walking cadence, but making each step longer by using a wider stride angle of degrees. (As efficiency-conscious runners well know, increasing step length beyond what is optimal for one's height has a dramatic effect on tiredness.)

Alternatively, if the shorter person exerts only the same effort as her taller companion, she will fall behind miles per hour of hiking. In such case, the taller person will have to wait (and get to rest!) minutes every hour, while waiting for the shorter person to catch up.

[Note: We’ve made the simplifying assumption of leg length as a fixed proportion (45%) of overall height – a reasonable constant, given that average ratios of leg length to height, and step length to leg length (a function of stride angle, which correlates positively with speed) enable trackers to infer height from footprints.]

Other factors driving differential physical effort between two companions are undoubtedly afoot during a hike: aerobic fitness, anaerobic endurance, strength-to-weight ratio, movement/form efficiency, backpack contents, stomach contents, sufficiency of recent sleep, injuries, performance of clothing/gear, and who’s chatting more than listening. Still, the point here is that leg length alone has a substantial impact on rate of travel. Regardless of which physical issues contribute to the exertion asymmetry, the optimal solution for both hikers (assuming they value fairness and social interaction) is to “put Herbie in front” — i.e., have the disadvantaged hiker set the pace.

Eli Goldratt’s 1984 classic The Goal vividly illustrates this principle of operational efficiency with….a hiking example! Herbie (the fat kid in the book; in our case, the short hiker) is the bottleneck. When the fast kids hike at their own pace with Herbie in the back of the single-file line of boy scouts, Herbie falls behind. They impatiently wait for him at trail intersections, but only to immediately take off hiking again as soon as he catches up and before he catches his breath. Herbie gets more and more tired, and thus even more physically disadvantaged, since fatigue initiates a negative feedback loop in terms of physical performance. Meanwhile, the fast kids get periodic rest, and so the effort differential increases from both directions. Putting Herbie in the front of the line — combined with distributing his backpack load among the fast kids — ensures that the hikers stay together and evenly spaced, and that the physical effort difference is somewhat lessened. (The effort saved by fast kids hiking slower than their capabilities is less than the effort saved by Herbie avoiding being in chronic, desperate catch-up mode.)

Posted in Interactive calculators, Main, Math is everywhere!, [All posts]

Obesity is not a choice: The ignorance of pseudo-libertarians

5 min read

Obesity isn’t a “choice”:  it’s neither rational nor conscious.

The late 20th -century revolution of behavioral economics enlightened us to the fundamental ways in which the human brain fails to respond rationally or consistently to information.  We came to a radical new understanding that “free will” isn’t actually free.

We also realized that “unregulated markets” are not “free markets”.  First, economists realized that unregulated markets give rise to the ills of monopoly power, barriers to innovation, widening income inequality, volatility, and the ability of a few individuals’ errors to trigger economic cataclysm (e.g., LTCM hedge fund collapse, 2007 mortgage securities crisis).  Second, economists realized that individual consumers – and therefore aggregate groups of consumers known as “markets” – are guided by intrinsic human irrationality. 

Thus, libertarianism definitively died decades ago among trained economists, becoming a theoretical curiosity in the history of social science.  However, as is always the case with scientific discoveries and academic research consensus, there’s a long delay between a realization in high culture and its gradual diffusion into popular/low culture. 

Consequently, libertarianism remains trendy among non-economists.  These pseudo-libertarians claim to advocate for unfettered free markets, ignorant of the settled science that removing governmental oversight and intervention often contravenes their own stated objective.

Let’s take the obesity epidemic as an example.  

Half of American schoolchildren are medically obese.  As they grow up, the adult population is getting more obese as well, and suffering costly and tragic health consequences like diabetes.  And, the problem has expanded beyond America to become nearly global.  Despite the vast population affected, it’s not uncommon to hear skinny and privileged American pseudo-libertarians sanctimoniously and counter-factually dismiss the obesity issue as one of “individuals making bad choices”.

What do we know about the causes of obesity?

  • Childhood obesity.  Default body size is set in childhood, when one has no autonomy over food choices.  Adults almost always revert to their childhood body type, regardless of short-term impact of exercise and nutrition programs.  (e.g., Even with infinite financial resources, professionally-optimized nutrition, a relatively non-sedentary job, and strong psychological/social incentives, Oprah Winfrey’s body always reverts to its adolescent, unhealthily-large size.)
  • Parental obesity.   The size of one’s parents impacts whether you will grow up to be obese yourself (both through overtly controlling your childhood nutrition and exercise, and through subconscious modeling of behavior).  
  • Obesity of your friends.  Behavioral economics research shows us that, if just one of your friends gains weight, your likelihood of gaining weight goes up.  (Even if one of your friend’s friends gains weight, your likelihood of gaining weight goes up.)  This creates a positive, self-reinforcing feedback loop outside of conscious control.
  • Lower metabolic rate, caused by:
    • Changes to our microbiome.   The bacteria colonies inside us that account for a few pounds of everyone’s body weight are newly suspected as an important factor (requiring more research to pinpoint).
    • Desk jobs.  Sitting is the new smoking.  Most desk jobs don’t yet support the recommended frequent breaks, walking meetings, standing desks and reasonable hours required to keep activity levels high.  Exercise is expensive and relatively unavailable to low-income people.
    • Time of the month relative to payday.  The metabolic rate of people living paycheck-to-paycheck actually decreases prior to payday, to conserve energy and store fat.  (Then, after spending a week in constant, uncomfortable hunger, they may overeat when cash becomes available.)
  • Overeating, caused by:
    • The number of people you eat with.  Kids who eat in larger groups at school eat more calories than kids who eat in small groups, given the same meal offered.  
    • The size of the plate.  We eat more calories when the same quantity of food is presented to us on a large plate, compared to a small plate.
    • Carbohydrates are the cheapest food (both per calorie and per unit of satiety).  If you’re poor and you’re hungry, it’s rational to eat bread, ramen and sweets.
    • Branding and marketing.  When you walk into a grocery store you relinquish any remnants of free will regarding food choices.  Food producers use teams of brilliant minds to spend enormous sums of money on manipulating your purchase behavior.  They use color, packaging, shelf position, and price.  And, those features don’t have any correlation with nutritional value – in fact, low-nutrition prepared foods have the highest margin and are manufactured by the largest companies, who thus have the resources and motivation to get you to buy those products.  Who are you to withstand the onslaught of subconscious influences?
    • Cultural norms.  Kids and adults unconsciously prefer the taste of foods they believe are unhealthy.  Our controlling cultural paradigm often guides our preferences away from nutrition.
    • Office policies.  Visibility and accessibility of food definitively drives consumption of more calories.  If your well-meaning office puts out trays of food in meetings and at birthdays, you have little defense against unconsciously consuming more calories than you otherwise would.
    • Lack of data to support nutritious choices.  Consumers don’t usually know which food choice is the least nutritious.  A pasta dish can have more sugar than a handful of cookies; a salad can have more carbs than a plate of meat and potatoes.  
    • Unclear definition of nutritionally-optimal choice.  Is added sugar the real culprit?  Total sugar, including natural sugars like in fruits?  Carbohydrates?  The latest science isn’t yet reflected in nutritional labels, the governmental recommendations are confusing, and good information is drowned out by the noise of pseudo-scientific theories.
    • Bandwidth poverty.  Even if we had the right data about food content, and a clear definition of optimal nutrition decision-making, an individual doesn’t have the time to research and scrutinize every micro-decision made in a day.  That’s precisely why we have consumer protection and labeling laws (which need to be vastly improved regarding grocery and restaurant foods).  Bandwidth poverty is highest among low-income people, who have been shown to actually make more daily decisions than corporate executives. Corporate executives get to reduce their decision fatigue with capsule wardrobes, personal outsourcing, and punting on many smaller decisions they can afford to make sub-optimally – leaving them more luxurious mental bandwidth to sift through complex nutritional choices.  Poor people don’t have that luxury.

