When I first graduated with a PhD in English Literature, I was plagued by the sense that no one in the non-academic job market cared about my achievement. After dozens of unsuccessful job applications, it became apparent that my education would not be enough without workplace experience to back it up. Like many, I assumed that “experience” was considered crucial because it indicated that workers had acquired job-specific skills and a professional knowledge base that made them immediately valuable to a prospective employer. Yet after working in the non-academic world for several years, I have realized that the “experience” that employers are looking for has less to do with a person’s skills and knowledge base than it does with the mode of learning that emerges through workplace experience.
On a general level, I believe that learning follows a trial-and-error model in the workplace much more than it does in the exam room. On any given workday, an individual can make a number of small or large mistakes, and good managers will respond by ensuring that the individual understands those mistakes and learns how to avoid them in the future. In contrast, the scholastic mode of learning (particularly for a high-functioning student) follows a perfectionist model. Parents of “A” students are not likely to say, “Just see how you do on the next test and learn from your mistakes.” Rather, they are much more likely to tell their children not to make any mistakes in the first place, especially when having a competitive GPA and applying for major scholarships doesn’t allow students the luxury of trial and error learning. This perfectionist emphasis, I argue, constitutes a fundamental point of difference between traditional scholastic learning and workplace learning. Having an impressive transcript might demonstrate your intellectual aptitude, but having work experience shows an employer that you’ve had the opportunity to learn from workplace mistakes and that (more importantly) you’ve already made those mistakes someplace else.
To illustrate the difference between a perfectionist and a trial-and-error-based “muddler,” I am drawn to Jonah Lehrer’s book, How We Decide. This book was actually pulled from stores when Lehrer was accused of fabricating quotes in a previous text, yet I believe it still offers valuable insight in one of its passages on computer intelligence. In this passage, Lehrer recounts the story of Deep Blue, the set of IBM mainframes that defeated chess champion Gary Kasparov in 1997. Deep Blue was capable of processing more than 200 million possible chess moves per second, while Kasparov (a world champion) could only process five. Of course, Deep Blue carried the day and defeated Kasparov 3.5 games to 2.5 (with a .5 point reflecting a draw). Yet this match was a rematch of a contest that Kasparov had won 4-2 in the previous year. Computer enthusiasts around the world lauded Deep Blue’s eventual victory, but many people in the world of computer programming were forced to ask, “Why did a machine with 40 million times the processing power of its human opponent win by so narrow a margin?” The answer lies in the limits of a perfectionist mode of learning.
Deep Blue was designed to make the “perfect” chess move every time it played. But in doing so, the computer had to completely recreate the chessboard and process millions of moves each time its turn began. It could not learn from experience and it required an enormous amount of energy to run its calculations over and over. The computer used so much power, in fact, that it required “specialized heat-dissipating equipment so that it didn’t burst into flames.” Kasparov’s brain, on other hand, could draw upon decades of experience to limit its attention to a small series of possible next moves. He had honed his craft through a long process of study and muddling, while Deep Blue was the ultimate perfectionist.
Surprised with the limitations of Deep Blue, a computer programmer at IBM named Gerald Tesauro designed a new program to become the ultimate Backgammon player. However, he encoded one crucial feature into its software. Unlike Deep Blue, which was designed to make a perfect move on every turn, this new software was designed never to make the same mistake twice. The program could not even beat an elementary Backgammon player when it was first tested. But after it was put through millions of simulated games, the program became far superior to Deep Blue while using a minute fraction of the processing power.
Talk to any number of high-achieving students today and you’re bound to meet a few anxiety-stricken Deep Blues whose brains are ready to burst into figurative flames. The mental health consequences of perfectionism have been well documented and they (like too many social problems) disproportionately affect young women. Yet the intense pressure never to make mistakes encourages this mode of learning throughout most formal schooling processes. It’s only relatively recently that educators have begun to embrace the trial-and-error aspects of Active Learning on a broad, formalized scale, and this is a development that I celebrate.
Now that I’ve talked about the virtues of muddling, I need to talk about its vices. Just as there are drawbacks to an overemphasis on proactive learning, there are potential disasters awaiting us if we start telling everyone, “Don’t worry, you’ll learn it after you mess it up a few times.” Some mistakes are difficult if not impossible to rectify, as we find every day with the ongoing destruction of our environment or any number of other issues we fail to address in a sufficiently proactive way. One needs look no further than the great tragedies of western literature to find countless examples of people who did not learn their lessons until it was too late. In fact, I strongly suspect that the enormous emotional power of tragedy comes from our fear that we ourselves might be doomed to learn our most valuable lessons only after our mistakes have become irredeemable.
I believe that on some general level, perfectionists thrive within formal education because their tendencies are well suited to a test-based system of merit that punishes more reactive approaches to learning. This dynamic helps explain the thesis behind a book like Why A Students Work for C students and Why B students work for the Government, by Robert Kiyosaki. In this book, Kiyosaki claims that “A” students thrive in school but less so in the “real world” because they are adept at working within a meritocratic system with clear rules and significant benefits for perfectionists. “C” students, however, are more likely to be self-directed muddlers who thrive much more once they enter the less formally meritocratic world outside of school. While I do not wish to argue for or against the veracity of this thesis, I mention it here to highlight how at least one bestselling author has connected this perfectionist/muddler distinction to the biases found in traditional education.
My purpose in writing this piece is not to turn a generation of perfectionist students into one of muddlers. Rather, I hope that this piece can get those perfectionists to reflect on the ways that formal schooling and rewards systems have pushed them to adopt a mode of learning that can create significant mental health problems and poor self-image in the long run. By better understanding these forces and by seeing the value in trial-and-error learning, I hope these students can find a renewed sense of self worth and hope for the future as they enter the world beyond that of formal schooling.