2018-02-23 18:58:03 +00:00
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---
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created_at: '2017-05-30T08:06:21.000Z'
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title: On anthropomorphism in science (1985)
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url: https://www.cs.utexas.edu/users/EWD/transcriptions/EWD09xx/EWD936.html
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author: dopkew
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points: 70
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story_text:
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comment_text:
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num_comments: 62
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story_id:
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story_title:
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story_url:
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parent_id:
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created_at_i: 1496131581
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_tags:
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- story
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- author_dopkew
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- story_14443638
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objectID: '14443638'
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2018-06-08 12:05:27 +00:00
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year: 1985
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2018-02-23 18:58:03 +00:00
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---
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2018-03-03 09:35:28 +00:00
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On anthropomorphism in science
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2018-02-23 18:19:40 +00:00
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2018-03-03 09:35:28 +00:00
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(Delivered at The Philosophers’ Lunch, 25 September 1985)
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2018-02-23 18:19:40 +00:00
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2018-03-03 09:35:28 +00:00
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I must apologize for not speaking to you, but reading to you. I chose to
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do so because, not yet feeling quite at home, I am a bit nervous. Of
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course I can argue to myself that I don’t need to, but that does not
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always work.
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2018-02-23 18:19:40 +00:00
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2018-03-03 09:35:28 +00:00
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I can argue to myself that I grew up in a country whose population is
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only slightly larger than that of Texas, so why should I feel not at
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home? I spent most of my life at two universities, one four centuries
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old, the other a quarter, and if I take the geometric mean of those two
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ages I arrive precisely at that of UT, so why shouldn’t I feel at home
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here?
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Well, actually it is not too bad. I think I am much happier here than I
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would have been, say, at XXX–XXX where it is possible to lose sight of
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what it means to be an intellectual. The reason that I am a bit nervous
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is that I am not quite sure what philosophers do and, hence, somewhat
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uncertain about my role here.
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OK, so much for an irrelevant introduction; it was given to give you the
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opportunity to adapt your ear to my English.
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\* \* \*
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I chose “anthropomorphism” because —besides being a nice broad topic— it
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is so pervasive that many of my colleagues don’t realize how pernicious
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it is.
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Let me first relate my experience that drove home how pervasive
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anthropomorphism is. It took place at one of the monthly meetings of the
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science section of the Royal Netherlands Academy of Arts and Sciences,
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where we were shown a motion picture made through a microscope. Thanks
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to phase contrast microscopy —the invention for which Zernike got the
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Nobel Prize— it is now possible to see through the microscope undyed
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cultures of living cells, and that was what they had done while making
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this motion picture. It showed us —somewhat accelerated— the life of a
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culture of amoebae. For quite a while we looked at something we had
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never seen: I can only describe it as identifiable bubbles with
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irregular changing contours, slowly moving without any pattern through a
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two-dimensional aquarium. To all intents and purposes it could have been
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some sort of dynamic wallpaper. It was, in fact, rather boring, looking
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at those aimlessly moving grey blots, until one of the amoeba in the
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centre of the screen began to divide. We saw it constrict, we saw in
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succession all the images familiar from our high-school biology, we saw
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the centres of the two halves move in opposite directions until they
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were only connected by a thin thread as they began to pull more
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frantically at either end of the leash that still connected them.
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Finally the connection broke and the two swam away from each other at
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the maximum speed young amoebae can muster.
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The fascinating and somewhat frightening observation, however, was that
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at the moment of the rupture one hundred otherwise respectable
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scientists gave all a sigh of relief: “at last they had succeeded in
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freeing themselves from each other.” None of us had been able to resist,
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as the division process went on, the temptation to discern two
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individuals with which we could identify and of which we felt —more in
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our bones than in our brains, but that is beside the point— how much
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they “wanted” to get loose. A whole pattern of human desires had been
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projected on those blots\! Crazy, of course, but such is the pervasive
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and insidious habit of anthropomorphic thought.
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Is anthropomorphic thinking bad? Well, it is certainly no good in the
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sense that it does not help. Why did the stone fall in Greek antiquity?