Notice that none of these obesity drivers are under the conscious control of an individual.  So, we see that the free market ideal of informed consumers making conscious, rational decisions is entirely absent with respect to the obesity issue.  It is not a choice.

Posted in <5 min read, Decision quality, Social issues, [All posts]

Think a Muslim ban is irrelevant to you?

7 min read

An indignant acquaintance demanded to know “why” I (“of all people”) am so upset about the latest of Trump’s outlandish executive orders.  That person didn’t voice objection to my participation in the recent Women’s March, but is befuddled by my response to what is effectively the beginnings of a Muslim Ban.  Here is my answer.

I shouldn’t have to recite my degrees of separation from how this “personally” affects me.  Perhaps it does, perhaps it doesn’t.  (Nonetheless, it’s important to note that – even as a white, Anglo-Saxon, Protestant, upper-middle class, graduate-educated woman – only an uncritical mind could simply assume it doesn’t affect me personally.)  I decline to recite any so-called personal reasons, since personal impact shouldn’t be the primary litmus test used to determine policy stance.  

The policy is a bad one on its own merits, on an empirical basis: 

  • Doesn’t accomplish it’s stated goal.  This is a major windfall to terrorists.  
  • Appears to be commercially-influenced.  Where’s Saudi Arabia, home of the 9/11 hijackers?  Why does the country list align with Trump’s business interests and not with US intelligence assessments?
  • Hurts our diplomatic and economic interests.  Reciprocity of banning individual Americans is only the beginning of how other countries will respond.  
  • Violates our nation’s moral principles.  So much for religious freedom.  Thomas Jefferson, who kept a Quran by his bed, weeps in his grave.
  • Expensive redundant regulation that should enrage small-government apologists.  We’re already doing extreme vetting of asylum-seekers.  US residents were already vetted in order to get their green cards.
  • Statistics and Skittles.  We’re more at risk from European passport-holders.  And the Trump family’s dehumanizing Skittles analogy was debunked last year with a dump-truck pile of colorful, fruity candies.
  • Experts across the political spectrum also think it’s empirically misguided.  Dick Cheney and Madeline Albright are outraged.  The ACLU quickly won in court against the poorly-worded, sloppily-conceived executive overreach.

Like most of Trump’s campaign promises and first-week executive actions, this one preys upon our irrational fears and reduces us as a people.  

Instead of thinking through the above points, people commonly define their stance based on whether the issue affects them.  That selfish evaluation mechanism passes for a socially-acceptable method of arriving at one’s political opinions.  Case in point: the weekend before last, a self-proclaimed liberal millennial spat out this red-faced proclamation to a gathering of his left-leaning friends: “My life won’t change one bit under Trump. It will never touch me personally, so I don’t care”.  His stunning lack of self-awareness, compassion and insight was only mildly rebuked.  Narcissism is now a political ideology. 

The politics of narrow self-interest engendered, for example, the Frankenstein phenomenon of Bernie-fanatic, big-government Libertarian, pro-trade isolationist, anti-science Musk-worshipping “benevolent” misogynists who label themselves as “politically progressive”.  Here in Denver, I’ve seen those people spend the past year-and-a-half articulating distaste for Trump and the alt-right… by unironically using Trump-style logic and rhetoric. 

Among the many morality lessons Trump offers us is a large-scale demonstration of what the politics of narcissism produce:  He’s on logically inconsistent sides of issues, appears to not think through consequences, proposes policy counterproductive to his stated goals, obsesses about ratings, makes quick enemies of would-be allies and is already perceived by the world as a petulant child (and is allegedly having tantrums like one).  If you can’t see why a Muslim Ban is bad without feeling affected yourself, or knowing someone affected, or knowing that I know someone affected, then you fundamentally share some of Trump’s worldview.

“First they came for…” is a moving argument.  Self-interested complacency will eventually violate your own self-interest.  But, it’s not persuasive to many today.  (And, it’s not widely known. Nobody I’ve asked in Denver since November 9 is familiar with that famous poem by an anti-Nazi German pastor whose activism sent him to a concentration camp.)  Too many people can’t imagine the logical succession that leads from attacks on Others to attacks on themselves.  They seem incapable of grasping that that we’re all sitting at the bottom of a slippery slope, no matter our perceived current level of comforting privilege.  

People don’t see when they are already being adversely affected (hence the common practice of unwittingly voting against one’s self-interest). They don’t consider effects on their family members, friends, and colleagues when they decide if something affects them (hence the common practice of not advocating for rights of other genders, races, religions, etc).  “Me” is literally only me — not me plus people who affect me and matter to me.

Last year, millions of millennials (and tens of millions of people overall) were vociferously certain that voting in elections doesn’t matter – because they couldn’t fathom how any presidential, congressional, supreme court or other judicial, regulatory agency, gubernatorial, state legislative, mayoral or city council actions would ever impact their lives.  That is both an error of not realizing that they will indeed be adversely affected, and also an error of not caring that people they know will be adversely affected.  Therefore, it’s not a great argument to ask people to realize that they could be harmed personally by a Muslim Ban, or to expect people to care that they know other people who are harmed by a Muslim Ban.  Waiting for such epiphanies is like waiting for “demographics” to organically eradicate right-wing extremism in America.  

