Zen and the Art of Science: A Tribute to Robert Pirsig

Author Robert Pirsig, widely acclaimed for his bestselling books, Zen and the Art of Motorcycle Maintenance (1974) and Lila (1991), passed away in his home on April 24, 2017. A well-rounded intellectual equally at home in the sciences and the humanities, Pirsig made the case that scientific inquiry, art, and religious experience were all particular forms of knowledge arising out of a broader form of knowledge about the Good or what Pirsig called “Quality.” Yet, although Pirsig’s books were bestsellers, contemporary debates about science and religion are oddly neglectful of Pirsig’s work. So what did Pirsig claim about the common roots of human knowledge, and how do his arguments provide a basis for reconciling science and religion?

Pirsig gradually developed his philosophy as response to a crisis in the foundations of scientific knowledge, a crisis he first encountered while he was pursuing studies in biochemistry. The popular consensus at the time was that scientific methods promised objectivity and certainty in human knowledge. One developed hypotheses, conducted observations and experiments, and came to a conclusion based on objective data. That was how scientific knowledge accumulated.

However, Pirsig noted that, contrary to his own expectations, the number of hypotheses could easily grow faster than experiments could test them. One could not just come up with hypotheses – one had to make good hypotheses, ones that could eliminate the need for endless and unnecessary observations and testing. Good hypotheses required mental inspiration and intuition, components that were mysterious and unpredictable.  The greatest scientists were precisely like the greatest artists, capable of making immense creative leaps before the process of testing even began.  Without those creative leaps, science would remain on a never-ending treadmill of hypothesis development – this was the “infinity of hypotheses” problem.  And yet, the notion that science depended on intuition and artistic leaps ran counter to the established view that the scientific method required nothing more than reason and the observation and recording of an objective reality.

Consider Einstein. One of history’s greatest scientists, Einstein hardly ever conducted actual experiments. Rather, he frequently engaged in “thought experiments,” imagining what it would be like to chase a beam of light, what it would feel like to be in a falling elevator, and what a clock would look like if the streetcar he was riding raced away from the clock at the speed of light.

One of the most fruitful sources of hypotheses in science is mathematics, a discipline which consists of the creation of symbolic models of quantitative relationships. And yet, the nature of mathematical discovery is so mysterious that mathematicians themselves have compared their insights to mysticism. The great French mathematician Henri Poincare believed that the human mind worked subliminally on problems, and his work habit was to spend no more than two hours at a time working on mathematics. Poincare believed that his subconscious would continue working on problems while he conducted other activities, and indeed, many of his great discoveries occurred precisely when he was away from his desk. John von Neumann, one of the best mathematicians of the twentieth century, also believed in the subliminal mind. He would sometimes go to sleep with a mathematical problem on his mind and wake up in the middle of the night with a solution. The Indian mathematical genius Srinivasa Ramanujan was a Hindu mystic who believed that solutions were revealed to him in dreams by the goddess Namagiri.

Intuition and inspiration were human solutions to the infinity-of-hypotheses problem. But Pirsig noted there was a related problem that had to be solved — the infinity of facts.  Science depended on observation, but the issue of which facts to observe was neither obvious nor purely objective.  Scientists had to make value judgments as to which facts were worth close observation and which facts could be safely overlooked, at least for the moment.  This process often depended heavily on an imprecise sense or feeling, and sometimes mere accident brought certain facts to scientists’ attention. What values guided the search for facts? Pirsig cited Poincare’s work The Foundations of Science. According to Poincare, general facts were more important than particular facts, because one could explain more by focusing on the general than the specific. Desire for simplicity was next – by beginning with simple facts, one could begin the process of accumulating knowledge about nature without getting bogged down in complexity at the outset. Finally, interesting facts that provided new findings were more important than facts that were unimportant or trivial. The point was not to gather as many facts as possible but to condense as much experience as possible into a small volume of interesting findings.

Research on the human brain supports the idea that the ability to value is essential to the discernment of facts.  Professor of Neuroscience Antonio Damasio, in his book Descartes’ Error: Emotion, Reason, and the Human Brain, describes several cases of human beings who lost the part of their brain responsible for emotions, either because of an accident or a brain tumor.  These persons, some of whom were previously known as shrewd and smart businessmen, experienced a serious decline in their competency after damage took place to the emotional center of their brains.  They lost their capacity to make good decisions, to get along with other people, to manage their time, or to plan for the future.  In every other respect, these persons retained their cognitive abilities — their IQs remained above normal and their personality tests resulted in normal scores.  The only thing missing was their capacity to have emotions.  Yet this made a huge difference.  Damasio writes of one subject, “Elliot”:

Consider the beginning of his day: He needed prompting to get started in the morning and prepare to go to work.  Once at work he was unable to manage his time properly; he could not be trusted with a schedule.  When the job called for interrupting an activity and turning to another, he might persist nonetheless, seemingly losing sight of his main goal.  Or he might interrupt the activity he had engaged, to turn to something he found more captivating at that particular moment.  Imagine a task involving reading and classifying documents of a given client.  Elliot would read and fully understand the significance of the material, and he certainly knew how to sort out the documents according to the similarity or disparity of their content.  The problem was that he was likely, all of a sudden, to turn from the sorting task he had initiated to reading one of those papers, carefully and intelligently, and to spend an entire day doing so.  Or he might spend a whole afternoon deliberating on which principle of categorization should be applied: Should it be date, size of document, pertinence to the case, or another?   The flow of work was stopped. (p. 36)

Why did the loss of emotion, which might be expected to improve decision-making by making these persons coldly objective, result in poor decision-making instead?  According to Damasio, without emotions, these persons were unable to value, and without value, decision-making in the face of infinite facts became hopelessly capricious or paralyzed, even with normal or above-normal IQs.  Damasio noted, “the cold-bloodedness of Elliot’s reasoning prevented him from assigning different values to different options, and made his decision-making landscape hopelessly flat.” (p. 51) Damasio discusses several other similar case studies.

So how would it affect scientific progress if all scientists were like the subjects Damasio studied, free of emotion, and therefore, hypothetically capable of perfect objectivity?  Well it seems likely that science would advance very slowly, at best, or perhaps not at all.  After all, the same tools for effective decision-making in everyday life are needed for the scientific enterprise as well. A value-free scientist would not only be unable to sustain the social interaction that science requires, he or she would be unable to develop a research plan, manage his or her time, or stick to a research plan.


Where Pirsig’s philosophy becomes particularly controversial and difficult to understand is in his approach to the truth. The dominant view of truth today is known as the “correspondence” theory of truth – that is, any human statement that is true must correspond precisely to something objectively real. In this view, the laws of physics and chemistry are real because they correspond to actual events that can be observed and demonstrated. Pirsig argues on the contrary that in order to understand reality, human beings must invent symbolic and conceptual models, that there is a large creative component to these models (it is not just a matter of pure correspondence to reality), and that multiple such models can explain the same reality even if they are based on wholly different principles. Math, logic, and even the laws of physics are not “out there” waiting to be discovered – they exist in the mind, which doesn’t mean that these things are bad or wrong or unreal.

There are several reasons why our symbolic and conceptual models don’t correspond literally to reality, according to Pirsig. First, there is always going to be a gap between reality and the concepts we use to describe reality, because reality is continuous and flowing, while concepts are discrete and static. The creation of concepts necessarily calls for cutting reality into pieces, but there is no one right way to divide reality, and something is always lost when this is done. In fact, Pirsig noted, our very notions of subjectivity and objectivity, the former allegedly representing personal whims and the latter representing truth, rested upon an artificial division of reality into subjects and objects; in fact, there were other ways of dividing reality that could be just as legitimate or useful. In addition, concepts are necessarily static – they can’t be always changing or we would not be able to make sense of them. Reality, however, is always changing. Finally, describing reality is not always a matter of using direct and literal language but may require analogy and imaginative figures of speech.