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Quite simply because it wanted to go to the centre of the earth. And,
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several centuries later, we had the burning question: why do stones want
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to go to the centre of the earth? Well, that is simple too: because
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that’s where they belong. Why are heavier stones heavier than lighter
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stones? Because they are more eager to be at the centre of the earth.
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But then Galileo made the troubling discovery that the heavier stone
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does not fall any faster than the lighter one. How come? Simple, dear
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Watson: the heavier stone has indeed a greater desire to be at the
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centre of the earth, but it is also more lazy. So much for a —be it
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somewhat simplified— history of the development of physics. I trust you
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got the message.
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So anthropomorphic thinking is no good in the sense that it does not
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help. But is it also bad? Yes, it is, because even if we can point to
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some analogy between Man and Thing, the analogy is always negligible in
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comparison to the differences, and as soon as we allow ourselves to be
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seduced by the analogy to describe the Thing in anthropomorphic
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terminology, we immediately lose our control over which human
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connotations we drag into the picture. And as most of those are totally
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inadequate, the anthropomorphism becomes more misleading than helpful.
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I started as a theoretical physicist, became involved in computing and
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may end up as a mathematician. It is specifically my connection with
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computing that has made me allergic, since computing science is cursed
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by a rampant anthropomorphism.
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This has been so right from its inception, and found its way in the
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public perception of the topic, as is illustrated by the title of the
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book that Edmund C. Berkeley published in the fifties: “Giant Brains or
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Machines that Think”. The simplest way of showing how preposterous that
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title is is by pointing at its two companion volumes —still to be
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written— “Giant Hearts or Machines that Fall in Love” and “Giant Souls
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or Machines that Believe in God”, the most fascinating feature of the
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latter, of course, being that they can believe in God much faster than
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you. Regrettably we cannot sweep this nonsense under the rug by saying
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“Why bother? This is only popular press”. It finds its echo in
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publications that are intended to be serious, such as Grace M. Hopper’s
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article with the title “The education of a computer.”. It also finds its
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reflection in the multi-billion yen mistake of the Japanese “fifth
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generation computer project”, of which you may have heard. It would have
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taken care of the Japanese competition; regrettably —for the Western
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world— they seem to come to their senses, as the larger Japanese
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companies are pulling out of the efforts aimed at blurring the
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distinction between Man and Machine.
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But the blur continues to linger on, and has a much wider impact than
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you might suspect. You see, it is not only that the question “Can
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machines think?” is regularly raised; we can —and should— deal with that
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by pointing out that it is just as relevant as the equally burning
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question “Can submarines swim?” A more serious byproduct of the tendency
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to talk about machines in anthropomorphic terms is the companion
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phenomenon of talking about people in mechanistic terminology. The
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critical reading of articles about computer-assisted learning —excuse
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me: CAL for the intimi— leaves you no option: in the eyes of their
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authors, the educational process is simply reduced to a caricature,
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something like the building up of conditional reflexes. For those
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educationists, Pavlov’s dog adequately captures the essence of Mankind
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—while I can assure you, from intimate observations, that it only
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captures a minute fraction of what is involved in being a dog—.
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The anthropomorphic metaphor is perhaps even more devastating within
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computing science itself. Its use is almost all-pervading. To give you
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just an example: entering a lecture hall at a conference I caught just
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one sentence and quickly went out again. The sentence started with “When
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this guy wants to talk to that guy...”. The speaker referred to two
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components of a computer network.
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The trouble with the metaphor is, firstly, that it invites you to
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identify yourself with the computational processes going on in system
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components and, secondly, that we see ourselves as existing in time.
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Consequently the use of the metaphor forces one to what we call
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“operational reasoning”, that is reasoning in terms of the
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computational processes that could take place. From a methodological
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point of view this is a well-identified and well-documented mistake: it
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induces a combinatorial explosion of the number of cases to consider and
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designs thus conceived are as a result full of bugs.