We can’t wait for that, because, for example:

  • A guy just said he’s finally speaking out against Trump because the temporary ban on arrivals by citizens of 7 countries “directly affects people” he knows.  However, though he has many women friends, a mother and a sister, he hasn’t felt outraged to speak against Trump’s anti-women rhetoric and policies.  (Error of inconsistent recognition that policies harm people you know.)
  • Lots of women voted for Trump, and though many undoubtedly already have a pit in their stomach about that decision, many didn’t see the post-inaugural Women’s March as relevant, because they “feel equal” (though empirically they are not, in terms of healthcare, safety, wages, legal protections, etc).  And, failing an accurate self-assessment, it still wasn’t motivating enough to them that their female and male friends do experience and observe gender inequality and thus chose to march.  (Error of not realizing that you are harmed.  Error of not caring that people you know are harmed.)
  • A white woman whose politics should make supporting Black Lives Matter axiomatic hasn’t supported that cause because the one black person she knows is the ex-husband she resents.  (Error of not caring that people you know are harmed.)

We can’t wait for people to realize that an issue does indeed affect them “personally”.  We can’t wait for people to recognize that they do know people who are “directly affected”.  We can’t wait for people to cross paths with individuals in an at-risk group and deem them likable enough on an interpersonal level to warrant concern.  On that last point:  We can’t demand support of women’s rights because women are sweet, or racial equality because we like hip-hop …. or religious freedom because we enjoy the family-owned Syrian falafel restaurant downtown. 

In the above cases, we could explain how this policy could eventually “personally” affect the now-indifferent person.  We could try to awaken them to recognize the link, to draw their boundary of self a little wider.  But such politics of narcissism leads to conclusions as unstable as the status of our personal relationships and our ever-shifting senses of identity and allegiance.  Moreover, it simply shouldn’t be necessary.  It should be sufficient that the policies hurt anyone, lack integrity with our values as a nation, and contradict empirical evidence.

So, in answer to your question:  No, I won’t satisfy you with my “personal” reasons for sorrow and outrage at what happened at our airports over the weekend.  It requires no justification other than I’m a human being who thinks critically.  Reciting my personal association with the issue would be a capitulation to your politics of narcissism – arguing my point on your parochial, self-interested terms.  

Men should attend women’s rights marches.  White, Asian, and Hispanic people should rally for Black Lives Matter.  Straight people should protect gay marriage.  Those not of child-bearing age/gender should defend access to contraception and reproductive choice.  Rich people should vote against taxes, tariffs, and program cuts that burden low-income people.  Millennials should care about losses to Medicare if the ACA is repealed.  Retired people should favor better public education.  Humans today should mobilize to mitigate climate change’s harm to future generations.  Those who don’t happen to live on the Standing Rock Reservation should care about the Dakota Access Pipeline.  Non-Arab, non-Muslim US citizens should be outraged about passport-based travel restrictions on US residents and visitors.  “Period.”

Denver, January 29, 2017

Posted in 5-10 min read, Decision quality, Religion, [All posts]

The very first nasty woman

4 min read

The first woman was also the first “nasty woman”.  (I refer to the misogynist insult – invoked by Donald Trump against Hillary Clinton in the October 2016 debate – now co-opted as an empowering feminist battle cry.)  Like women still experience today, the mythical first female human was punished for the sin of having ambition.   

Nearly 3000 years ago, a Jerusalem priest wrote down an old etiological myth:  Chava (a pun on the word for “life”, transliterated into English as Eve) ate from the figurative Tree of the Knowledge of Good and Evil.  Her nameless male companion also partook.  God perceived this as an attempt by humans to become wise like him.

But it is Eve who incurs horrific retributive intergenerational punishment for women: increased pain in childbirth and permanent loss of gender equality.  For his equally ambitious act, the as-yet-unnamed man incurs the punishment of farming difficulties.

Laws are symptomatic of the need for laws.  A given society’s rules tell us what is common behavior in that society, in that a rule was deemed necessary to curb or prevent that behavior.  This startlingly profound solipsism yields important realizations about Biblical times, such as:

  • Homosexuality was common (as it has been in most cultures in most historical periods). We know this because Hebrew scriptures felt the need to proscribe homosexuality (though unevenly so, and not as brutally as right-wing apologists’ cherry-picked text quotations suggest).
  • Men knew women have high sex drives (as most cultures through most historical periods have known, with the notable exception of present-day America – in fact, evolutionary biologists posit that pro-sex female physiology reflects the social incentive of primitive women for multiple sex partners in order to achieve partible paternity to ensure offspring survival under resource scarcity). We know that men knew this, because they felt the need to create myriad rules to control women sexually – keeping us covered up, out of sight, and generally separate from what could tempt our wanton sexual appetites.  Hebrew scripture says women are to be more severely punished for adultery than are men….because, by implication, women are proactive in seeking those adulterous encounters. (Today, men accuse women of wanting sex in the form of rape because…men believe women really want sex that much.  And, yet, men simultaneously whine that women don’t really want sex very much.  That self-serving, contradictory logic deserves its own essay.)      

Similarly, we see that, since prehistoric times, women have been blamed as overly-ambitious for doing the exact same thing as a man does.  Men have always felt the need to punish women for being ambitious because… women are in fact ambitious, just like men are.  According to the foundational text of our Judeo-Christian culture, the reason God punished women is that Eve showed ambition (which was more threatening coming from her than from her male companion). 

I was recently up for a job to be the first woman in a mid-level (or higher) position at a Philadelphia-based consulting firm.  After 4 months of interviews and discussions, I was informed that I “seem too ambitious and enthusiastic”.  Ironically, I had meanwhile actually been thinking how to convey more enthusiasm to the prospective employer, lest my gentle female aura be misinterpreted (as is so often the case in the corporate world) as lack of ambition.  

Such unfair, gendered (i.e., illegal) criticism is commonplace – and at this point, its ubiquity should also be common knowledge.  In this instance, it’s even more absurd because consulting businesses depend on their advisors being forthright and often contrarian to powerful clients, as well as ambitious and tenacious in the never-ending task of prying open reluctant corporate wallets to secure new 6- and 7-figure projects to fill the pipeline.  Consultants without copious amounts of ambition and enthusiasm don’t last very long.

However, the male cabal that installs and assiduously maintains glass ceilings usually perceives (a) the intangible emotional cost of de-biasing themselves towards women as greater than (b) the tangible financial cost of missed business opportunity.  Irrationally, they prefer to reach twice as deep down into a pool of male candidates, than to hire the top people from a combined, gender-agnostic candidate pool.  And, by conveniently ignoring their false negative rate of rejecting competent women, they convince themselves that their scraping-the-bottom-of-the-male-barrel hiring system “works perfectly well”. 