Because of these difficulties in expressing reality directly, a variety of symbolic and conceptual models, based on widely varying principles, are not only possible but necessary – necessary for science as well as other forms of knowledge. Pirsig points to the example of the crisis that occurred in mathematics in the nineteenth century. For many centuries, it was widely believed that geometry, as developed by the ancient Greek mathematician Euclid, was the most exact of all of the sciences.  Based on a small number of axioms from which one could deduce multiple propositions, Euclidean geometry represented a nearly perfect system of logic.  However, while most of Euclid’s axioms were seemingly indisputable, mathematicians had long experienced great difficulty in satisfactorily demonstrating the truth of one of the chief axioms on which Euclidean geometry was based. This slight uncertainty led to an even greater crisis of uncertainty when mathematicians discovered that they could reverse or negate this axiom and create alternative systems of geometry that were every bit as logical and valid as Euclidean geometry.  The science of geometry was gradually replaced by the study of multiple geometries. Pirsig cited Poincare, who pointed out that the principles of geometry were not eternal truths but definitions and that the test of a system of geometry was not whether it was true but how useful it was.

So how do we judge the usefulness or goodness of our symbolic and conceptual models? Traditionally, we have been told that pure objectivity is the only solution to the chaos of relativism, in which nothing is absolutely true. But Pirsig pointed out that this hasn’t really been how science has worked. Rather, models are constructed according to the often competing values of simplicity and generalizability, as well as accuracy. Theories aren’t just about matching concepts to facts; scientists are guided by a sense of the Good (Quality) to encapsulate as much of the most important knowledge as possible into a small package. But because there is no one right way to do this, rather than converging to one true symbolic and conceptual model, science has instead developed a multiplicity of models. This has not been a problem for science, because if a particular model is useful for addressing a particular problem, that is considered good enough.

The crisis in the foundations of mathematics created by the discovery of non-Euclidean geometries and other factors (such as the paradoxes inherent in set theory) has never really been resolved. Mathematics is no longer the source of absolute and certain truth, and in fact, it never really was. That doesn’t mean that mathematics isn’t useful – it certainly is enormously useful and helps us make true statements about the world. It’s just that there’s no single perfect and true system of mathematics. (On the crisis in the foundations of mathematics, see the papers here and here.) Mathematical axioms, once believed to be certain truths and the foundation of all proofs, are now considered definitions, assumptions, or hypotheses. And a substantial number of mathematicians now declare outright that mathematical objects are imaginary, that particular mathematical formulas may be used to model real events and relationships, but that mathematics itself has no existence outside the human mind. (See The Mathematical Experience by Philip J. Davis and Reuben Hersh.)

Even some basic rules of logic accepted for thousands of years have come under challenge in the past hundred years, not because they are absolutely wrong, but because they are inadequate in many cases, and a different set of rules is needed. The Law of the Excluded Middle states that any proposition must be either true or false (“P” or “not P” in symbolic logic). But ever since mathematicians discovered propositions which are possibly true but not provable, a third category of “possible/unknown” has been added. Other systems of logic have been invented that use the idea of multiple degrees of truth, or even an infinite continuum of truth, from absolutely false to absolutely true.

The notion that we need multiple symbolic and conceptual models to understand reality remains controversial to many. It smacks of relativism, they argue, in which every person’s opinion is as valid as another person’s. But historically, the use of multiple perspectives hasn’t resulted in the abandonment of intellectual standards among mathematicians and scientists. One still needs many years of education and an advanced degree to obtain a job as a mathematician or scientist, and there is a clear hierarchy among practitioners, with the very best mathematicians and scientists working at the most prestigious universities and winning the highest awards. That is because there are still standards for what is good mathematics and science, and scholars are rewarded for solving problems and advancing knowledge. The fact that no one has agreed on what is the One True system of mathematics or logic isn’t relevant. In fact, physicist Stephen Hawking has argued:

[O]ur brains interpret the input from our sensory organs by making a model of the world. When such a model is successful at explaining events, we tend to attribute to it, and to the elements and concepts that constitute it, the quality of reality or absolute truth. But there may be different ways in which one could model the same physical situation, with each employing different fundamental elements and concepts. If two such physical theories or models accurately predict the same events, one cannot be said to be more real than the other; rather we are free to use whichever model is more convenient (The Grand Design, p. 7).

Among the most controversial and mind-bending claims Pirsig makes is that the very laws of nature themselves exist only in the human mind. “Laws of nature are human inventions, like ghosts,” he writes. Pirsig even remarks that it makes no sense to think of the law of gravity existing before the universe, that it only came into existence when Isaac Newton thought of it. It’s an outrageous claim, but if one looks closely at what the laws of nature actually are, it’s not so crazy an argument as it first appears.

For all of the advances that science has made over the centuries, there remains a sharp division of views among philosophers and scientists on one very important issue: are the laws of nature actual causal powers responsible for the origins and continuance of the universe or are the laws of nature summary descriptions of causal patterns in nature? The distinction is an important one. In the former view, the laws of physics are pre-existing or eternal and possess god-like powers to create and shape the universe; in the latter view, the laws have no independent existence – we are simply finding causal patterns and regularities in nature that allow us to predict and we call these patterns “laws.”

One powerful argument in favor of the latter view is that most of the so-called “laws of nature,” contrary to the popular view, actually have exceptions – and sometimes the exceptions are large. That is because the laws are simplified models of real phenomena. The laws were cobbled together by scientists in order to strike a careful balance between the values of scope, predictive accuracy, and simplicity. Michael Scriven, a mathematician and philosopher at Claremont Graduate University, has noted that as a result of this balance of values, physical laws are actually approximations that apply only within a certain range. This point has also been made more recently by Ronald Giere, a professor of philosophy at the University of Minnesota, in Science Without Laws and Nancy Cartwright of the University of California at San Diego in How the Laws of Physics Lie.

Newton’s law of universal gravitation, for example, is not really universal. It becomes increasingly inaccurate under conditions of high gravity and very high velocities, and at the atomic level, gravity is completely swamped by other forces. Whether one uses Newton’s law depends on the specific conditions and the level of accuracy one requires. Newton’s laws of motion also have exceptions, depending on the force, distance, and speed. Kepler’s laws of planetary motion are an approximation based on the simplifying assumption of a planetary system consisting of one planet. The ideal gas law is an approximation which becomes inaccurate under conditions of low temperature and/or high pressure. The law of multiple proportions works for simple molecular compounds, but often fails for complex molecular compounds. Biologists have discovered so many exceptions to Mendel’s laws of genetics that some believe that Mendel’s laws should not even be considered laws.

So if we think of laws of nature as being pre-existing, eternal commandments, with god-like powers to shape the universe, how do we account for these exceptions to the laws? The standard response by scientists is that their laws are simplified depictions of the real laws. But if that is the case, why not state the “real” laws? Because by the time we wrote down the real laws, accounting for every possible exception, we would have an extremely lengthy and detailed description of causation that would not recognizably be a law. The whole point of the laws of nature was to develop tools by which one could predict a large number of phenomena (scope), maintain a good-enough correspondence to reality (accuracy), and make it possible to calculate predictions without spending an inordinate amount of time and effort (simplicity). That is why although Einstein’s conception of gravity and his “field equations” have supplanted Newton’s law of gravitation, physicists still use Newton’s “law” in most cases because it is simpler and easier to use; they only resort to Einstein’s complex equations when they have to! The laws of nature are human tools for understanding, not mathematical gods that shape the universe. The actual practice of science confirms Pirsig’s point that the symbolic and conceptual models that we create to understand reality have to be judged by how good they are – simple correspondence to reality is insufficient and in many cases is not even possible anyway.



