Hobbes: “A new decade is coming up.”

Calvin: “Yeah, big deal! Hmph. Where are the flying cars? Where are the moon colonies? Where are the personal robots and the zero-gravity boots, huh? You call this a new decade?! You call this the future?? HA! Where are the rocket packs? Where are the disintegration rays? Where are the floating cities?”

Calvin and Hobbes

by Bill Watterson

December 30, 1989

Indeed, where are the flying cars, the intelligent robots, the bases on the Moon, the atomic powered lawnmowers? Forty years ago futurists confidently predicted all of these things in our time. Some of these predictions were undoubtedly unjustified hype. Nonetheless, the predictions were more reasonable than may now seem the case. Power and propulsion technologies experienced dramatic progress for almost two centuries from the introduction of the separate condenser steam engine in the 1770’s until the manned landing on the Moon in 1969. This progress included both steady evolutionary progress and several technological revolutions: the separate condenser steam engines, the high pressure expansive steam engines, internal combustion engines, the electric battery, electric generators and motors, the diesel engine, jet engines, rocket engines, and atomic power. Progress in power and propulsion technology has been very limited over the last forty years.

So too, progress in many other areas ranging from artificial intelligence to fundamental physics has been quite limited. This has made electronics and computers highly visible with society pinning its hopes for a better future on these fields, in a way that was not the case with comparable advances in the past — radio and television for example — that were overshadowed by dramatic advances in power, propulsion, and other technologies. Probably a number of factors, including just plain bad luck in some cases, have contributed to the limited scientific and technical progress of the last forty years. This article will focus on excessive reliance on symbolic manipulation in place of the conceptual reasoning and conceptual leaps usually involved in major scientific or technological breakthroughs.

**What is symbolic manipulation?**

The ancient Greeks knew mathematics as arithmetic and geometry. Arithmetic consisted of addition, subtraction, multiplication, and division of numbers. The Greeks had a crude number system similar to the Roman Number system familiar to most students today (I = 1, V = 5, X = 10, L = 50, C = 100, etc.). Geometry consisted of words and pictures, verbal proofs, derivation, or calculations, and diagrams constructed with a compass and ruler. When it is said the Greeks could find the roots of certain quadratic equations, this meant performing visual geometric operations with a compass and ruler. When the Greeks referred to the square of a number, they meant a literal square drawn on a piece of paper. This style of mathematics is found in the works of Euclid, Archimedes, and Apollonius of Perga. It became the standard in Europe for over fifteen hundred years. Johannes Kepler wrote his book *New Astronomy*, published in 1609, detailing the discovery of the elliptical orbit of Mars and the precise mathematical law of planetary motion along the ellipse in this style. Isaac Newton used a similar style in his famous * Principia * published in 1687.

In about 1600, the French mathematician Francois Viete introduced the concept of using letters to represent arbitrary numbers, what we now call *variables*. Viete’s system was crude by modern standards. He used only vowels to represent variables and consonants for constants. It took several generations for symbolic mathematics to mature and replace the traditional Greek style of words and pictures. Mathematics and physics have moved in the direction of increasing reliance on symbolic mathematics and abstraction since then.

Most people encounter symbolic mathematics in high school algebra. For example, what is [tex] x [/tex] in:

[tex] x $+$ 3 = 5 [/tex]

This can be solved easily by subtracting the number three (3) from both sides of the equation.

[tex] x = 5 $-$ 3 = 2 [/tex]

With symbols, this solution can be generalized to an entire class of problems:

[tex]x $+$ a = b [/tex]

Then, subtract [tex] a [/tex] from both sides of the equation.