Women are forced to put far more effort (and enthusiasm?) than men into conveying our competence, intensity and motivation.  We have to combat the pervasive a priori bias among (still mostly-male) corporate gatekeepers that women are less inherently competent, less hard-working (because surely I have children and am their primary caretaker, or will clock out early to pursue my true passions of scrapbooking and knitting, or will slack off when I’m “bleeding out of my whatever”), and less in need of a job (because surely I am just some man’s secondary income, or it’s simply audacious for me to aim to thrive and I should suffice with mere passive survival).  However, once we “lean in” to demonstrate those desirable qualities, we are often punished with accusations of being arrogant (it’s unseemly for women to tout accomplishments, or to ask for a market-level salary at parity with male peers), too emotive or enthusiastic… and too ambitious. 

The enduring reality is that if that the owners of that consulting firm don’t want a woman in their inner circle or upper ranks, they will continue to get away with it.  They can safely look down on me through their impenetrable glass floor, smugly congratulating themselves for honoring a 3000-year-old paradigm of prejudice against women’s ambition.  

Posted in <5 min read, Business topics, Personal, Religion, Social issues, [All posts]

Bad stats at stats firms

5 min read

Convergys Corporation does data analytics to identify operating cost reduction opportunities for its call center clients.  They ask prospective analysts to do a take-home analysis of 120 rows of call center data.  I’m 20 years past being an analyst, but a networking acquaintance encouraged me to apply as she believed her firm would love to have a quantitative expert on staff.  And I would have been extraordinarily happy to have the work.

I asked this acquaintance if I should dumb myself down, but she assured me that this firm is an ego-less meritocracy for which there’s no need to self-censor.  So, I made a 10-page PowerPoint with data visualizations so slick that I’ve since used them as samples of great data visualizations.  Storyboard structure, trend summary (by agent, agent tenure, day of week, over time, by topic, etc), hypothesized causal relationships to investigate outside the data, Tufte-style clean design, and concrete next steps to pitch a meaty engagement to the fictive prospective client.  I hit this thing so far out of the park it landed in the next stadium.

In addition, I explained to the reader that all inferences are preliminary, because the low sample size translates into large margins of error, even at an aggressively low confidence level.  In fact, there were no statistically significant differences in the data Convergys provided.  None.  All of the blips in those 120 rows of data have a strong chance of just being random noise.  The margin of error is bigger than the effect size we’re trying to identify.

This is a situation where they either haul you in to fix the whole department or reject you in terror at realizing they don’t understand their own career domain.  In Convergys’ case, I never heard back – not even a “thanks-but-no-thanks”.  And, – perhaps needless to say – that acquaintance never returned email or call again.  

Twenty-ish years ago, after I got the figurative Golden Ticket of a premier Wall Street internship, I discovered a calculation error in the Goldman Sachs M&A Group’s Excel valuation model.  This was the model that the most elite department in the most elite investment bank used to value every multi-billion-dollar, front-page-WSJ transaction.  They were proud of it, and asked us 8 carefully-selected interns from the top business school programs to check the model in our spare time – issuing a challenge they believed was ironclad.   I was the only one who found something.  I wrote a gentle email explaining the issue – and in obsequiously non-threatening, self-deprecating, know-one’s-place-in-the-world female fashion, gushed that this is not a material error in almost any case but an error nonetheless, relayed to them respectfully in direct response to their own request.

Needless to say, I wasn’t popular.  My male boss at Goldman Sachs told me to “go shopping” since there were no deals available for me to be staffed on.  

That same summer in Manhattan, I got a glass of lemonade with a floating piece of lemon that still had its sticker attached.  Meaning they didn’t wash the lemons to make the lemonade, and so not likely the fruit for the fruit salad either.  Arguably, since produce stickers are food-grade and edible, it’s not a material error.  But, it tells you about the level of care and competence in the kitchen, and makes you wonder what more egregious issues you didn’t catch.  And, finding paper in a mouthful of food is gross.

But, – again, perhaps needless to say – the waiter was pissed at the request for new lemonade.  

The next year, I visited my new fiancee’s parents’ house for the holidays.  He had been a Harvard math whiz.  He thought it would be fun to get out one of his old statewide math competition tests and have the two of us compete to do the test.  I got the same score as he did – despite the fact that he was a math major (I was literature), he went to Harvard (I to one of the “lesser” Ivies), and he had done the test in high school (it was sight unseen to me).

In a flash of rage at the outcome, he threw his pencil across the room.  (Needless to say?)

Once I did get hired by a consulting firm explicitly to revamp their numeric capabilities – after I pointed out that their Fortune 50 pharmaceutical client was making global business decisions on data “trends” indistinguishable from random noise.  Neither the consultancy nor their clients knew what a margin of error was or how to calculate one.  I brought in several new tools to improve decision quality and lower decision analysis costs: Bayesian methods, lower confidence levels, better survey design, reliably automated calculations, hypothesis testing to replace data mining, and framing research questions within business strategy. 

My boss was embarrassed, needless to say.  But he was eager enough for a competitive edge to stoically ride through his discomfort.  It was my own loyalty that faltered in this case:  when I found him cooking the books to his subcontractors, I blew the whistle.

But, then there was the time, at age 26, I explained Monte Carlo simulation to an insecure, hot-headed, community college-educated boss, and he punched a wall in frustration that he couldn’t follow me.  The CEO sought me out directly, bringing little ol’ me to the Board meeting to explain it directly to the Board.  (And he arranged a fat bonus for me, and he gave me a promotion.)  Impressive leaders don’t feel they need to know more than their quant analysts.  But, those leaders are the rare exceptions.  In twenty-some years since, I’ve never again met someone so unthreatened by a smart employee.  (I would have eagerly defined my career by following that CEO, but a few months later he died of cardiac arrest while jogging.)

The story at hand — of not getting a callback from Convergys — has many themes:  It’s a story about the consequences of being overqualified for a position and of being smarter than the boss, a story of a would-be academic mind forced to grovel for acceptance in the daft corporate world, a story about the psychological barriers to innovation and learning, a story about humans projecting destructive competition onto situations that should be about productive collaboration, and a story most concretely about innumeracy among self-proclaimed numerati.

Falling over oneself to apologize for one’s own competence isn’t usually enough to mitigate other people’s terror at confronting their own incompetence.  However, hiding your competence won’t get you a good job, a promotion, or a satisfying career either.  The solution, as with most issues in life, is finding a strategic middle ground.

The unfortunate reality is that many firms using and/or selling data analytics mess up basic statistics.  And yet the world keeps turning.