Ultimately, Pirsig concluded, the scientific enterprise is not that different from the pursuit of other forms of knowledge – it is based on a search for the Good. Occasionally, you see this acknowledged explicitly, when mathematicians discuss the beauty of certain mathematical proofs or results, as defined by their originality, simplicity, ability to solve many problems at once, or their surprising nature. Scientists also sometimes write about the importance of elegance in their theories, defined as the ability to explain as much as possible, as clearly as possible, and as simply as possible. Depending on the field of study, the standards of judgment may be different, the tools may be different, and the scope of inquiry is different. But all forms of human knowledge — art, rhetoric, science, reason, and religion — originate in, and are dependent upon, a response to the Good or Quality. The difference between science and religion is that scientific models are more narrowly restricted to understanding how to predict and manipulate natural phenomena, whereas religious models address larger questions of meaning and value.

Pirsig did not ignore or suppress the failures of religious knowledge with regard to factual claims about nature and history. The traditional myths of creation and the stories of various prophets were contrary to what we know now about physics, biology, paleontology, and history. In addition, Pirsig was by no means a conventional theist — he apparently did not believe that God was a personal being who possessed the attributes of omniscience and omnipotence, controlling or potentially controlling everything in the universe.

However, Pirsig did believe that God was synonymous with the Good, or “Quality,” and was the source of all things.  In fact, Pirsig wrote that his concept of Quality was similar to the “Tao” (the “Way” or the “Path”) in the Chinese religion of Taoism. As such, Quality was the source of being and the center of existence. It was also an active, dynamic power, capable of bringing about higher and higher levels of being. The evolution of the universe, from simple physical forms, to complex chemical compounds, to biological organisms, to societies was Dynamic Quality in action. The most recent stage of evolution – Intellectual Quality – refers to the symbolic models that human beings create to understand the universe. They exist in the mind, but are a part of reality all the same – they represent a continuation of the growth of Quality.

What many religions were missing, in Pirsig’s view, was not objectivity, but dynamism: an ability to correct old errors and achieve new insights. The advantage of science was its willingness and ability to change. According to Pirsig,

If scientists had simply said Copernicus was right and Ptolemy was wrong without any willingness to further investigate the subject, then science would have simply become another minor religious creed. But scientific truth has always contained an overwhelming difference from theological truth: it is provisional. Science always contains an eraser, a mechanism whereby new Dynamic insight could wipe out old static patterns without destroying science itself. Thus science, unlike orthodox theology, has been capable of continuous, evolutionary growth. (Lila, p. 222)

The notion that religion and orthodoxy go together is widespread among believers and secularists. But there is no necessary connection between the two. All religions originate in social processes of story-telling, dialogue, and selective borrowing from other cultures. In fact, many religions begin as dangerous heresies before they become firmly established — orthodoxies come later. The problem with most contemporary understandings of religion is that one’s adherence to religion is often measured by one’s commitment to orthodoxy and membership in religious institutions rather than an honest quest for what is really good.  A person who insists on the literal truth of the Bible and goes to church more than once a week is perceived as being highly religious, whereas a person not connected with a church but who nevertheless seeks religious knowledge wherever he or she can find it is considered less committed or even secular.  This prejudice has led many young people to identify as “spiritual, not religious,” but religious knowledge is not inherently about unwavering loyalty to an institution or a text. Pirsig believed that mysticism was a necessary component of religious knowledge and a means of disrupting orthodoxies and recovering the dynamic aspect of religious insight.

There is no denying that the most prominent disputes between science and religion in the last several centuries regarding the physical workings of the universe have resulted in a clear triumph for scientific knowledge over religious knowledge.  But the solution to false religious beliefs is not to discard religious knowledge — religious knowledge still offers profound insights beyond the scope of science. That is why it is necessary to recover the dynamic nature of religious knowledge through mysticism, correction of old beliefs, and reform. As Pirsig argued, “Good is a noun.” Not because Good is a thing or an object, but because Good  is the center and foundation of all reality and all forms of knowledge, whether we are consciously aware of it or not.

What Does Science Explain? Part 3 – The Mythos of Objectivity

In parts one and two of my series “What Does Science Explain?,” I contrasted the metaphysics of the medieval world with the metaphysics of modern science. The metaphysics of modern science, developed by Kepler, Galileo, Descartes, and Newton, asserted that the only true reality was mathematics and the shape, motion, and solidity of objects, all else being subjective sensations existing solely within the human mind. I pointed out that the new scientific view was valuable in developing excellent predictive models, but that scientists made a mistake in elevating a method into a metaphysics, and that the limitations of the metaphysics of modern science called for a rethinking of the modern scientific worldview. (See The Metaphysical Foundations of Modern Science by Edwin Arthur Burtt.)

Early scientists rejected the medieval worldview that saw human beings as the center and summit of creation, and this rejection was correct with regard to astronomical observations of the position and movement of the earth. But the complete rejection of medieval metaphysics with regard to the role of humanity in the universe led to a strange division between theory and practice in science that endures to this day. The value and prestige of science rests in good part on its technological achievements in improving human life. But technology has a two-sided nature, a destructive side as well as a creative side. Aspects of this destructive side include automatic weaponry, missiles, conventional explosives, nuclear weapons, biological weapons, dangerous methods of climate engineering, perhaps even a threat from artificial intelligence. Even granting the necessity of the tools of violence for deterrence and self-defense, there remains the question of whether this destructive technology is going too far and slipping out of our control. So far the benefits of good technology have outweighed the hazards of destructive technology, but what research guidance is offered to scientists when human beings are removed from their high place in the universe and human values are separated from the “real” world of impersonal objects?

Consider the following question: Why do medical scientists focus their research on the treatment and cure of illness in humans rather than the treatment and cure of illness in cockroaches or lizards? This may seem like a silly question, but there’s no purely objective, scientific reason to prefer one course of research over another; the metaphysics of modern science has already disregarded the medieval view that humans have a privileged status in the universe. One could respond by arguing that human beings have a common self-interest in advancing human health through medical research, and this self-interest is enough. But what is the scientific justification for the pursuit of self-interest, which is not objective anyway? Without a recognition of the superior value of human life, medical science has no research guidance.

Or consider this: right now, astronomers are developing and employing advanced technologies to detect other worlds in the galaxy that may have life. The question of life on other planets has long interested astronomers, but it was impossible with older technologies to adequately search for life. It would be safe to say that the discovery of life on another planet would be a landmark development in science, and the discovery of intelligent life on another planet would be an astonishing development. The first scientist who discovered a world with intelligent life would surely win awards and fame. And yet, we already have intelligent life on earth and the metaphysics of modern science devalues it. In practice, of course, most scientists do value human life; the point is, the metaphysics behind science doesn’t, leaving scientists at a loss for providing an intellectual justification for a research program that protects and advances human life.

A second limitation of modern science’s metaphysics, closely related to the first, is its disregard of certain human sensations in acquiring knowledge. Early scientists promoted the view that only the “primary qualities” of mathematics, shape, size, and motion were real, while the “secondary qualities” of color, taste, smell, and sound existed only in the mind. This distinction between primary and secondary qualities was criticized at the time by philosophers such as George Berkeley, a bishop of the Anglican Church. Berkeley argued that the distinction between primary and secondary qualities was false and that even size, shape, and motion were relative to the perceptions and judgment of observers. Berkeley also opposed Isaac Newton’s theory that space and time were absolute entities, arguing instead that these were ideas rooted in human sensations. But Berkeley was disregarded by scientists, largely because Newton offered predictive models of great value.