[tex]x = b $-$ a [/tex]

This is a general solution for all equations of the form [tex] x + a = b [/tex]. Up to a point, this general solution will be true independent of the interpretation or meaning of the symbols [tex]x[/tex], [tex]a[/tex], and [tex]b[/tex]. This is part of the power and appeal of symbolic mathematics. It is also one of the pitfalls of symbolic mathematics. In physics or engineering, what if the phenomema represented by [tex]x[/tex], [tex]a[/tex], and [tex]b[/tex] don’t actually have the properties of numbers? In this computer age, another important aspect of symbolic formulas, equations, and solutions is that they can be programmed on a computer. Essentially all computer programming languages have been designed to incorporate variables and at least simple symbolic mathematics involving addition, subtraction, multiplication, and division. Some programming languages and environments such as Matlab and Mathematica can handle quite advanced symbolic mathematics. Some programming languages and environments such as Mathematica and Maxima (formerly known as MACSYMA) can perform certain types of symbolic manipulations, automatic theorem proving, and other exotic functions.

The example above is very simple. Symbolic mathematics can be much more complex and solve much more difficult problems. A more difficult problem that took many centuries to find a solution is the general solution for the quadratic equation. This is usually taught in high school algebra. It is actually rare in everyday life, but occasionally surfaces as a real problem in computer graphics. This is an example of a quadratic equation:

[tex] x^2 $-$ 2x + 1 = 0 [/tex]

This equation has a simple solution. It can be factored into two terms:

[tex] (x $-$ 1)(x $-$ 1) = 0 [/tex]

The solution is [tex] x = 1 [/tex]. In general, the square of [tex] x + a [/tex] is:

[tex] (x + a)(x + a) = x^2 + 2a + a^2 [/tex]

With some thought, this actually gives us a way to solve the general quadratic equation:

[tex] ax^2 + bx + c = 0 [/tex]

where [tex]a[/tex], [tex]b[/tex], and [tex]c[/tex] can be any three numbers. In a nutshell, we want to reorganize the equation so it is in the form:

[tex] (x+e)^2 = f [/tex]

This is known as “completing the square”. First, divide both sides of the general quadratic equation by [tex]a[/tex]:

[tex] x^2 + \frac{b}{a}x + \frac{c}{a} = 0 [/tex]

To turn the left hand side into [tex](x+e)^2[/tex], [tex]\frac{b}{a}[/tex] must be [tex]2e[/tex]. So, subtract [tex]\frac{c}{a}[/tex] from both sides of the equation:

[tex] x^2 + \frac{b}{a}x = -\frac{c}{a} [/tex]

Then, add [tex] (\frac{b}{2a})^2 [/tex] to both sides of the equation.

[tex] x^2 + \frac{b}{a}x + (\frac{b}{2a})^2 = -\frac{c}{a} + (\frac{b}{2a})^2 [/tex]

Now, the equation is in the form [tex] (x+e)^2 = f [/tex] where [tex] e = \frac{b}{2a} [/tex] and [tex] f = -\frac{c}{a} + (\frac{b}{2a})^2 [/tex]. Therefore:

[tex] x + \frac{b}{2a} = \sqrt{ -\frac{c}{a} + (\frac{b}{2a})^2 } [/tex]

Then, subtract [tex] \frac{b}{2a} [/tex] from both sides of the equation and remove the factor [tex] \frac{1}{2a} [/tex] outside the square root sign to get the formula for [tex] x [/tex].

[tex]x = \frac{-b\pm\sqrt{b^2-4ac}}{2a}\hspace{2cm}a\neq0[/tex]

This is the general solution for the quadratic equation in symbolic form. Once again, up to a point, this formula is correct regardless of whether [tex]a[/tex], [tex]b[/tex], [tex]c[/tex], and [tex]x[/tex] refer to distances on a surveyor’s map, distances in a blueprint of a building, a machine, or an electromagnetic field. The answer seems to be found in a precise, step-by-step logical procedure seemingly devoid of unpredictable and sometimes wrong intuition.