Posted in <5 min read, Business topics, Math is everywhere!, [All posts]

Progressives v. Conservatives: It’s all about fear of Type I or Type II errors

25 min read

Chatting with a libertarian friend over at a wine bar before the recent election, I was reminded how fear motivates conservatives to advocate against social welfare programs.  My friend, for example, is so fearful of welfare fraud and voter fraud that he says he’d prefer to eliminate social programs in order to avoid the possibility of abuse.  Strong emotion blinds him to the fundamental trade-off implicit in his policy position.  A more constructive conversation focuses explicitly on the trade-off calculation and its consequences.

How fine is your sieve?

Political leaning boils down to what kind of evaluation “sieve” you prefer.  Are you more worried about “letting in” someone “undeserving”, or “keeping out” someone “deserving”?   Conservatives worry more about the former and progressives worry more about the latter. 

For policies of inclusion or assistance, conservatives fear false positives and progressives fear false negatives.  Conversely, when it comes to policies of exclusion or punishment (e.g., criminal justice), the concern is inverted: conservatives fear false negatives and progressives fear false positives.

My libertarian friend’s evaluation sieve has an extremely fine mesh:  He is willing to refuse help to needy people, in the hopes of ensuring that no non-needy people ever pass through the sieve to receive public assistance.  Similarly, he believes that disenfranchising eligible voters is an acceptable trade-off to prevent any cases of voter fraud from slipping through. 

My sieve is more liberal, with a coarser mesh:  I am more focused on the ethics of refusing help to needy people than I am with a few low-income people improperly slipping through.  I am willing to accept that some people will undeservedly pass through a coarse sieve to receive non-needed welfare benefits – but I put far more weight on ensuring we don’t fail to serve the truly needy.  Similarly, I know that if we make it easy for all eligible voters to vote, we might correspondingly see a few more voter irregularities – but I view that trade-off as socially and ethically beneficial. 

  • My friend the conservative is afraid that a soft, “gullible” system will make a mistake of over-inclusion and cost him too much money.
  • I the progressive want to prevent a cold, “blind” system from being so overly-exclusive that we miss opportunities and fail to meet ethical obligations.


In contrast to this particular libertarian friend, when I do the trade-off arithmetic, I consider the actual rate of the undeserving passing through the sieve our system currently uses:  Actual incidence voter fraud in US federal elections is documented to affect approximately 0.00001% of ballots.  Reducing an error rate from 0.00001% to 0.000000% is prohibitively expensive and practically impossible.

Type I and Type II errors

How would my libertarian buddy and I decide which individuals need our taxpayer-funded help?  We administer an evidence-based test (i.e., use a decision-making sieve) and make an inferential assessment.  That assessment might correctly describe the true situation… or it might be a wrong conclusion.  The combination of our inferential conclusion (help or no help offered) and unknown true situation (help or no help needed) leads to four possible outcomes:

  Null hypothesis is false Null hypothesis is true
Assessment Null hypothesis is false True positive
Type I error
False positive
Alpha error
Wrong assertion
Null hypothesis is true Type II error
False negative
Beta error
Missed opportunity
True negative
“nothing to report”

Coming to a wrong conclusion is always a bad thing.  Depending on the situation and one’s value system, either a Type 1 (false positive) or Type II (false negative) error is comparatively worse.

  • Type I error = Incorrect rejection of the going-in assumption (null hypothesis). Seeing something that isn’t there.
  • Type II error = Incorrect acceptance of the null hypothesis. Failing to see something that is there.

Type I and II error rates are dictated by the test’s confidence level, the data sample size, the effect size being measured, and the background prevalence of the phenomenon being measured.  Administering a test with a high confidence level means that it takes a lot of evidence/effort to reject the null hypothesis.  Thus, we’re less likely to have Type I errors — but instead we are, by definition, more likely to have Type II errors.  Conversely, selecting a low confidence level generates more Type I errors in exchange for reducing Type II errors.  

Test design involves an inescapable trade-off between two types of error.  You MUST choose which one matters more in each situation.  In other words: Would you rather be gullible or blind? 

Confidence vs power

Welfare program eligibility

Null hypothesis:  People generally don’t need public assistance. (It’s a safety net, not an automatic benefit.)
Burden of proof:  Applicant must provide extensive documentation to prove eligibility

Assessment FALSE Help many people who really need it Accidentally help a few less-deserving people
TRUE Fail to help some people in dire need Turn away those who don’t qualify for help

Social welfare was the first example in my wine-fueled libertarian-vs-progressive debate.  In this system, we are selecting for inclusion – evaluating applicants to determine who deserves assistance.  This evaluation “sieve” presumes that public assistance is not an automatic benefit, but rather a “safety net” for when structural economic conditions cause exceptional individual suffering.  In statistics terms, the null hypothesis is that an applicant doesn’t qualify for help.  The burden is on the applicant to provide evidence that they meet the government’s qualifying threshold.

Like all modern nations, the United States offers various “welfare” programs because of both (a) the moral imperative and (b) the economy-wide benefit of limiting desperation among the poorest.  For example, giving cash to poor single mothers of infants correlates to their children having higher IQ, lower lifetime medical costs, and less criminality.  Our society as a whole reaps long-term economic benefit from government spending money on welfare programs.

The progressive leaning is to worry most about the sin of failing to help the needy.  Turning away a desperate, impoverished human being means that the decision process was too skeptical — we used too high of a confidence level for the evidentiary test.  As a result, that test wasn’t powerful enough to include enough of the people we hoped to help.  The “sieve” was too fine.

Conservative rhetoric often claims that there are untold numbers of well-to-do or lazy people free-riding on the welfare system.  (Usually, as in my wine bar friend’s case, such passion is based on extrapolation from one or two anecdotes, rather than on data.)  Their illogical argument in an appeal to the emotion of fear, suggesting that we’re being tricked and letting our tax money be frivolously deployed.

The progressive counterpoint is to consider data about who those “undeserving” Type I error beneficiaries actually are as individuals and families.  Arguably, the inevitable Type I mistakes aren’t such clear-cut mistakes — nobody enduring the shame of welfare and living off its parsimony has anything close to an easy life.  It should perhaps give the conservatives some solace to consider that false positive welfare recipients are still poor, and so there’s thus undoubtedly still some multiplier effect creating a secondary benefit to society.

Re-framing the policy debate as a less-emotional discussion about the relative costs of Type I and Type II errors enables mutual understanding – and perhaps a path to compromise:  What is the false positive and false negative rate of our current system?  What are the financial and social costs of each type of error?  What trade-off are we willing to accept between accidentally excluding the needy and accidentally including the less-needy?