Three hundred years later, Isaac Newton’s models retain their great value and are still widely used — but it is worth noting that Berkeley’s metaphysics has actually proved superior in many respects to Newton’s metaphysics.

Consider the nature of mathematics. For many centuries mathematicians believed that mathematical objects were objectively real and certain and that Euclidean geometry was the one true geometry. However, the discovery of non-Euclidean geometries in the nineteenth century shook this assumption, and mathematicians had to reconcile themselves to the fact that it was possible to create multiple geometries of equal validity. There were differences between the geometries in terms of their simplicity and their ability to solve particular problems, but no one geometry was more “real” than the others.

If you think about it, this should not be surprising. The basic objects of geometry — points, lines, and planes — aren’t floating around in space waiting for you to take note of them. They are concepts, creations of the human brain. We may see particular objects that resemble points, lines, and planes, but space itself has no visible content; we have to add content to it.  And we have a choice in what content to use. It is possible to create a geometry in which all lines are straight or all lines are curved; in which some lines are parallel or no lines are parallel;  or in which lines are parallel over a finite distance but eventually meet at some infinitely great distance. It is also possible to create a geometry with axioms that assume no lines, only points; or a geometry that assumes “regions” rather than points. So the notion that mathematics is a “primary quality” that exists within objects independent of human minds is a myth. (For more on the imaginary qualities of mathematics, see my previous posts here and here.)

But aside from the discovery of multiple mathematical systems, what has really killed the artificial distinction between “primary qualities,” allegedly objective, and “secondary qualities,” allegedly subjective, is modern science itself, particularly in the findings of relativity theory and quantum mechanics.

According to relativity theory, there is no single, objectively real size, shape, or motion of objects — these qualities are all relative to an observer in a particular reference frame (say, at the same location on earth, in the same vehicle, or in the same rocket ship). Contrary to some excessive and simplistic views, relativity theory does NOT mean that any and all opinions are equally valid. In fact, all observers within the same reference frame should be seeing the same thing and their measurements should match. But observers in different reference frames may have radically different measurements of the size, shape, and motion of an object, and there is no one single reference frame that is privileged — they are all equally valid.

Consider the question of motion. How fast are you moving right now? Relative to your computer or chair, you are probably still. But the earth is rotating at 1040 miles per hour, so relative to an observer on the moon, you would be moving at that speed — adjusting for the fact that the moon is also orbiting around the earth at 2288 miles per hour. But also note that the earth is orbiting the sun at 66,000 miles per hour, our solar system is orbiting the galaxy at 52,000 miles per hour, and our galaxy is moving at 1,200,000 miles per hour; so from the standpoint of an observer in another galaxy you are moving at a fantastically fast speed in a series of crazy looping motions. Isaac Newton argued that there was an absolute position in space by which your true, objective speed could be measured. But Einstein dismissed that view, and the scientific consensus today is that Einstein was right — the answer to the question of how fast you are moving is relative to the location and speed of the observer.

The relativity of motion was anticipated by the aforementioned George Berkeley as early as the eighteenth century, in his Treatise Concerning the Principles of Human Knowledge (paragraphs 112-16). Berkeley’s work was subsequently read by the physicist Ernest Mach, who subsequently influenced Einstein.

Relativity theory also tells us that there is no absolute size and shape, that these also vary according to the frame of reference of an observer in relation to what is observed. An object moving at very fast speeds relative to an observer will be shortened in length, which also affects its shape. (See the examples here and here.) What is the “real” size and shape of the object? There is none — you have to specify the reference frame in order to get an answer. Professor Richard Wolfson, a physicist at Middlebury College who has a great lecture series on relativity theory, explains what happens at very fast speeds:

An example in which length contraction is important is the Stanford Linear Accelerator, which is 2 miles long as measured on Earth, but only about 3 feet long to the electrons moving down the accelerator at 0.9999995c [nearly the speed of light]. . . . [Is] the length of the Stanford Linear Accelerator ‘really’ 2 miles? No! To claim so is to give special status to one frame of reference, and that is precisely what relativity precludes. (Course Guidebook to Einstein’s Relativity and the Quantum Revolution, Lecture 10.)

In fact, from the perspective of a light particle (a photon), there is infinite length contraction — there is no distance and the entire universe looks like a point!

The final nail in the coffin of the metaphysics of modern science is surely the weird world of quantum physics. According to quantum physics, particles at the subatomic level do not occupy only one position at a particular moment of time but can exist in multiple positions at the same time — only when the subatomic particles are observed do the various possibilities “collapse” into a single outcome. This oddity led to the paradoxical thought experiment known as “Schrodinger’s Cat” (video here). The importance of the “observer effect” to modern physics is so great that some physicists, such as the late physicist John Wheeler, believed that human observation actually plays a role in shaping the very reality of the universe! Stephen Hawking holds a similar view, arguing that our observation “collapses” multiple possibilities into a single history of the universe: “We create history by our observation, rather than history creating us.” (See The Grand Design, pp. 82-83, 139-41.) There are serious disputes among scientists about whether uncertainties at the subatomic level really justify the multiverse theories of Wheeler and Hawking, but that is another story.

Nevertheless, despite the obsolescence of the metaphysical premises of modern science, when scientists talk about the methods of science, they still distinguish between the reality of objects and the unreality of what exists in the mind, and emphasize the importance of being objective at all times. Why is that? Why do scientists still use a metaphysics developed centuries ago by Kepler, Galileo, and Newton? I think this practice persists largely because the growth of knowledge since these early thinkers has led to overspecialization — if one is interested in science, one pursues a degree in chemistry, biology, or physics; if one is interested in metaphysics, one pursues a degree in philosophy. Scientists generally aren’t interested in or can’t understand what philosophers have to say, and philosophers have the same view of scientists. So science carries on with a metaphysics that is hundreds of years old and obsolete.

It’s true that the idea of objectivity was developed in response to the very real problem of the uncertainty of human sense impressions and the fallibility of the conclusions our minds draw in response to those sense impressions. Sometimes we think we see something, but we don’t. People make mistakes, they may see mirages; in extreme cases, they may hallucinate. Or we see the same thing but have different interpretations. Early scientists tried to solve this problem by separating human senses and the human mind from the “real” world of objects. But this view was philosophically dubious to begin with and has been refuted by science itself. So how do we resolve the problem of mistaken and differing perceptions and interpretations?

Well, we supplement our limited senses and minds with the senses and minds of other human beings. We gather together, we learn what others have perceived and concluded, we engage in dialogue and debate, we conduct repeated observations and check our results with the results of others. If we come to an agreement, then we have a tentative conclusion; if we don’t agree, more observation, testing, and dialogue is required to develop a picture that resolves the competing claims. In some cases we may simply end up with an explanation that accounts for why we come up with different conclusions — perhaps we are in different locations, moving at different speeds, or there is something about our sensory apparatus that causes us to sense differently. (There is an extensive literature in science about why people see colors differently due to the nature of the eye and brain.)

Central to the whole process of science is a common effort — but there is also the necessity of subduing one’s ego, acknowledging that not only are there other people smarter than we are, but that the collective efforts of even less-smart people are greater than our own individual efforts. Subduing one’s ego is also required in order to prepare for the necessity of changing one’s mind in response to new evidence and arguments. Ultimately, the search for knowledge is a social and moral enterprise. But we are not going to succeed in that endeavor by positing a reality separate from human beings and composed only of objects. (Next: Part 4)

The Mythos of Mathematics

‘Modern man has his ghosts and spirits too, you know.’