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It is possible to base one’s reasoning on non-operational semantics and
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to design for instance one’s programs by manipulating one’s program text
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as a formal object in its own right, in one’s arguments completely
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ignoring that these texts also admit the interpretation of executable
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code. By ignoring the computational processes one saves oneself from the
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combinatorial explosion. This nonoperational approach is the only known
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reliable way of digital system design, and enables you to publish for
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instance in full confidence intricate algorithms you designed but never
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tested on a machine. The implied abstraction, in which time has
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disappeared from the picture, is however beyond the computing scientist
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imbued with the operational approach that the anthropomorphic metaphor
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induces. In a very real and tragic sense he has a mental block: his
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anthropomorphic thinking erects an insurmountable barrier between him
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and the only effective way in which his work can be done well. By the
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prevailing anthropomorphism the US, computer industry could easily be
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done in.
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It is not only the industry that suffers, so does the science. Recently,
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a whole group of computing scientists from all over the world has wasted
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several years of effort. They had decided to apply to the relationship
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between a component and its environment a dichotomy: the “obligations”
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of the environment versus the “responsibilities” of the component. The
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terminology alone should have been sufficient to make them very
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suspicious; it did not and they learned the hard way that the whole
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distinction did not make sense.
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Another notion that creeps in as a result of our anthropomorphism is the
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dichotomy of cause and effect. These terms come from our perception of
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our intended acts: we wish to pour ourselves a glass of wine, so we pick
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up the bottle and turn it, thereby causing the wine to flow from the
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bottle into our glass. Our act of pouring had the desired effect. But in
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the inanimate world there is little place for such a causal hierarchy.
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One of Newton’s Laws says that force equals mass times acceleration, and
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there is no point in insisting that the one causes the other or the
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other way round: they are equal. In the case of a piezo-electric crystal
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deformation and voltage difference are accompanying phenomena: if one
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applies a voltage difference, the crystal changes its shape, if the
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crystal is deformed, a voltage difference appears (as we all know from
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the butane cigarette lighter).
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In particular the study of distributed computer systems has severely
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suffered from the vain effort to impose a causal hierarchy on the events
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that constitute a computational process, thus completely hiding the
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symmetry between the sending and the receiving of messages, and between
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input and output.
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But even in the so much more abstract world of mathematics this has
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created havoc. It has caused a preponderance of mathematical structures
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of the form: “If A then B” or equivalently “A implies B”. Take good old
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Pythagoras
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> “If, in triangle ABC, angle C is right, then a2+b2=c2”.
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but we have equally well
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> “If, in triangle ABC, a2+b2=c2, then angle C is right”.
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and the proper way of stating Pythagoras’s Theorem is by saying that in
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triangle ABC “a2+b2=c2” and “angle C is right” are equivalent
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propositions, either both true or both false. Analyzing the structure of
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traditional mathematical arguments one will discover that the
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equivalence is the most underexploited logical connective, in contrast
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to the implication that is used all over the place. The
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underexploitation of the equivalence, i.e. the failure to exploit
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inherent symmetries, often lengthens an argument by a factor of 2, 4 or
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more.
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Why then have mathematicians stuck to the implication? Well, because
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they feel comfortable with it because they associate it —again\!— with
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cause and effect. They will rephrase “If A then B” also as “B because A”
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or “B follows from A”. (The use of the words “because” and “follows” is
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very revealing\!). Somehow, in the implication “if A then B”, the
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antecedent A is associated with the cause and the consequent B with the
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effect.
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One can defend the thesis that traditional mathematics is
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anthropomorphic in the sense that its proofs reflect the causal
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hierarchy we discern in our acts, in the same way that traditional logic
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—for centuries viewed as the handmaiden of philosophy— is
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anthropomorphic in the sense that it tries to formalize and follow our
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habits of reasoning.
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The advantage of this thesis is that it invites the speculation how
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mathematics and logic will evolve when they divest themselves from our
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ingrained human reasoning habits, when the role of formalisms will no
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longer be to mimic our familiar reasoning patterns but to liberate
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ourselves from the latter’s shackles.
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And that is a fascinating question to ponder about\!
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Austin, 23 September 1985
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prof. dr. Edsger W. Dijkstra
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Department of Computer Sciences
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The University of Texas at Austin
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AUSTIN, Texas 78712–1188
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USA
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Transcribed by Michael Lugo
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Last revised 10 April, 2016 .
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