Criminal justice

Null hypothesis:  Accused person is innocent
Burden of proof:  Prosecutor must prove guilt, beyond a reasonable doubt

Assessment FALSE Acquit innocent person Convict innocent person (4%)
TRUE Acquit guilty person Convict guilty person

In the criminal justice system, we are selecting for exclusion – evaluating the accused to determine who deserves punishment.  The threshold of reasonable doubt intentionally makes it difficult to reject the null hypothesis of innocence.  Our system recognizes that convicting an innocent person (Type I error) is morally much worse than acquitting a guilty person (Type II error).  That trade-off is made even more morally obvious because convicting an innocent person almost always means that a guilty person has remained unpunished for the crime in question.

The progressive viewpoint is to worry most about convicting the innocent.  A false conviction means that the jury wasn’t skeptical enough of evidence – it used too low of a confidence level for the evidentiary test.  In the past few decades, America has been shocked awake about the staggeringly high false conviction rates in our criminal justice system.  One in 25 death sentence convictions have subsequently been proven false.  That’s a 4% Type I error rate… where the moral consequences of every single uncorrected error are astronomical.

Conservatives traditionally use rhetoric grounded in fear of acquitting guilty people.  Indeed, sometimes the null hypothesis is implicitly characterized as guilt, challenging the accused to prove innocence.  The problem with this inversion of the US constitution is not the oft-repeated idea that establishing certainty about the non-existence of something (guilt, god, bigfoot) is challenging.  (Establishing absolute certainty about the existence of those same things is also challenging.)  Rather, presumption of innocence is a universal human rights standard because society has agreed that false convictions are worse than false acquittals.  Placing the burden of proof on the accuser is intended to limit Type I errors (and to ensure that accused innocents are treated well all the way through the process until they are, hopefully, acquitted).  

Stop-and-frisk practices 

New York City’s police department infamously accepts a very high Type I error rate (frisking innocent people) in hopes of lowering their Type II error rate (failing to prevent crime, by not frisking gun-toting troublemakers).  The practice trampled the right of hundreds of thousands of innocent young black and Latino men to walk around in their own neighborhoods… and has failed to significantly lower crime rates.  The very fear used to justify the practice is left unquelled by the practice’s abysmal results in crime prevention.

This poorly-designed stop-and-frisk “test” yields an astonishingly high Type I error rate of 98.2% (% of stops where no gun is found) and thus only a 1.8% positive predictive value (% of stops where a gun is found).  

Re-stating the debate as a difference in relative concern about Type I versus Type II errors is useful.  We can perhaps nudge conservatives and progressives out of the deadlock of conflicting value systems and into dialogue:  How can stop-and-frisk advocates explain why abrogating rights of 49 people to find 1 gun is an ethically defensible trade-off?  Could we create alternate “tests” for guns that have a higher predictive value, and thus a less egregious civil rights cost?  If we continue this practice with such a high false “conviction” rate, how can we soften the real person harm of all those false positives?

Hiring and promoting women

Anti-woman bias in the workplace equates to a high false negative rate in hiring and promotion.  Systematically failing to acknowledge and reward women for their valuable capabilities and contributions constitutes a Type II error of omission (blind/dismissive).  Overvaluing men who aren’t actually better performers is a Type I error of inclusion (credulous/gullible).  The deeply-biased “test” for hiring begins with a skeptical null hypothesis that discriminatorily burdens women with proactively proving our worth.  It’s like a warped judicial system wherein the accused must prove their own innocence. 

Again, fear is the culprit:  fear of working with someone different than oneself (gender, race, age, religion, etc), fear the comfortable status quo culture will change.  Ironically, fear of making a mistake leads directly to the very costly mistake of over-exclusion. 

Slowly, some progressive companies have begun to realize the high cost of Type II hiring errors relative to Type I hiring errors.  Failing to recruit from half the population means that a company is, by definition, reaching deeper down into the barrel of male talent – which ultimately costs the company in productivity, innovation and competitiveness.  Meanwhile, accidentally hiring or promoting the wrong person can be reversed, once observed performance clearly diverges from expectations.  From a rational economic perspective, companies have a much to gain and little to lose by adopting a much coarser hiring sieve to proportionally include women.

[See my related article “The Lost Generation”]

Voter eligibility

Null hypothesis:  Person isn’t eligible to vote
Burden of proof:  Voter must produce eligibility documents at polling location

Assessment FALSE Eligible voter casts ballot Rare cases of voter fraud (0.00001%)
TRUE Disenfranchise eligible voters (4.4%) Ineligible voters not allowed to vote

In the case of voter eligibility, our system is one that selects for inclusion by requiring would-be voters to prove eligibility at the polls (with specific identification requirements varying widely by state).  Therefore, the progressive preference is for a coarse sieve.  We ought to make it relatively easy for people to register, prove their eligibility at the polls, and exercise their constitutional right to vote.  Wrongfully excluding many eligible voters does far greater harm to society than a rare case of counting a fraudulent vote.

Conservatives’ fear-based worldview leads to willful misapprehension of the Type I error rate by multiple orders of magnitude.  In fact, out of 197 million votes cast in federal elections between 2002 and 2005, there were 26 confirmed cases of voter fraud (i.e., ineligible voters being allowed to vote).  Assuming that all fraud instances were caught, that equates to a Type I error rate of less than 1 in 7 million, or 0.00001%.  In other words, we’re currently screening voters at the polls with a 99.99999% confidence level.  (An ultra-high-availability computer system with that level of “seven nines” service guarantee would have just 3 seconds of downtime per year.)

Meanwhile, in states with strict photo identification laws, studies estimate that 11% of eligible voters lack qualifying identification.  If 40% of those voters turn out to vote (per average federal election turnout rates), that means as many as 4.4% of eligible would-be voters are disenfranchised.  A Type I error rate of 0.00001% and Type II error rate of 4.4% means that our system trades off over 300,000 disenfranchised voters for every 1 voided illegitimate ballot.  (Note:  Though strict photo ID laws have been proven in court to disenfranchise voters and suppress turnout, they are not necessarily the direct means by which we catch voter fraud.  The relationship between avoiding fraud and avoiding disenfranchisement is another — more complex — story.)

Economic logic tells us that the marginal cost of reducing an already-minuscule Type I error rate would be an inefficient use of taxpayer money.  Typically, conservatives would eagerly support such an argument.  However, in this case, political power agenda trumps economic principle.  Eligible voters lacking photo identification are disproportionately low-income and left-leaning.  So, the harm done to them helps conservative candidates.  To deflect charges of intentional voter suppression, conservatives focus on obfuscating data about what is, in truth, the vanishingly low base rate incidence of voter fraud.  They incite fear of something that is not, in fact, happening. 

Re-framing this debate can advance an otherwise-stalled dialogue.  We can pose questions that directly address the underlying disagreement:   How does the marginal cost to taxpayers of further reducing a low Type I error rate compare to the marginal cost of reducing a high Type II error rate?  How many disenfranchised voters is one avoided fraudulent vote “worth” to us as a society?  How can we better communicate voter fraud data, to combat the factual ignorance that underpins support for voter suppression laws?

