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H. L. Mencken’s Law, and the Inverse Relationship Between Knowledge and Happiness

December 10, 2009

While walking through the halls of Arthur K. Watson Hall in the Department of Computer Science at my alma mater (a dismal school with Gothic architecture located somewhere in New Haven, the mood of academic tortu*, ahem, of academic pursuit, rather, of which in 1989 to 1994 (including my LOA) was characterized by a local art poster which once depicted a screaming man getting run over a train while muttering “This too shall pass.”), one day in circa 1992 to 1994, I once encountered a sheet of paper pasted to a door of one of the professors of computer science with the following inscription:

Those who can, do.
Those who can’t, teach.
Those who can’t teach, do research.

Later, I came to learn that the first two lines have been attributed to H. L. Mencken (American journalist, essayist, magazine editor, satirist, critic of American life and culture, and student of American English (September 12, 1880 – January 29, 1956)); however, it is not clear who came up with the last line.

In a brief mental journey to that very location in the past, just this evening, I happened to have a dream in which I was back at Old Campus at my college, but this time, as a guide who was showing a visitor around campus while jokingly mimicking the other students. Upon waking up, I somehow came up with my own version of an extension to Mencken’s Law, which I have termed “Russell’s Extension to Mencken’s Law” below:

Those who can, do.
Those who can’t, teach.
Those who can’t teach, write.

— myself

Later, I learned that the extension that I had come up with was very similar to a different extension that someone else had also devised:

Those who can, do.
Those who can’t, teach.
Those who can’t teach, criticize.

— Solomon Short

After some research, I discovered that Solomon Short is actually a fictional alter ego of David Gerrold (American science fiction author (24 January 1944-)), author of The Galactic Whirlpool [1].

(Just to be fair, Nicolas Martin had also previously, without my knowledge, came up with what is sometimes known as Martin’s Extension to H. L. Mencken’s Law:

Those who can, do.
Those who can’t, teach.
Those who can’t teach, teach education.

— Nicolas Martin


These quotes bring to mind another saying that I had also see posted on another door in the same department on another occasion. Although I can’t remember the exact wording (and am unable to find the reference now), the gist was somewhat like the following:

Before going to college, we think that we know everything.
When in college, we learn that there are some things that we don’t know.
When in graduate school, we learn that there are quite a few things that we don’t know.
When in a Ph.D. program, we learn how truly enormous and insurmountable are the things that we really don’t know.

(If anybody knows the source or exact wording of the above-mentioned quote, I would greatly appreciate an explanatory comment with the corresponding information below.)

Incidentally, there can be an inversely proportional relation between knowledge and happiness. In my case, before going to college, I felt much happier than just after graduating, because I did not know how much I did not know. In particular, I felt that I could easily become an astronomer or astrophysicist or any other professional with enough study. Before going to college, I was greatly inspired by the book Cosmos [2] by Carl Sagan (American astronomer, astrochemist, and author (November 9, 1934 – December 20, 1996)), which described a variety of scientific topics related to astronomy and astrophysics. Back then, I used to dream of becoming an astronomer or astrophysicist.

Unfortunately, I had one major problem: Because of financial circumstances, I was unable to attend high school in Tokyo before matriculating at college (I was almost entirely self-taught), and was therefore unable to participate in physics-related lab experiments, which required access to a laboratory. Furthermore, I did not have access to sophisticated books or tutors for physics, and my home environment was very noisy and distracting. Worst, my concentration was repeatedly interrupted every thirty minutes or so by an over-protective parent at home who insisted on bringing pink grapefruit to my desk, and who demanded to know what I was thinking about every time that I tried to contemplate anything deeply. Essentially, I matriculated at college without having had much exposure to physics.

Then I discovered that the introductory courses in physics all assumed more knowledge of physics than I had, so I wound up never taking any courses in physics for lack of time to catch up. Instead, I wound up pursuing computer science, since I had had some exposure to programming. Although I eventually graduated with a Bachelor of Science in computer science, my lack of significant aptitude in mathematics (I initially had to overcome mathematics phobia as well) caused me to lose much sleep in trying to catch up, and I eventually chose not to pursue computer science in graduate school because of fear of over-exhaustion due to even more lack of sleep; I had not mastered linear algebra, in particular, in part because my visiting professor had only spent two weeks on the subject in my discrete mathematics class, and I was very concerned about whether I could catch up with such a serious deficiency, since I was keenly aware of how fundamental and essential this topic would be in any graduate program in computer science.