‘Oh, the laws of physics and of logic . . . the number system . . . the principle of algebraic substitution. These are ghosts. We just believe in them so thoroughly they seem real.’

Robert Pirsig, Zen and the Art of Motorcycle Maintenance


It is a popular position among physicists that mathematics is what ultimately lies behind the universe. When asked for an explanation for the universe, they point to numbers and equations, and furthermore claim that these numbers and equations are the ultimate reality, existing objectively outside the human mind. This view is known as mathematical Platonism, after the Greek philosopher Plato, who argued that the ultimate reality consisted of perfect forms.

The problem we run into with mathematical Platonism is that it is subject to some of the same skepticism that people have about the existence of God, or the gods. How do we know that mathematics exists objectively? We can’t sense mathematics directly; we only know that it is a useful tool for dealing with reality. The fact that math is useful does not prove that it exists independently of human minds. (For an example of this skepticism, see this short video).

Scholars George Lakoff and Rafael Nunez, in their book Where Mathematics Comes From: How the Embodied Mind Brings Mathematics into Being, offer the provocative and fascinating thesis that mathematics consists of metaphors. That is, the abstractions of mathematics are ultimately grounded in conceptual comparisons to concrete human experiences. In the view of Lakoff and Nunez, all human ideas are shaped by our bodily experiences, our senses, and how these senses react to our environment. We try to make sense of events and things by comparing them to our concrete experiences. For example, we conceptualize time as a limited resource (“time is money”); we conceptualize status or mood in terms of space (happy is “up,” while sad is “down”); we personify events and things (“inflation is eating up profits,” “we must declare war on poverty”). Metaphors are so prevalent and taken for granted, that most of the time we don’t even notice them.

Mathematical systems, according to Lakoff and Nunez, are also metaphorical creations of the human mind. Since human beings have common experiences with space, time, and quantities, our mathematical systems are similar. But we do have a choice in the metaphors we use, and that is where the creative aspect of mathematics comes in. In other words, mathematics is grounded in common experiences, but mathematical conceptual systems are creations of the imagination. According to Lakoff and Nunez, confusion and paradoxes arise when we take mathematics literally and don’t recognize the metaphors behind mathematics.

Lakoff and Nunez point to a number of common human activities that subsequently led to the creation of mathematical abstractions. The collection of objects led to the creation of the “counting numbers,” otherwise known as “natural numbers.” The use of containers led to the notion of sets and set theory. The use of measuring tools (such as the ruler or yard stick) led to the creation of the “number line.” The number line in turn was extended to a plane with x and y coordinates (the “Cartesian plane“). Finally, in order to understand motion, mathematicians conceptualized time as space, plotting points in time as if they were points in space — time is not literally the same as space, but it is easier for human beings to measure time if it is plotted on a spatial graph.

Throughout history, while the counting numbers have been widely accepted, there have been controversies over the creation of other types of numbers. One of the reasons for these controversies is the mistaken belief that numbers must be objectively real rather than metaphorical. So the number zero was initially controversial because it made no sense to speak literally of having a collection of zero objects. Negative numbers were even more controversial because it’s impossible to literally have a negative number of objects. But as the usefulness of zero and negative numbers as metaphorical expressions and in performing calculations became clear, these numbers became accepted as “real” numbers.

The metaphor of the measuring stick/number line, according to Lakoff and Nunez, has been responsible for even more controversy and confusion. The basic problem is that a line is a continuous object, not a collection of objects. If one makes an imaginative metaphorical leap and envisions the line as a collection of objects known as segments or points, that is very useful for measuring the line, but a line is not literally a collection of segments or points that correspond to objectively existing numbers.

If you draw three points on a piece of paper, the sum of the collection of points clearly corresponds to the number three, and only the number three. But if you draw a line on a piece of paper, how many numbers does it have? Where do those numbers go? The answer is up to you, depending on what you hope to measure and how much precision you want. The only requirement is that the numbers are in order and the length of the segments is consistently defined. You can put zero on the left side of the line, the right side of the line, or in the middle. You can use negative numbers or not use negative numbers. The length of the segments can be whatever you want, as long as the definitions of segment length are consistent.

The number line is a great mental tool, but it does not objectively exist, outside of the human mind. Neglecting this fact has led to paradoxes that confounded the ancient Greeks and continue to mystify human beings to this day. The first major problem arose when the Greeks attempted to determine the ratio of the sides of a particular polygon and discovered that the ratio could not be expressed as a ratio of whole numbers, but rather as an infinite, nonrepeating decimal. For example, a right triangle with two shorter sides of length 1 would, according to the Pythagorean theorem, have a hypotenuse length equivalent to the square root of 2, which is an infinite decimal: 1.41421356. . .  This scandalized the ancient Greeks at first, because many of them had a religious devotion to the idea that whole numbers existed objectively and were the ultimate basis of reality. Nevertheless, over time the Greeks eventually accepted the so-called “irrational numbers.”

Perhaps the most famous irrational number is pi, the measure of the ratio between the circumference of a circle and its diameter: 3.14159265. . . The fact that pi is an infinite decimal fascinates people to no end, and scientists have calculated the value of pi to over 13 trillion digits. But the digital representation of pi has no objective existence — it is simply a creation of the human imagination based on the metaphor of the measuring stick / number line. There’s no reason to be surprised or amazed that the ratio of the circumference of a circle to its diameter is an infinite decimal; lines are continuous objects, and expressing lines as being composed of discrete objects known as segments is bound to lead to difficulties eventually. Moreover, pi is not necessary for the existence of circles. Even children are perfectly capable of drawing circles without knowing the value of pi. If children can draw circles without knowing the value of pi, why should the universe need to know the value of pi? Pi is simply a mental tool that human beings created to understand the ratio of certain line lengths by imposing a conceptual framework of discrete segments on a continuous quantity. Benjamin Lee Buckley, in his book The Continuity Debate, underscores this point, noting that one can use discrete tools for measuring continuity, but that truly continuous quantities are not really composed of discrete objects.

It is true that mathematicians have designated pi and other irrational numbers as “real” numbers, but the reality of the existence of pi outside the human mind is doubtful. An infinitely precise pi implies infinitely precise measurement, but there are limits to how precise one can be in reality, even assuming absolutely perfect measuring instruments. Although pi has been calculated to over 13 trillion digits, it is estimated that only 39 digits are needed to calculate the volume of the known universe to the precision of one atom! Furthermore, the Planck length is the smallest measurable length in the universe. Although quite small, the Planck length sets a definite limit on how precise pi can be in reality. At some point, depending on the size of the circle one creates, the extra digits in pi are simply meaningless.

Undoubtedly, the number line is an excellent mental tool. If we had perfect vision, perfect memory, and perfect eye-hand coordination, we wouldn’t need to divide lines into segments and count how many segments there are. But our vision is imperfect, our memories fallible, and our eye-hand coordination is imperfect. That is why we need to use versions of the number line to measure things. But we need to recognize that we are creating and imposing a conceptual tool on reality. This tool is metaphorical and, while originating in human experience, it is not reality itself.

Lakoff and Nunez point to other examples of metaphorical expressions in mathematics, such as the concept of infinity. Mathematicians discuss the infinitely large, the infinitely small, and functions in calculus that come infinitely close to some designated limit. But Lakoff and Nunez point out that the notion of actual (literal) infinity, as opposed to potential infinity, has been extremely problematic, because calculating or counting infinity is inherently an endless process. Lakoff and Nunez argue that envisioning infinity as a thing, or the result of a completed process, is inherently metaphorical, not literal. If you’ve ever heard children use the phrase “infinity plus one!” in their taunts, you can see some of the difficulties with envisioning infinity as a thing, because one can simply take the allegedly completed process and start it again. Oddly, even professional mathematicians don’t agree on the question of whether “infinity plus one” is a meaningful statement. Traditional mathematics says that infinity plus one is still infinity, but there are more recent number systems in which infinity plus one is meaningful. (For a discussion of how different systems of mathematics arrive at different answers to the same question, see this post.)