Refugee vetting for asylum

Null hypothesis:  Asylum-seeker deserves refuge
Burden of proof:  Immigration department must identify security threats

Assessment FALSE Turn away refugees who pose a threat Deny help to many innocent people in dire need
TRUE Rare cases of granting visas to dangerous people (0.00009%) Provide refuge, liberty, opportunity to hard-working good people

Progressives further point out that the “false negative” rate of accidentally giving out visas to terrorists is comfortingly low:  Among 3.25 million refugees admitted into the United States 1975-2015, 3 caused a death.  Thus, our current asylum applicant test has a 0.00009% Type II error rate.  That’s a 99.99991% power level (in exchange for what is theoretically a low confidence level – which we can’t calculate because the number of rejected refugees who would have been domestic terrorists is unknowable).

(Note:  If you prefer to conceptualize our current system as selecting for inclusion, just swap all of the vocabulary. The conclusion remains the same if we invert terminology to describe the null hypothesis as “terrorist”, progressives’ concern as avoiding “Type II” errors of wrongful denial of asylum, and “false positives” of over-inclusion as historically low.)

In truth, because the base rate prevalence of terroristic leanings among human beings is very low, it is mathematical corollary that most rejected refugees are harmless.  When prevalence is very low, false positives (i.e., rejecting innocent refugees) are by definition more numerous than true positives (i.e., rejecting terrorists).  Misapprehension of this counter-intuitive truth is the same “base rate fallacy” that reared its xenophobic, bigoted head in the 2016 election cycle’s Skittles-refugee comparisons. 

[See my related article “Skittles vs Refugees: The humanitarian cost of inferential error”]

Conservatives’ attitude on refugee immigration is entirely explicable as a manifestation of a fear-based worldview.  Conservatives put more emphasis on the low risk of admitting one horrifically dangerous person, compared to the high risk of failing to help hundreds of thousands of innocent victims of war.  Fear makes them overstate the low risk of a Type II error, and fear makes them less motivated by the humanitarian failure of large-scale Type I errors. 

Psychology research shows that conservatives’ minds are more sensitive to threats of harm and oriented toward protective separation, whereas progressives’ minds are more sensitive to threats of loss and oriented toward community.  We can imagine how both worldviews would have been useful evolutionary adaptive traits, and how both remain valuable today.  But, because people don’t easily alter their worldview, the policy debate stalls as a clash of worldviews. 

Again, it is more fruitful to re-frame the debate as a less emotional one, based on consideration of real data and statistical trade-offs: 

  • How much higher do we think terrorism prevalence is among Syrian refugees today, compared to the known low rate of 0.00009% among all refugee immigrants over the past 40 years?
  • How could we change our immigrant vetting process to keep us safe (by minimizing false negatives), without violating our nation’s core principles (via high false positive rates)?

Market research

Null hypothesis:  No trends (signals) exist in the data (noise)
Burden of proof:  Researcher seeks to identify all market signals for a business decision-maker

Assessment FALSE Correctly report real market signals Misleading report of random noise as a market signal
TRUE Report nothing, even though there are trends in the market Correctly report an absence of market trends

When we move out of the realm of social policy and into the business world, relative values of Type I and Type II errors shift.  Typically, there are only dollars at stake and no direct humanitarian cost of drawing erroneous conclusions about the world.  Which grade sieve is appropriate is therefore highly situational.

The aim of market research is to identify actionable market trends and customer preferences – to find meaningful “signals” within the “noise” of voluminous data.  Business decision-makers use Bayesian methods to incrementally update prior beliefs based on new research results.  And, those market research results are but one of many information sources considered. 

Decision-makers want to consider numerous possible signals – not just the few that would pass through a super-fine sieve using a high confidence interval:

  • Spurious findings (Type I errors) aren’t harmful because they go through additional post hoc judgment filters and don’t independently drive action.
  • In contrast, incorrectly believing there’s nothing happening in the market (Type II errors) can be quite costly to a business. We are very concerned about missing real phenomena. 

Therefore, market research optimally uses a low confidence level to define statistical significance, allowing more potential signals to surface and be reported as significant findings.  

[See my related article “Intentional gullibility: Slash your statistical confidence level to 80%!”]

Product safety testing

Null hypothesis:  Product has no defect
Burden of proof:  Tester must show a product is defective, to justify pulling it out of the supply chain

Assessment FALSE Destroy defective products Waste money by dumping some non-defective products
TRUE Ship dangerous products to customers Ship safe products

In manufactured product safety testing, there is a humanitarian cost on only one side of the equation.  The trade-off between Type I and Type II errors is a trade-off between money and people

As in the market research example above, everyone is logically more worried about false negatives than false positives – politics doesn’t affect how people value the two error types.  But, in product safety testing, a Type II error is even worse than in market research:  it actually harms customers.  We draw the same conclusion as in market research regarding which error type is most costly, but we may go even further to lower confidence and increase power levels of our test to minimize that error.

As in the immigration example above, everyone is worried about false negatives because they harm people.  (Bad products can be dangerous; immigrants can be dangerous.)  However, in product safety testing, we are trading off only money for people’s safety – whereas the refugee question forces us to trade off some people’s safety for other people’s safety. 

Wasting money on dumping some perfectly good product involves no ethical cost.  On the other hand, harming our customers does.  Just as “refugees aren’t Skittles” (per the famous tweet by Skittles manufacturer Mars, Inc.), so too people aren’t products.  When faced with a money-vs-people tradeoff, politics don’t apply.  Everyone agrees we must focus primarily on minimizing Type II errors and keep bad products off the shelf. 

Depending on the specific harm a defect would cause (inconvenience, discomfort, illness), the monetary value of identifying defects changes.  Additionally, a manufacturer’s self-interest also lies in spending money to minimize Type II errors, due to the reputational ripple effects of shipping bad products.  In this case, both public safety and self-interest are in agreement as to the Type I-Type II error tradeoff.

Scientific research

Null hypothesis:  No effect/link exists
Burden of proof:  Researcher aims to show that there is a link/effect

Assessment FALSE Publish important findings Publish non-replicable results, damage reputation (5%)
TRUE Fail to identify potentially important effect (~30-60%) Uninteresting outcome – nothing to publish

Physical science researchers are extremely concerned about the embarrassment of reporting false positive results.  False positives misdirect further experiments in the wrong direction.  They damage the researcher’s personal reputation and public credibility of the scientific process. 

False negatives, in contrast, aren’t as harmful.  Other research teams will eventually identify and publish the real effect that any one particular experiment fails to uncover.  For individual scientists, lack of publishable results is indeed a disappointing, missed opportunity, but not punitive. 