Paradoxically, however, my great difficulty in overcoming the mathematical prerequisites for computer science did confer one hidden advantage: The process made me much better at explaining mathematical concepts to beginners and to students who are relatively weak at mathematics. After graduation, I once tutored my roommate in mathematics, and he commented then that my explanations were significantly easier to understand than those of his regular mathematics tutor. Later, one of my employers in Manhattan told me that he was much better at teaching a subject than others who knew more about the subject than he did, because he was able to see the subject from the student’s perspective. Paradoxically, my explanations for mathematics have therefore generally been evaluated much more favorably than those for English, which is my forte, because when teaching mathematics, I can understand why the student finds a concept difficult, whereas in English, most concepts seem so trivially elementary that I simply cannot understand how any student could possibly not understand them. Therefore, I have the curious advantage of being much better at explaining those mathematical concepts that I do understand than mathematicians who have never had to struggle in understanding them.

Back to physics, however. Curiously, however, before matriculating at college, I had always found the few books on physics that I had studied to be much more interesting and approachable than equivalent-level books on high school mathematics (in particular, although I enjoyed set theory and, later on, graph theory, I had always had a strong antipathy toward algebra and, in general, toward any areas which did not allow visualization of the concepts). I enjoyed visualizing structures mentally, but had difficulty in doing so in algebra; however, I found visualization much easier in physics. Even in college, when I occasionally encountered physics problems that a tutor nearby was explaining to other students, I was surprised that I did not feel that the problem seemed difficult, since I could readily visualize it. However, I was so overloaded in coping with computer science that I had no spare time in which to explore physics at the time.

In college, I discovered that my visual approach to learning put me at a distinct disadvantage in learning topics that could not readily be approached visually, such as algebra. This discovery made me deeply unhappy. My writing professor told me that I had a flair for writing and actually walked me to the library microfiche collection in recommending that I apply for a graduate program in English, whereas Drew McDermott, my computer science professor for Computer Science 201a: Introduction to Computer Science, had told me that he thought that I was “not cut out for computer science”; nevertheless, I was determined to prove McDermott wrong, and persevered in computer science, eventually completing the major after going through much difficulty in overcoming Computer Science 365a: Design and Analysis of Algorithms. (Curiously, I found the following course, Computer Science 465b: Advanced Algorithms, to be much more manageable than the introductory course, even though the material was more sophisticated, because there was much less material to cover.)

It was not until many years later, a few days ago, while watching a series of educational programs on NHK public television in Japan, that I realized that I actually found the topics in science to be distinctly easier to understand than topics of an equivalent level in algebra; until then, I had thought that the difference would be insignificant. This was when I realized that I might have been better off in a natural science; in particular, physics.

In order to undo my depression from the college experience, I had to restore my mental continuation (to borrow a concept from the Scheme programming language) to just before entering college, and to set aside mentally the entire college experience and accompanying negative emotional weight as a separate continuation, pretending that a different mental process had proceeded with that mental computation. This required visiting some memorable places before college and recovering some emotional memories by walking along some of the same paths and drinking some of the same soft drinks as back then. This brought back the mathematics phobia that I had previously overcome, but at least I was no longer depressed: I had, in a sense, gone back in time mentally and restored my old self, so to speak.

If more knowledge can lead to less happiness, then, by the inverse rule, less knowledge may also lead to more happiness (or at least to a recovery of happiness preceding the greater knowledge).

A corollary is that great souls often are burdened with great unhappiness. Albert Einstein (US (German-born) physicist (14 March 1879–18 April 1955)) once said the following [3]:

If I had only known, I would have been a locksmith.

Wolfgang Amadeus Mozart (composer born in the Archbishopric of Salzburg ((27 January 1756 – 5 December 1791)) died at only thirty-five years of age. Thomas Edison (American inventor, scientist, and businessman (February 11, 1847 – October 18, 1931)) suffered hearing impairment when his chemical laboratory caught fire and the train conductor smacked him on the ears and threw him off the train. Ludwig van Beethoven (German composer and pianist (17 December 1770[1] – 26 March 1827)) reportedly suffered a severe form of tinnitus which caused him great difficulty in hearing his own music; he was also tragically unsuccessful in romance.

I may not be a “great soul” (yet!), but recently, I have come to believe that until I become one, I at least have the benefit of being less unhappy (at least for now).