Nevertheless, many mathematicians and physicists fervently reject the idea that mathematics comes from the human mind. If mathematics is useful for explaining and predicting real world events, they argue, then mathematics must exist in objective reality, independent of human minds. But why is it important for mathematics to exist objectively? Isn’t it enough that mathematics is a useful mental tool for describing reality? Besides, if all the mathematicians in the world stopped all their current work and devoted themselves entirely to proving the objective existence of mathematical objects, I doubt that they would succeed, and mathematical knowledge would simply stop progressing.

Uncertainty, Debate, and Imprecision in Mathematics

If you remember anything about the mathematics courses you took in high school, it is that mathematics is the one subject in which there is absolute certainty and precision in all its answers. Unlike history, social science, and the humanities, which offer a variety of interpretations of subject matter, mathematics is unified and absolute.  Two plus two equals four and that is that. If you answer a math problem wrong, there is no sense in arguing a different interpretation with the teacher. Even the “hard sciences,” such as physics, may revise long-established conclusions, as new evidence comes in and new theories are developed. But mathematical truths are seemingly forever. Or are they?

You might not know it, but there has been a revolution in the human understanding of mathematics in the past 150 years that has undermined the belief that mathematics holds the key to absolute truth about the nature of the universe. Even as mathematical knowledge has increased, uncertainty has also increased, and different types of mathematics have been created that have different premises and are incompatible with each other. The value of mathematics remains clear. Mathematics increases our understanding, and science would not be possible without it. But the status of mathematics as a source of precise and infallible truth about reality is less clear.

For over 2000 years, the geometrical conclusions of the Greek mathematician Euclid were regarded as the most certain type of knowledge that could be obtained. Beginning with a small number of axioms, Euclid developed a system of geometry that was astonishing in breadth. The conclusions of Euclid’s geometry were regarded as absolutely certain, being derived from axioms that were “self-evident.”  Indeed, if one begins with “self-evident” truths and derives conclusions from those truths in a logical and verifiable manner, then one’s conclusions must also be undoubtedly true.

However, in the nineteenth century, these truths were undermined by the discovery of new geometries based on different axioms — the so-called “non-Euclidean geometries.” The conclusions of geometry were no longer absolute, but relative to the axioms that one chose. This became something of a problem for the concept of mathematical “proof.” If one can build different systems of mathematics based on different axioms, then “proof” only means that one’s conclusions are derivable from one’s axioms, not that one’s conclusions are absolutely true.

If you peruse the literature of mathematics on the definition of “axiom,” you will see what I mean. Many authors include the traditional definition of an axiom as a “self-evident truth.” But others define an axiom as a “definition” or “assumption,” seemingly as an acceptable alternative to “self-evident truth.” Surely there is a big difference between an “assumption,” a “self-evident truth,” and a “definition,” no? This confusing medley of definitions of “axiom” is the result of the nineteenth century discovery of non-Euclidean geometries. The issue has not been fully cleared up by mathematicians, but the Wikipedia entry on “axiom” probably represents the consensus of most mathematicians, when it states: “No explicit view regarding the absolute truth of axioms is ever taken in the context of modern mathematics, as such a thing is considered to be irrelevant.”  (!)

In reaction to the new uncertainty, mathematicians responded by searching for new foundations for mathematics, in the hopes of finding a set of axioms that would establish once and for all the certainty of mathematics. The “Foundations of Mathematics” movement, as it came to be called, ultimately failed. One of the leaders of the foundations movement, the great mathematician Bertrand Russell, declared late in life:

I wanted certainty in the kind of way in which people want religious faith. I thought that certainty is more likely to be found in mathematics than elsewhere. But I discovered that many mathematical demonstrations, which my teachers expected me to accept, were full of fallacies, and that, if certainty were indeed discoverable in mathematics, it would be in a new kind of mathematics, with more solid foundations than those that had hitherto been thought secure. But as the work proceeded, I was continually reminded of the fable about the elephant and the tortoise. Having constructed an elephant upon which the mathematical world could rest, I found the elephant tottering, and proceeded to construct a tortoise to keep the elephant from falling. But the tortoise was no more secure than the elephant, and after some twenty years of arduous toil, I came to the conclusion that there was nothing more that I could do in the way of making mathematical knowledge indubitable. (The Autobiography of Bertrand Russell)

Today, there are a variety of mathematical systems based on a variety of assumptions, and no one yet has succeeded in reconciling all the systems into one, fundamental, true system of mathematics. In fact, you wouldn’t know it from high school math, but some topics in mathematics have led to sharp divisions and debates among mathematicians. And most of these debates have never really been resolved — mathematicians have simply grown to tolerate the existence of different mathematical systems in the same way that ancient pagans accepted the existence of multiple gods.

Some of the most contentious issues in mathematics have revolved around the concept of infinity. In the nineteenth century, the mathematician Georg Cantor developed a theory about different sizes of infinite sets, but his arguments immediately attracted criticism from fellow mathematicians and remain controversial to this day. The central problem is that measuring infinity, assigning a quantity to infinity, is inherently an endless process. Once you think you have measured infinity, you simply add a one to it, and you have something greater than infinity — which means your original infinity was not truly infinite. Henri Poincare, one of the greatest mathematicians in history, rejected Cantor’s theory, noting: “Actual infinity does not exist. What we call infinite is only the endless possibility of creating new objects no matter how many exist already.”  Stephen Simpson, a mathematician at Pennsylvania University likewise argues “What truly infinite objects exist in the real world?” Objections to Cantor’s theory of infinity led to the emergence of new mathematical schools of thought such as finitism and intuitionism, which rejected the legitimacy of infinite mathematical objects.

Cantor focused his mental energies on concepts of the infinitely large, but another idea in mathematics was also controversial — that of the infinitely small, the “infinitesimal.” To give you an idea of how controversial the infinitesimal has been, I note that Cantor himself rejected the existence of infinitesimals! In Cantor’s view, the concept of something being infinitely small was inherently contradictory — if something is small, then it is inherently finite! And yet, infinitesimals have been used by mathematicians for hundreds of years. The infinitesimal was used by Leibniz in his version of calculus, and it is used today in the field of mathematics known as “non-standard analysis.” There is still no consensus among mathematicians today about the existence or legitimacy of infinitesimals, but infinitesimals, like imaginary numbers, seem to be useful in calculations, and as long as it works, mathematicians are willing to tolerate them, albeit not without some criticism.

The existence of different types of mathematical systems leads to some strange and contradictory answers to some of the simplest questions in mathematics. In school, you were probably taught that parallel lines never meet. That is true in Euclidean geometry, but not in hyperbolic geometry. In projective geometry, parallel lines meet at infinity!

Or consider the infinite decimal 0.9999 . . .  Is this infinite decimal equal to 1? The common sense answer that students usually give is “of course not.” But most mathematicians argue that both numbers are equivalent! Their logic is as follows: in the system of “real numbers,” there is no number between 0.999. . . and 1. Therefore, if you subtract 0.999. . .  from 1, the result is zero. And that means both numbers are the same!

However, in the system of numbers known as “hyperreals,” a system which includes infinitesimals, there exists an infinitesimal number between 0.999. . .  and 1. So under this system, 0.999. . .  and 1 are NOT the same! (A great explanation of this paradox is here.) So which system of numbers is the correct one? There is no consensus among mathematicians. But there is a great joke:

How many mathematicians does it take to screw in a light bulb?

