Poetry, Programming and People Management

The human brain does ambiguity well. Most of us are strangely drawn to multiple meanings, surrealities and pattern recognition. We thrive on metaphors and similes, rejoice in symbols, dance to nonsense syllables and ad hoc syncopations. And paradoxes? We both hate and love them — paradoxically, of course.

This may be one of the reasons so many people become frustrated and even fearful when confronted by math and logic. Those disciplines feel so cold and hard-edged with their unitary meanings and wearisome concatenations of implacable reasoning.

It’s the same with computer coding. If you take an Introduction to Computer Science course, the professors often go out of their way to compare natural languages (a phrase which itself is an oxymoron) with computer languages.

Yao graph with number of ray k=8; from Wikimedia, by Rocchini

The gist is that while while both types of language share common and, indeed, essential properties such as syntax and semantics, they differ widely in that natural language can often be understood even when the speaker or writer fails to follow basic spelling or grammatical rules. In contrast, a computer program (much like a mathematical equation) will typically fail to work if even a single character is left out or misplaced. An absent bracket can be a fatal bug, a backwards greater-than symbol can cause an infinite loop, a poorly assigned variable can inadvertently turn  100 dollars into a dime.

A computer has no use for the artful ambiguities and multiple meanings of poetry. If you give the machine a couple of lines of verse such asanyone lived in a pretty how town (with up so floating many bells down)”,  it will — unless you carefully guide the words into the code as a string —  give you an error message.  (I know a lot of people who might respond the same way, of course.)  Yet, without the precisely imprecise wordplay of e e cummings, those lines of poetry would not be poetry at all.

So, what does any of this have to do with people management?

Just this: people management is sometimes poetry, sometimes programming, and it helps to know which is which. Before the rise of civilizations and cities, when virtually all people were hunting and gathering in smallish bands and clans, people management (in the forms it would have existed then) was all poetry.

Walden Pond; from Wikimedia, by QuarterCircleS

Sure, there were unwritten rules, harsh taboos, constant rumors and deadly serious superstitions. And a leader, to the degree there were leaders as we understand them today, could leverage those cultural components to influence his or her clansmen. But this was mostly a matter of nuance, persuasion, the formation of alliances, the wielding of knowledge and lore (when, that is, it wasn’t a matter of force and coercion). In the largest sense, it was art and song.

Today, good managers must still be attuned to the poetry of human attitudes and actions, able to sort through the ambiguities of rumor mills and hurt feelings and arrogant posturings. But now managers must also cope with or even rely on laws, regulations and rules.

Is there a “zero tolerance” clause in the company policy somewhere? Then even a terrific employee who gets caught using illegal drugs may need to go.  Are there complex legal regulations barring a worker from having financial holdings in a certain client company? Well, then, the employee must divest or hit the door. There are countless other examples of rules that are as hard-and-fast as rule-of-law societies can make them. Although these human rules will never be quite as rigorous as the requirements of programming languages, they are a kind of human programming; there are true and false statements,  barriers that can’t be broken, classifications that should never be breached.

This is why we have legal departments. It is also why uncertain managers call in the hired gun of the HR professional to take care of dismissals and drug tests and background checks.

We simultaneously hate  this programming of human behavior and depend on it. We can, for example, rely on the kind of code that states:

while worker performance >= level 3: { {

provide paycheck and health insurance }

else if: {

performance <= level 2:

leverage performance review proceedings

 }}

Okay, the coding in companies is much more complex than that. Still, the point is that we rely on it because it’s clean, logical and, best of all, spares us from having to make hard and potentially dangerous decisions on our own. In such settings, we are no longer “poets of people management,” the kind of managers who might have led a clan though a vast and dangerous prehistoric wilderness in millennia gone by.

This dependence on programming is a shame in many ways, one that harried managers should ponder from time to time.  I know we can’t utterly avoid modern programming — at least, not unless we retreat into the wildness, as metaphorically  isolated as Thoreau in his cabin by Walden Pond. Nor should we. The rule of law is essential to our modern societies, and formal policies are often forged to protect employees from arbitrary or biased decisions. Still, we might strive to be better poets, respecting employees as the people they are rather than viewing them as components of a well-programmed machine.

Featured image: The Parnassus (1511) by Raphael: famous poets recite alongside the nine Muses atop Mount Parnassus.

Do You Treat Employees Like Fixed-Program Computers?