Most high school algebra textbooks stop at this point and mention the existence of general solutions to the cubic and quartic equations:

[tex] ax^3 + bx^2 + cx + d = 0 [/tex] (Cubic Equation)

and

[tex] ax^4 + bx^3 + cx^2 + dx + e = 0 [/tex] (Quartic Equation)

This practice in high school algebra textbooks is somewhat misleading and probably contributes to magical thinking about the power of symbolic manipulation. The solution to the quadratic equation by symbolic manipulation involves only about a dozen steps which are typically spelled out step by step (as above) in a high school algebra textbook. As mathematical problems become more complex, the possible symbolic manipulations grow, often exponentially, in number. A successful solution to a problem in higher mathematics may involve hundreds, thousands, or even more steps. There are many possible rearrangements of the symbolic formulas that lead nowhere, much as there are many choices in a game of chess that lead only to defeat. The number of possible sequences of substitutions and other symbolic manipulations grows beyond the limited ability of human beings to exhaustively try each sequence and often beyond the capacity of a computer program, as powerful as computers have become, to exhaustively try each possible sequence. Thus, the mysterious human “intuition” reappears. Mathematicians develop a sense of the sequence of steps that is likely to result in a useful answer. Even so, they are often wrong and have to make repeated attempts — trial and error — to find a useful result, but many less trials than an exhaustive brute force search.

**The Status of Symbolic Manipulation**

Symbolic mathematics and symbolic manipulation have acquired great status and a reputation bordering on magic both in general society and much of the scientific, engineering, and mathematical community. Not without good reason. Symbolic manipulation has solved many problems and undoubtedly will continue to do so. Nonetheless, it has substantial limitations relative to human abilities and judgement, our conceptual reasoning abilities as will be discussed further below.

Probably the epitome of the mystique of symbolic mathematics and manipulation in general culture is the famous equation:

[tex] e = m c^2 [/tex]

This cryptic equation became closely associated with Albert Einstein and the atomic bomb. In the equation, [tex]e[/tex] represents energy, [tex]m[/tex] represents mass, and [tex]c[/tex] represents the speed of light. In the Special Theory of Relativity one can derive this equation to show an equivalence between mass and energy. The atomic bomb derives its enormous destructive power from the conversion of a fraction of a percent of its mass into energy. A bomb that converted all of its mass into energy would be hundreds of times more powerful than the most powerful thermonuclear weapons in the arsenals of the leading nuclear powers.

The general press, popular science, textbooks and many other sources frequently display pictures of scientists, engineers, and mathematicians standing in front of blackboards covered with cryptic symbols, reinforcing the message that the symbolic mathematics and manipulations is the essence of science, engineering, and mathematics.

The nineteenth and twentieth century saw the steady “professionalization” of science and engineering. This accelerated during and after World War II which saw a huge expansion in the size, the scope, and the level of public funding of scientific research and technological invention, much of it associated with military research and development. Hundreds of years ago, science and engineering, invention and discovery, were conducted by a hodge podge of often wealthy amateurs, craftsmen such as clockmakers, blacksmiths, bicycle makers (the Wright Brothers), and university professors who often relied on teaching rather than research grants for their income. Prizes for inventions and discoveries, in principle (if not always in practice) open to anyone, rather than proposal-driven grants and R&D contracts to specific researchers or research groups were much more common. This changed gradually over the nineteenth and early twentieth century. Germany, in particular, witnessed the rise of a professional civil-service of university professors conducting basic and applied scientific research. Many of the German customs were deliberately copied in the United States or imported with the huge wave of refugees from Nazi Germany.

The professionalization of science and engineering was accompanied by an increasing emphasis on the importance of mathematics and symbolic mathematics. In the ideal, the professional scientist or engineer would conduct sober logical scientific research, perform sophisticated calculations or derivations correctly the first time through rigorous professional training, and produce scientific and technical advances as needed, avoiding the messy intuition and time consuming, unpredictable trial and error of the mere “inventor” or “tinkerer.” The reality, of course, never matched this ideal, but the enormous success of wartime projects such as the Manhattan Project, the German V-2 rocket program, and lesser known successes such as the Jet Assisted Take Off (JATO) rockets in the United States (all these projects led by German or German-trained scientists and engineers — J. Robert Oppenheimer with a Ph.D. from the University of Gottingen, Werner Von Braun, and Theodor Von Karman) contributed to the credibility of this picture of the central role of symbolic mathematics, and symbolic manipulation in particular, in the post-World War II professional science and engineering. According to the semi-official history of the Manhattan Project, the atomic bomb succeeded on the first test, a remarkable achievement in the history of invention where repeated failures and repeated trials have been the norm, based in part on purely theoretical calculations by Hans Bethe, J. Robert Oppenheimer, and other theoretical physicists, giving great credibility to mathematically driven science and engineering.