0.999 . . .

The invention of computers has led to the creation of a new system of mathematics known as “floating point arithmetic.” This was necessary because, for all of their amazing capabilities, computers do not have enough memory or processing capability to precisely deal with all of the real numbers. To truly depict an infinite decimal, a computer would need an infinite amount of memory. So floating point arithmetic deals with this problem by using a degree of approximation.

One of the odd characteristics of the standard version of floating point arithmetic is that there is not one zero, but two zeros: a positive zero and a negative zero. What’s that you say? There’s no such thing as positive zero and negative zero? Well, not in the number system you were taught, but these numbers do exist in floating point arithmetic. And you can use them to divide by zero, which is something else I bet you thought you couldn’t do.  One divided by positive zero equals positive infinity, while one divided by negative zero equals negative infinity!

What the history of mathematics indicates is that the world is not converging toward one, true system of mathematics, but creating multiple, incompatible systems of mathematics, each of which has its own logic. If you think of mathematics as a set of tools for understanding reality, rather than reality itself, this makes sense. You want a variety of tools to do different things. Sometimes you need a hammer, sometimes you need a socket wrench, sometimes you need a Phillips screwdriver, etc. The only true test of a tool is how useful it is — a single tool that tried to do everything would be unhelpful.

You probably didn’t know about most of the issues in mathematics I have just mentioned, because they are usually not taught, either at the elementary school level, the high school level, or even college. Mathematics education consists largely of being taught the right way to perform a calculation, and then doing a variety of these calculations over and over and over. . . .

But why is that? Why is mathematics education just about learning to calculate, and not discussing controversies? I can think of several reasons.

One reason may be that most people who go into mathematics tend to have a desire for greater certainty. They don’t like uncertainty and imprecise answers, so they learn math, avoid mathematical controversies or ignore them, and then teach students a mathematics without uncertainty. I recall my college mathematics instructor declaring to class one day that she went into mathematics precisely because it offered sure answers. My teacher certainly had that much in common with Bertrand Russell (quoted above).

Another reason surely is that there is a large element of indoctrination in education generally, and airing mathematical controversies among students might have the effect of undermining authority. It is true that students can discuss controversies in the social sciences and humanities, but that’s because we live in a democratic society in which there are a variety of views on social issues, and no one group has the power to impose a single view on the classroom. But even a democratic society is not interested in teaching controversies in mathematics — it’s interested in creating good workers for the economy. We need people who can make change, draw up a budget, and measure things, not people who challenge widely-accepted beliefs.

This utilitarian view of mathematics education seems to be universal, shared by democratic and totalitarian governments alike. Forcing students to perform endless calculations without allowing them to ask “why” is a great way to bore children and make them hate math, but at least they’ll be obedient citizens.

The Role of Imagination in Science, Part 3

In previous posts (here and here), I argued that mathematics was a product of the human imagination, and that the test of mathematical creations was not how real they were but how useful or valuable they were.

Recently, Russian mathematician Edward Frenkel, in an interview in the Economist magazine, argued the contrary case.  According to Frenkel,

[M]athematical concepts and ideas exist objectively, outside of the physical world and outside of the world of consciousness.  We mathematicians discover them and are able to connect to this hidden reality through our consciousness.  If Leo Tolstoy had not lived we would never had known Anna Karenina.  There is no reason to believe that another author would have written that same novel.  However, if Pythagoras had not lived, someone else would have discovered exactly the same Pythagoras theorem.

Dr. Frenkel goes on to note that mathematical concepts don’t always match to physical reality — Euclidean geometry represents an idealized three-dimensional flat space, whereas our actual universe has curved space.  Nevertheless, mathematical concepts must have an objective reality because “these concepts transcend any specific individual.”

One problem with this argument is the implicit assumption that the human imagination is wholly individualistic and arbitrary, and that if multiple people come up with the same idea, this must demonstrate that the idea exists objectively outside the human mind.  I don’t think this assumption is valid.  It’s perfectly possible for the same idea to be invented by multiple people independently.  Surely if Thomas Edison never lived, someone else would have invented the light bulb.   Does that mean that the light bulb is not a true creation of the imagination, that it was not invented but always existed “objectively” before Edison came along and “discovered” it?  I don’t think so.  Likewise with modern modes of ground transportation, air transportation, manufacturing technology, etc.  They’re all apt to be imagined and invented by multiple people working independently; it’s just that laws on copyright and patent only recognize the first person to file.

It’s true that in other fields of human knowledge, such as literature, one is more likely to find creations that are truly unique.  Yes, Anna Karenina is not likely to be written by someone else in the absence of Tolstoy.  However, even in literature, there are themes that are universal; character names and specific plot developments may vary, but many stories are variations on the same theme.  Consider the following story: two characters from different social groups meet and fall in love; the two social groups are antagonistic toward each other and would disapprove of the love; the two lovers meet secretly, but are eventually discovered; one or both lovers die tragically.  Is this not the basic plot of multiple stories, plays, operas, and musicals going back two thousand years?

Dr. Frenkel does admit that not all mathematical concepts correspond to physical reality.  But if there is not a correspondence to something in physical reality, what does it mean to say that a mathematical concept exists objectively?  How do we prove something exists objectively if it is not in physical reality?

If one looks at the history of mathematics, there is an intriguing pattern in which the earliest mathematical symbols do indeed seem to point to or correspond to objects in physical reality; but as time went on and mathematics advanced, mathematical concepts became more and more creative and distant from physical reality.  These later mathematical concepts were controversial among mathematicians at first, but later became widely adopted, not because someone proved they existed, but because the concepts seemed to be useful in solving problems that could not be solved any other way.

The earliest mathematical concepts were the “natural numbers,” the numbers we use for counting (1, 2, 3 . . .).  Simple operations were derived from these natural numbers.  If I have two apples and add three apples, I end up with five apples.  However, the number zero was initially controversial — how can nothing be represented by something?  The ancient Greeks and Romans, for all of their impressive accomplishments, did not use zero, and the number zero was not adopted in Europe until the Middle Ages.

Negative numbers were also controversial at first.  How can one have “negative two apples” or a negative quantity of anything?  However, it became clear that negative numbers were indeed useful conceptually.  If I have zero apples and borrow two apples from a neighbor, according to my mental accounting book, I do indeed have “negative two apples,” because I owe two apples to my neighbor.  It is an accounting fiction, but it is a useful and valuable fiction.  Negative numbers were invented in ancient China and India, but were rejected by Western mathematicians and were not widely accepted in the West until the eighteenth century.

The set of numbers known explicitly as “imaginary numbers” was even more controversial, since it involved a quantity which, when squared, results in a negative number.  Since there is no known number that allows such an operation, the imaginary numbers were initially derided.  However, imaginary numbers proved to be such a useful conceptual tool in solving certain problems, they gradually became accepted.   Imaginary numbers have been used to solve problems in electric current, quantum physics, and envisioning rotations in three dimensions.

Professor Stephen Hawking has used imaginary numbers in his own work on understanding the origins of the universe, employing “imaginary time” in order to explore what it might be like for the universe to be finite in time and yet have no real boundary or “beginning.”  The potential value of such a theory in explaining the origins of the universe leads Professor Hawking to state the following:

This might suggest that the so-called imaginary time is really the real time, and that what we call real time is just a figment of our imaginations.  In real time, the universe has a beginning and an end at singularities that form a boundary to space-time and at which the laws of science break down.  But in imaginary time, there are no singularities or boundaries.  So maybe what we call imaginary time is really more basic, and what we call real is just an idea that we invent to help us describe what we think the universe is like.  But according to the approach I described in Chapter 1, a scientific theory is just a mathematical model we make to describe our observations: it exists only in our minds.  So it is meaningless to ask: which is real, “real” or “imaginary” time?  It is simply a matter of which is the more useful description.  (A Brief History of Time, p. 144.)