In contrast to the market research example above, scientific research has good reason to prioritize avoidance of Type I errors over avoidance of Type II errors.  Therefore, a higher confidence level (e.g., 95%) is warranted.  Meta-studies of published scientific papers have shown that the high confidence levels and experimental design (effect size, sample size) commonly yield Type II error rates (1 – power) exceeding 50%.  Science is quite willing to be blind in order to avoid gullibility.

Consider the consequences

Improving the quality and productiveness of policy debates starts with gathering data: 

  1. What is the base rate incidence/prevalence?
  2. What are the Type I and Type II error rates in the current system?  
  3. What are the financial, ethical and social costs of each type of error?

From that factual basis, conservatives and progressives wielding opposing value systems can more rationally clarify their position regarding inescapable trade-offs:  How much are we willing to pay to reduce either error rate towards zero?  How many of mistakes of over-inclusion are acceptable to avoid one instance of over-exclusion?  

Ultimately, to satisfy everyone, a system must include policy responses to the consequences of each inevitable type of errorGiven that we will always have some false criminal convictions, how can we provide adequate remedies?   If we settle on a narrower welfare system, how can we provide a secondary safety net for those in dire need who nonetheless inevitably fall between the cracks?   If we continue with extreme vetting and rejecting refugees, can we appropriate money to create a robust appeal system, or to subsidize their resettlement in more receptive countries? 

The failure mode that many conservatives bring to policy debates is a refusal to consider the consequences.  Even within conservative politics, discussion quality is greatly improved by framing issues as Type I-Type II error trade-offs.  For example:

  • My right-leaning libertarian friend in the wine bar was initially only concerned with keeping the “undeserving” people “out” and government expenditure low – implicitly (and irrationally) at any cost. He hadn’t considered the downstream fate of people wrongly excluded in his idealized-but-inevitably-imperfect system.  Even though the humanitarian argument doesn’t move someone like him, he does care about the long-term net cost to society of the wrongful exclusion (once it’s brought to his attention quantitatively).
  • In contrast, a center-leaning libertarian friend adopts a more holistic perspective from the outset: She too may fervently prefer limited entitlement spending.  But, before simplistically advocating budget cuts, she considers the real financial, social and ethical costs of withdrawing assistance from poor people.  She realizes that reduced spending must be packaged with a concrete plan to mitigate the consequences of errors of over-inclusion and over-exclusion. 



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Trump is not a “businessman”

<5 min read

Donald Trump is not a businessman.  He’s a real estate developer.  Huuuge difference.

If we had, say, a software company CEO poised to set a new direction for our nation’s diplomacy, trade policy, military actions, law enforcement and healthcare system, we’d rightly be less worried.  Businesses that successfully solve problems (in the form of products and services) for people (individuals or business customers) prioritize sensitivity to human values, needs, and behaviors.  Customer experience (CX) and user experience (UX) are paramount.

Real estate developers have little customer orientation.  They aren’t making products or providing a service to anyone in particular.  They create value primarily by building something quickly, cashing out and moving on to the next project — not by innovating solutions, delighting customers, or understanding real-world consumer psychology. (Financial investing, commodity trading and Romney-style leveraged buyout businesses are also in this camp.)  A developer like Trump must care about macroeconomic trends that affect lending rates and property values — not so much the microeconomics of individual consumer/citizen experiences and decisions.  Trump, Inc. doesn’t have a CXO. 

The real estate development process rewards bullies: intimidating competitors out of the market to prevent overbuilding, strong-arming contractors to reduce construction costs, rationalizing corner-cutting, and berating permitting authorities.  The industry is notorious for graft and heavy-handed lobbying.  Arm-twisting politicians to grease the permitting wheels is not uncommon.  

Real estate is a rare industry where the least possible oversight is unambiguously best for the capital owner.  Delays cost money.  In fact, the biggest swing in a building project’s value at completion is attributable to time.  So, developers axiomatically rail against regulatory requirements to assess environmental impact, offset habitat destruction, consider visual impact, involve community voices, or abide by aesthetic covenants. Whereas raw libertarianism fails in most industries (and lacks adherents among contemporary business leaders and has long been debunked by academic economists), it finds an eager case study in real estate development.  

Most business sectors benefit from some regulation that ensures fair competition, protects customers, and minimizes volatility.  Even the oil and gas industry doesn’t want Trump to lift regulations on fracking, as they are fundamental to the good community relations that make operations smooth.  (Lifting regulations on fracking is in any case almost immaterial to o&g decision-making. Global commodity prices dictate whether its rational to recover a resource.)  Many industries have invested billions over time in accommodating smart regulations about air pollution, efficiency standards, toxin disposal, product labeling and safety practices.  Unwinding rules doesn’t deliver cost savings, because companies are optimized to meet existing requirements.  Imagine the absurdity of a manufacturer deciding to spend money to dismantle a production line, in order to then spend more money to rebuild it to new, lower standards.

The wild west of real estate development breeds a lose-lose, black-and-white mentality in those who make it their life.  It takes little creativity and (for those, like Trump, without resource constraints) little collaboration to provide the predictably ever-urbanizing world with yet another, bigger building.  It is unsurprising to those with exposure to the high-stakes commercial real estate sector that Trump’s oft-criticized “temperament” is one of simplistic, short-attention-span tribalism.  He’s a product a 50-year-long, monothematic career inside this one peculiar corner of “the business world”. 

Contrast the zero-sum real estate game with the complexity of technology product and service market dynamics.  Leading tech companies think creatively about creating new markets (not just wielding influence, to borrow more money, to make more of the same stuff, to match GDP and population growth).  Innovation, not access to capital, is the key to success for such companies.  They task sophisticated strategy brains with finding mutual advantage with competitors, and allocate substantial bandwidth to developing collaborative alliances and channel partnerships.  


Back to the thought experiment of a software company CEO as president-elect:  She has likely had to have the humility and foresight to radically pivot the business strategy at some juncture.  And, she’s gained functional expertise in marketing, sales, finance, operations, data analytics and IT over a varied career path.  Success in that realm requires patience (not asset-flipping), thoughtfulness (not impulsiveness), creativity (not force), collaboration (not tone-deaf demagoguery), and flexibility (not intransigence).  

While the idea of a “businessman” politician evidently seems appealing to masses of uncritical voters, that is a meaninglessly broad descriptor (encompassing the 70% of American workers who don’t work in government, military, academia or healthcare delivery).  Not all segments of “business” engender the perspective, experience, habits, and temperament necessary to translate running a business into running a country.  Real estate development should have been the least appropriate candidate.  


Posted in <5 min read, Business topics, Social issues, [All posts]