The primacy of symbolic manipulation offered a comforting world in which the scientist, engineer, or mathematician would be an intellectual plumber, assigned to solve a specific narrow problem in a specific time without trial and error and without having to deal with any “big picture” issues, “above his pay grade” in government-speak. Policy makers, business leaders, and the generals who dominated the vast military R&D apparatus of the post World War II world could deal with the important issues while assigning the solution of a specific problem to a narrow technical expert. Conceptual analysis is much more likely than symbolic manipulation to raise uncomfortable and sometimes explosive issues. In his famous book *New Astronomy*, Johannes Kepler, a devout Lutheran, devoted much of the introduction to arguing that his discovery of the elliptical orbit of Mars did not in fact conflict with the Bible, which seems to state that the Earth is stationary and the Sun moves around the Earth in a few places (such as the Book of Joshua when God miraculously stops the Sun in its path). Indeed, the wiser contemporaries of Galileo and Kepler advised both men to present their discoveries as mere mathematical conveniences for computing the positions of the planets and moons. In this way, the big picture and potentially explosive issues (the Bible is wrong) could be avoided. By its nature conceptual reasoning can raise uncomfortable issues and raise those issues in ordinary words and pictures that are easily understood by non-experts, unlike abstract symbols. Galileo, the epitome of the vain and arrogant scientist, blindly ignored the advice and got into serious trouble. Kepler, not without his faults but far more conciliatory in style than Galileo, avoided a similar fiasco (he was excommunicated by the Lutheran Church — a much less serious matter than a trial by the Inquisition — over an unrelated matter of religious doctrine).

Nor is the sensitive nature of conceptual analysis a problem restricted to Galileo, Kepler, and the Renaissance. The logical and philosophical problems with quantum mechanics that puzzled Albert Einstein, Erwin Schrödinger, and Prince Louis de Broglie ultimately led Einstein to deduce the non-local nature of quantum mechanics, something that appears to have been confirmed in numerous experiments over the last several decades. This quantum entanglement in which widely separated objects maintain some sort of mysterious instantaneous connection — certainly faster than light — raises many curious issues and has been embraced by mystics, parapsychologists and others as empirical evidence of the oneness of all things claimed in most mystical systems. Quantum mechanics as an abstract algorithm expressed in symbolic mathematics raises none of these issues — or, at least, they are well disguised.

**Conceptual Reasoning and Analysis**

Curiously, most people never use symbolic mathematics and symbolic manipulation. They encounter it in high school algebra and often never again. For the most part, human beings reason with concepts, with mental pictures, and only very rarely with explicit numbers, mathematical symbols, symbolic formulas, or symbolic manipulation. After decades of mostly failed research into artificial intelligence, we have some idea why this is. For certain classes of geometric objects such as circles, spheres, cubes, and so forth, there are exact symbolic formulas.

[tex] (x-a)^2 + (y-b)^2 = r^2 [/tex] (The Circle)

This is the formula for a circle with arbitrary location and size. The radius of the circle is [tex]r[/tex]. The location of the center of the circle is [tex](a,b)[/tex]. We can do the same with spheres:

[tex] (x-a)^2 + (y-b)^2 + (z-c)^2 = r^2 [/tex] (The Sphere)

This is a sphere with radius [tex]r[/tex] centered at [tex](a,b,c)[/tex] in three dimensions.