If you have trouble understanding this passage, you are not alone.  I have a hard enough time understanding imaginary numbers, let alone imaginary time.  The main point that I wish to underline is that even the best theoretical physicists don’t bother trying to prove that their conceptual tools are objectively real; the only test of a conceptual tool is if it is useful.

As a final example, let us consider one of the most intriguing of imaginary mathematical objects, the “hypercube.”  A hypercube is a cube that extends into additional dimensions, beyond the three spatial dimensions of an ordinary cube.  (Time is usually referred to as the “fourth dimension,” but in this case we are dealing strictly with spatial dimensions.)  A hypercube can be imagined in four dimensions, five dimensions, eight dimensions, twelve dimensions — in fact, there is no limit to the number of dimensions a hypercube can have, though the hypercube gets increasingly complex and eventually impossible to visualize as the number of dimensions increases.

Does a hypercube correspond to anything in physical reality?  Probably not.  While there are theories in physics that posit five, eight, ten, or even twenty-six spatial dimensions, these theories also posit that the additional spatial dimensions beyond our third dimension are curved up in very, very small spaces.  How small?  A million million million million millionth of an inch, according to Stephen Hawking (A Brief History of Time, p. 179).  So as a practical matter, hypercubes could exist only on the most minute scale.  And that’s probably a good thing, as Stephen Hawking points out, because in a universe with four fully-sized spatial dimensions, gravitational forces would become so sensitive to minor disturbances that planetary systems, stars, and even atoms would fly apart or collapse (pp. 180-81).

Dr. Frenkel would admit that hypercubes may not correspond to anything in physical reality.  So how do hypercubes exist?  Note that there is no limit to how many dimensions a hypercube can have.  Does it make sense to say that the hypercube consisting of exactly 32,458 dimensions exists objectively out there somewhere, waiting for someone to discover it?   Or does it make more sense to argue that the hypercube is an invention of the human imagination, and can have as many dimensions as can be imagined?  I’m inclined to the latter view.

Many scientists insist that mathematical objects must exist out there somewhere because they’ve been taught that a good scientist must be objective and dedicate him or herself to the discovery of things that exist independently of the human mind.  But there’re too many mathematical ideas that are clearly products of the human mind, and they’re too useful to abandon merely because they are products of the mind.

The Role of Imagination in Science, Part 2

In a previous posting, we examined the status of mathematical objects as creations of the human mind, not objectively existing entities.  We also discussed the fact that the science of geometry has expanded from a single system to a great many systems, with no single system being true.  So what prevents mathematics from falling into nihilism?

Many people seem to assume that if something is labeled as “imaginary,” it is essentially arbitrary or of no consequence, because it is not real.  If something is a “figment of imagination” or “exists only in your mind,” then it is of no value to scientific knowledge.  However, two considerations impose limits or restrictions on imagination that prevent descent into nihilism.

The first consideration is that even imaginary objects have properties that are real or unavoidable, once they are proposed.  In The Mathematical Experience, mathematics professors Philip J. Davis and Reuben Hersh argue that mathematics is the study of “true facts about imaginary objects.”  This may be a difficult concept to grasp (it took me a long time to grasp it), but consider some simple examples:

Imagine a circle in your mind.  Got that?  Now imagine a circle in which the radius of the circle is greater than the circumference of the circle.  If you are imagining correctly, it can’t be done.  Whether or not you know that the circumference of a circle is equal to twice the radius times pi, you should know that the circumference of a circle is always going to be larger than the radius.

Now imagine a right triangle.  Can you imagine a right triangle with a hypotenuse that is shorter than either of the two other sides?  No, whether or not you know the Pythagorean theorem, it’s in the very nature of a right triangle to have a hypotenuse that is longer than either of the two remaining sides.  This is what we mean by “true facts about imaginary objects.”  Once you specify an imagined object with certain basic properties, other properties follow inevitably from those initial, basic properties.

The second consideration that puts restrictions on the imagination is this: while it may be possible to invent an infinite number of mathematical objects, only a limited number of those objects is going to be of value.  What makes a mathematical object of value?  In fact, there are multiple criteria for valuing mathematical objects, some of which may conflict with each other.

The most important criterion of mathematical objects according to scientists is the ability to predict real-world phenomena.  Does a particular equation or model allow us to predict the motion of stars and planets; or the multiplication of life forms; or the growth of a national economy?  This ability to predict is a most powerful attribute of mathematics — without it, it is not likely that scientists would bother using mathematics at all.

Does the ability to predict real-world phenomena demonstrate that at least some mathematical objects, however imaginary, at least correspond to or model reality?  Yes — and no.  For in most cases it is possible to choose from a number of different mathematical models that are approximately equal in their ability to predict, and we are still compelled to refer to other criteria in choosing which mathematical object to use.  In fact, there are often tradeoffs when evaluating various criteria — often, so single mathematical object is best on all criteria.

One of the most important criteria after predictive ability is simplicity.  Although it has been demonstrated that Euclidean geometry is not the only type of geometry, it is still widely used because it is the simplest.  In general, scientists like to begin with the simplest model first; if that model becomes inadequate in predicting real-world events, they modify the model or choose a new one.  There is no point in starting with an unnecessarily complex geometry, and when one’s model gets too complex, the chance of error increases significantly.  In fact, simplicity is regarded as an important aspect of mathematical beauty — a mathematical proof that is excessively long and complicated is considered ugly, while a simple proof that provides answers with few steps is beautiful.

Another criterion for choosing one mathematical object over another is scope or comprehensiveness.  Does the mathematical object apply only in limited, specific circumstances?  Or does it apply broadly to phenomena, tying together multiple events under a single model?

There is also the criterion of fruitfulness.  Is the model going to provide many new research findings?  Or is it going to be limited to answering one or two questions, providing no basis for additional progress?

Ultimately, it’s impossible to get away from value judgments when evaluating mathematical objects.  Correspondence to reality cannot be the only value.  Why do we use the Hindu-Arabic numeral system today and not the Roman numeral system?  I don’t think it makes sense to say that the Hindu-Arabic system corresponds to reality more accurately than the Roman numeral system.  Rather, the Hindu-Arabic numeral system is easier to use for many calculations, and it is more powerful in obtaining useful results.  Likewise a base 10 numeral system doesn’t correspond to reality more accurately than a base 2 numeral system — it’s just easier for humans to use a base 10 system.  For computers, it is easier to use a base 2 system.  A base 60 system, such as the ancient Babylonians used, is more difficult for many calculations than a base 10, but it is more useful in measuring time and angles.  Why?  Because 60 has so many divisors (1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 60) it can express fractions of units more simply, which is why we continue to use a modified version of base 60 for measuring time and angles (and geographic coordinates) to this day.

What about mathematical objects that don’t predict real world events or appear to model anything in reality at all?  This is the realm of pure mathematics, and some mathematicians prefer this realm to the realm of applied mathematics.  Do we make fun of pure mathematicians for wasting time on purely imaginary objects?  No, pure mathematics is still a form of knowledge, and mathematicians still seek beauty in mathematics.

Ultimately, imaginative knowledge is not arbitrary or inconsequential; there are real limits even for the imagination.  There may be an infinite number of mathematical systems that can be imagined, but only a limited number will be good.  Likewise, there is an infinite variety of musical compositions, paintings, and novels that can be created by the imagination, but only a limited number will be good, and only a very small number will be truly superb.  So even the imagination has standards, and these standards apply as much to the sciences as to the arts.