Computers didn’t always work they do today. The first ones were what we now called “fixed-program computers,” which means that, without some serious  and complex adjustments, they could do only one type of computation.

Sometimes that type of computer was superbly useful, such as when breaking Nazi codes during World War II (see the bombe below). Still, they weren’t much more programmable than a calculator, which is a kind of modern-day fixed program computer.

The brilliant mathematician John von Neumann and colleagues had a different vision of what a computer should be. To be specific, they had Alan Turing’s vision of a “universal computing machine,” a theoretical machine that the genius Turing dreamt up in 1936. Without going into specifics, let’s just say that the von Neumann model used an architecture has been very influential up the present day.

One of the biggest advantages associated with Turing/von Neumann computers is that multiple programs can be stored in them, allowing them to do many different things depending on which  programs are running.

Von Neumann architecture: Wikimedia

Today’s employers clearly see the advantage of stored-program computers. Yet I’d argue that many treat their employees and applicants more like the fixed-program computers of yesteryear.  That is, firms make a lot of hiring decisions based more on what people know when they walk in the door than based on their ability to acquire new learning.  These days, experts are well paid largely because of the “fixed” knowledge and capabilities they have. Most bright people just out of college, however, don’t have the same fixed knowledge and so are viewed as less valuable assets.

Employers aren’t entirely in the wrong here. It’s a lot easier to load a new software package into a modern computer than it is to train an employee who lacks proper skill sets.  It takes money and time for workers to develop expertise, resources that employers don’t want to “waste” in training.

But there’s also an irony here: human beings are the fastest learning animals (or machines, for that matter) in the history of, well, the universe, as far as we know. People are born to learn (we aren’t designated as sapiens sapiens for nothing), and we tend to pick things up quickly.

What’s more, there’s a half-life to existing knowledge and techniques in most professions. An experienced doctor may misdiagnose a patient simply because his or her knowledge about certain symptoms or treatments are out-of date. The same concept applies to all kinds of employees but especially to professionals such as engineers, scientists, lawyers, and doctors. In other words, it applies to a lot of the people who earn the largest salaries in the corporate world.

Samuel Arbesman, author of The Half-Life of Facts: Why Everything We Know Has an Expiration Date, stated in a TEDx video, “Overall, we know how knowledge grows, and just as we know how knowledge grows, so too do we know how knowledge becomes overturned. ” Yet, in our recruitment and training policies, firms often act as if we don’t know this.

The only antidote to the shortening half-life of skills is more learning, whether it’s formal, informal or (preferably) both. And the only antidote to a lack of experience is giving people experience, or at least a good facsimile of experience, as in simulation-based learning.

The problem of treating people like fixed-program computers is part of a larger skills-shortage mythology. In his book  Why Good People Can’t Get Jobs , Prof. Peter Cappelli pointed to three driving factors behind the skills myth. A Washington Post article sums up:

Cappelli points to many’s unwillingness to pay market wages, their dependence on tightly calibrated software programs that screen out qualified candidates, and their ignorance about the lost opportunities when jobs remain unfilled…”Organizations typically have very good data on the costs of their operations—they can tell you to the penny how much each employee costs them,” Cappelli writes, “but most have little if any idea of the [economic or financial] value each employee contributes to the organization.” If more employers could see the opportunity cost of not having, say, a qualified engineer in place on an oil rig, or a mobile-device programmer ready to implement a new business idea, they’d be more likely to fill that open job with a less-than-perfect candidate and offer them on-the-job training.

The fixed-program mentality should increasingly become a relic of the past. Today, we know more than ever about how to provide good training to people, and we have a growing range of new technologies and paradigms, such as game-based learning, extended enterprise elearning systems, mobile learning and “massively open online courses” (aka, MOOCs).

A squad of soldiers learn communication and decision-making skills during virtual missions: Wikimedia

With such technologies, it’s become possible for employers to train future applicants even before they apply for a position. For example, a company that needs more employees trained in a specific set of programming languages could work with a provider to build online courses that teach those languages. Or they could potentially provide such training themselves via extended enterprise learning management systems.

The point is that there are more learning options today ever before. We live in a new age during which smart corporations will able to adopt a learning paradigm that is closer to that of stored-program computers, one that they’ve trusted their technologies to for over half a century.

Featured image: A rebuild of a British Bombe located at Bletchley Park museum. Transferred from en.wikipedia to Commons by Maksim. Wikimedia Commons.