So far, so good. What is the symbolic formula for the shape of a cat? A tree? A car? A chair? The letter “A”? A door handle? The issue is not the symbolic formula for a single instance of a cat, a tree, and so forth. Movies are now full of specific computer generated cats, trees, cars, and so forth. But what is the symbolic formula for the class of cats, the class of trees, the class of cars, etc.? How do human beings look at cats — big cats, small cats, thin cats, fat cats, brown cats, orange cats, cats dyed purple — and recognize that each specific cat is an instance of the class of cats, the concept of a cat? We have no idea. Object oriented programming such as found in Java, C#, C++ and many other programming languages was inspired in part by this problem, but object oriented programs still cannot think conceptually like human beings. Various highly mathematical artificial intelligence methods such as artificial neural networks were developed to solve the same problem, but so far they have not succeeded.

For most everyday shapes, objects, and phenomena, we do not know a symbolic mathematical formula, unlike simple geometrical shapes like the circle or sphere. Somehow, through conceptual reasoning skills and “intuition” human beings can reason successfully about the many shapes, objects, and phenomena they encounter every day without anything clearly identifiable as symbolic or numerical mathematics. This can be very precise. Human beings have little difficulty picking up and manipulating quite complex shapes. Computers and robots, even using the most advanced mathematics that we know, often cannot do the same. This is why many common tasks such as clerks at convenience stores or assembly tasks in factories are still performed by human beings. This is a very precise ability, as if the human mind was subconsciously computing the precise coordinates of surfaces. There is little conscious awareness of this if some sort of mathematics is in use as it probably is.

Most major advances in power and propulsion have involved at least one conceptual leap, usually a new architecture — a new component or a radically new system. In many cases, these are new shapes and/or combinations of materials that work in a substantially different way from past systems. Since in most cases, the new shapes or structures cannot be represented as a symbolic formula, it is difficult to see how any known symbolic manipulation could find the improvement. In most cases, it was a mental leap — so-called intuition. One can often simulate a specific instance of a new architecture or design with modern mathematics and computers. Some critical equations such as the Navier-Stokes equations remain difficult or impossible to simulate numerically or solve exactly through symbolic methods. Neglecting the problems with solving or numerically simulating equations like the Navier-Stokes equation, it remains the case that there is no way to express the general shapes or concepts as symbolic formulas, although one can often express a specific instance as a computer or symbolic model. Consequently, both symbolic manipulation and computer simulations are simply unable to find a new shape, structure, or system for power or propulsion or many other forms of mechanical invention. They can be helpful in verifying a concept once found, determining that it is likely to succeed.

In fundamental physics, most major advances consisted of a conceptual leap, often expressed in words or pictures, followed by the development of a mathematical formula that implemented the new concept. Kepler realized that the orbit of Mars was something like an ellipse. He was fortunate that the ellipse was a simple geometric form that had been studied successfully by Apollonius of Perga over a thousand years before. He was basically able to look up the mathematics that matched his mental concept. In probably the most extreme example of the separation of concepts and mathematics, Michael Faraday constructed a mental picture of the electrical and magnetic fields (as pressures and circulating vortices in the hypothetical luminiferous ether) entirely without mathematics. William Thomson (later Lord Kelvin) and then James Clerk Maxwell, Thomson’s student, were able to translate the pictures into mathematical language, eventually Maxwell’s Equations. In the case of Faraday and Maxwell, it was not a simple matter of looking up the mathematics in an old book. Rather, Maxwell had to construct mechanical models of the hypothetical ether, assign values to special or representative points in the mechanical model, and then infer the differential equations that comprise Maxwell’s Equations. Einstein developed the concept that gravity was due to mass and energy warping space-time well before he was able, after several failures, to find a specific equation that implemented his concept and appeared to match certain observational measurements (notably the precession of Mercury). Schrödinger started from Louis de Broglie’s concept that the electron had a wave associated with it and tried for some time to find a wave equation that matched the spectrum of hydrogen. There are many possible wave equations and Schrödinger, like Kepler before him, was fortunate that a simple, easy to guess formula could be found.