Recently, I have become more interested in resurrecting my old study of physics, and to see how far I might be able to pursue this subject formally. In particular, I am tentatively entertaining the (admittedly perhaps wild) idea of re-doing my entire undergraduate degree in physics instead of computer science, just to see how far I could go. I have a curious feeling that, because of my visual way of thinking and my greater aptitude for physics than mathematics, I might actually be able to attend graduate school faster by redoing my entire undergraduate education in physics than by trying continue directly on to graduate school in computer science. As a first step, I am considering reviewing some of the Feynman Lectures on Physics [4] at the next available opportunity.

[1] Gerold, David. The Galactic Whirlpool. New York, NY: Random House Publishing Group, 1997. <>.

[2] Sagan, Carl. Cosmos. New York, NY: Random House, Inc., 1980. <>.

[3] Moncur, Michael. “Michael Moncur’s (Cynical) Quotations.” and Michael Moncur. 10 December 2009. 10 December 2009. <>.

[4] Feynman, Richard P., Robert B. Leighton and Matthew Sands. The Feynman Lectures on Physics. London, U.K.: Addison Wesley Longman, 1970. <>.


From → Psychology, Quotes

  1. Michael Vanier permalink

    Woody Allen had a funny version of this quote: “Those who can, do. Those who can’t, teach. Those who can’t teach, teach gym.”

    • Interesting quote. However, since teaching gym can be considered a type of teaching, it seems somewhat self-contradictory: an oxymoron. Consider the following sentences:

      Those who can’t play, play tic-tac-toe.
      Those who can’t eat, eat quiche.
      Those who can’t program, program in BASIC.

      The above-mentioned sentences are also oxymoronic. (No offense against BASIC, incidentally; my own first language was N-80 BASIC, a version of line BASIC which ran on the NEC PC-8001 mkII, back in circa 1983, so I’m actually making fun of myself in that last sentence. I’d still rather program in BASIC than in C++; maybe I can’t program? ;-) )

  2. Dave permalink

    Interesting peek into the background of a classic saying, but the rest of it pretty much made me want to set myself on fire or dive off a high cliff. Wow. Can you imagine sitting next to this guy on a long flight?

    • Interesting peek into the background of a classic saying, but the rest of it pretty much made me want to set myself on fire or dive off a high cliff. Wow. Can you imagine sitting next to this guy on a long flight?

      Ha ha ha. I had a similar reaction from a fellow student once in college, actually, when I explained the traumatic experience awaiting an under-prepared student about to take a required course in algorithms, “CS 365a: Design and Analysis of Algorithms.”

      That course had a mid-term exam containing questions that had nothing to do with the apparently relevant chapter from the textbook, mathematical material that basically assumed graduate-level sophistication in writing proofs, series of problem set questions where each successive question could not be solved unless the previous problem had already been solved, a mad TA who insisted on creating problem sets wherein the average student could only hope to solve 50% of the questions correctly given the material without exceptional ingenuity (that TA himself told me that the problem sets were “curved” so that the average student earned approximately a 50% grade), and a mad professor who thought that his job was to separate the truly gifted from the average in his class, rather than to encourage under-prepared students to conquer challenging material (these “features” of the course applied when I had audited the course under then-Assistant Professor Richard Beigel in circa 1991).

      Immediately after I had described my experiences with the “Design and Analysis of Algorithms” course to that student, a mutual friend asked me something substantially equivalent to the following in private: “Did you see what kind of reaction you caused in that person? You just made him twist and turn in agony!” I replied, “Really!? I was just telling him what kind of course ‘Design and Analysis of Algorithms’ was like.”

      Fortunately, after returning to where I had lived before going to college, I was able to make a series of mental time leaps to remember myself before college. These experiences gradually undid the depression of college, restoring me from my college-age pessimistic personality to my pre-college optimistic one (when I was more ignorant and therefore happier). Unfortunately, this came at a price: the return of mathematics phobia.

      (This phenomenon makes sense if one realizes what a mental “time leap” essentially does: It restores the current mental/emotional state to that of the restoration point in the past. Essentially, I am summoning my past emotions and the thoughts that accompanied them. Since I used to have mathematics phobia before entering college and had only conquered it at the price of becoming depressed over my lack of aptitude for the new field-of-interest of mathematics, by making a time leap to just before college, I lost the interest in mathematics and hence no longer felt depressed about my lack of aptitude for the subject (since it no longer felt important or even relevant), but at the price of restoring the mathematics phobia as well.)

      Which is better: A less ignorant but traumatized and pessimistic self, or a less knowledgeable but blissful and optimistic self? The more one learns, the more one learns how little one has learned. Sometimes less is truly more.

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