If one tries to perform fundamental physics purely by symbolic manipulation without any mental visualization or conceptual analysis, one has very little to work with. One must basically guess symbolic formulas, often by randomly adding terms to known formulas and expressions. This is very easy to do with the Lagrangian formalism. The literature of theoretical physics in the last forty or fifty years is filled with guesses at Lagrangians for various forces and phenomena and especially the theory of everything (TOE). Historically, physicists used concepts to narrow down the number and range of symbolic formulas to try. There was still a lot of trial and error but often the conceptual analysis, the “intuition,” seems to have been effective.

**Conclusion**

Symbolic mathematics and symbolic manipulation are very powerful in some cases. They are not a panacea. In fact, given the current state of mathematical knowledge, they have great limitations. Most people have, use, and rely for their survival on conceptual reasoning abilities every day that we don’t know how to express in symbolic mathematical form or program on a computer, if this is even possible. To make major progress, even in highly mathematical areas like pure mathematics or theoretical physics or power systems, we probably need to make heavy use of conceptual analysis and visualization just as the successful scientists and engineers of the past did.

Copyright © 2010 John F. McGowan, Ph.D.

**About the Author**

John F. McGowan, Ph.D. is a software developer, research scientist, and consultant. He works primarily in the area of complex algorithms that embody advanced mathematical and logical concepts, including speech recognition and video compression technologies. He has extensive experience developing software in C, C++, Visual Basic, Mathematica, MATLAB, and many other programming languages. He is probably best known for his AVI Overview, an Internet FAQ (Frequently Asked Questions) on the Microsoft AVI (Audio Video Interleave) file format. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech). He can be reached at [email protected].

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Pedantry:

(x + a)(x + a) = x^2 + 2a + a^2

and not

(x + a)(x + a) = x^2 + 2a + 1

🙂

I believe this is the mathematical equivalent of a typo. 🙂 Fixed.

I like the article, but I have a problem with you singling out symbolic treatment. I feel the problem derives from a more fundamental root: “being clever.”

Often we are told to be “clever” in school: it is frustrating because the panacea of tricks we learn and have been taught forever can handle only specific scenarios. An explicit example is Gauss’ law. It is widely used (and even celebrated) as it allows one to handle oddly-shaped objects where they otherwise could not be (easily.) Another is General relativity, which tells us that all (on-shell) masses behave identically with regard to space-time.

The transition from school, where one learns generic solutions to generic problems, to real-world or research, where one has a specific problem and the prospect of a general solution is bleak, is too much to handle for most. They shirk their grade- and high-school problem solving knowledge and never return to it. If they do, they do so in a more “human” way: using the methods you describe above.

But, as I believe, the problem is not symbolic computation itself – it is simply that there is no better way: people are content to abandon ship or move on to greener pastures.

As far as I can see, there is no better way. The current method has produced or optimized (most likely) nearly everything you have used today. Toothpaste was probably an intuition before it was a product. Modern computers were a brainchild before they existed. Vehicles are undergoing a small revolution because people are thinking about alternative fuel types.

Do you, Antonio, have an idea for a solution to this problem?

The fact that this `problem’ has no immediate, algorithmic solution shows that cleverness IS necessary for advancement. It’s not something that should be shied away from. It should be embraced. It’s what separates us humans from the computer.

I should add to my above argument.

Humans have created some wonderful things. We know a lot about physics, chemistry, biology, engineering, economics, psychology, warfare, computation, …

The slowdown in modern times is, I would argue, not due to symbolic treatment in school. It is due to the sparsity of topics in which the easy tasks have been completed or are well understood.

Put this in perspective: humans have been to the moon, and we STILL do not have a completely general, closed form solution for roots of arbitrary functions. We STILL only have a handful of elementary functions. We STILL have to fight for funding from society so as to help improve society.

Symbolic manipulation? Who’s worried about that?

Re: closed form solution for roots of arbitrary functions. See Galois theory. We haven’t found one because it’s been proved not to exist.

There exist varfious ideologies in mathematics. Symbolomania is feature of those following reductionism, they reduce everything to natural numbers. Their opposite follow holism and employ visualization as much as possible. The difference is that holists use both visualization and symbolic logic and reductionists claim that nothing but symbols is needed. Presently reductionism is ruling, though combinations of symbols yield only combinations of symbols. All new ideas come from the visual, then undergo formalization and as the result, become property of reductionists. It is much easier to express the formalized in terms of symbols than to explain “what I see”. There exist thing that very few people are able to see this, but ater it is formalized nobody needs to have this ability. Therefore, finally everything is (mis)appropriates by them.

Actually not

(x + a)(x + a) = x^2 + 2a + a^2

but

(x + a)(x + a) = x^2 + 2ax + a^2

🙂

It may be more of a footnote than anything else, but Aristotle had already come up with the idea of symbolic representation not of numbers, but of propositions. The “Prior Analytics” is heavily dependent on this symbolic representation for propositions.

Now it would have been more of a breakthrough if he had thought of doing the same for logical operators, but he didn’t go that far. He used letters just to stand for things like subject and predicate of categorical syllogisms.

Looking back on it now, it looks SO close to the more modern representations, it looks like one of the great ironic accidents of history that he didn’t make that small extension of the notion that would have been such a great leap forwards.

Also, I didn’t see any mention in the article of how the Greeks were also already using symbolic notation for points, lines and sides of polygons and polyhedra. Again, so close and yet so far!

But the Stoics, who were also working in the time of classical Greece, did have a system of propositional logic. As, I believe, was the case in Indian philosophy.

mathematics reduces to this simple definition: It is the tool humans used to solve problems. Forget about whether or not it is symbolic. Just see it as a problem solving tool. There is matter in Space, There is Time associated with matter manifested as its adventure in Space, there are problems associated with these adventures and there is mathematics.

Random thoughts:

– The notion of the generalized idea of a “cat” (instead of a striped alley cat) was addressed by Plato in his discussion of /arete/ (= essential nature). Consider Plato’s example of the people in the cave, looking at a shadow play based on puppets of real people. Plato said that the world of human senses, amounted to the shadows of the images of “real” things, such as catness. We construct our knowledge of such concepts through generalization from specific instances.

– Science doesn’t provide us with ultimate truth. Theories are valuable to the extent that they are useful and help us organize knowledge, but no scientific theory can be proven to be correct. OTOH, some theories known to be fundamentally flawed are still highly useful — e.g., Newton’s laws of motion are completely discredited, yet produced accurate enough predictions to support the engineering needed to reach the Moon and distant planets.

– Some mathematicians pooh-pooh the notion that a computer-generated proof is valid. (I’ve seen characterizations of such proofs as “experiments”, as if the traditional systems using calcium carbonate and metamorphic shale, or graphite and wood fibers, are mechanically purer than transistors and copper wires.) But tools like Mathematica have facilitated major advances in our ability to attack difficult problems. In the early days of computers, reporters often characterized the output of a some computer as equivalent to a human being’s manual calculations that would occupy several decades (= one’s entire career, on one problem). Consider the (manual) numerical integration that went into predicting the time that Halley’s Comet would reappear — a whole team of human computers worked on that one.

– Some questions are not (I expect never will be) amenable to scientific analysis. Jurisprudence, legislation, and theology might at times be informed by science, but the decision on whom to vote for is not really a scientific one.

– Even in mathematics, not all questions are answerable; I was somewhat shaken when I learned of Kurt Gödel’s Incompleteness Theorem (published about 1931). Earlier, Newton’s 3-body problem had bumped up against a fairly hard barrier in the mathematical analysis of apparently simple physical systems.

– Even if we can’t know the answer to everything (though Douglas Adams predicted that that will turn out to equal 42), I think that there is unending challenge and fun in learning to use new tools to clear away brush in what once appeared to be an impenetrable thicket. Newton was happy to have spotted some pretty pebbles on the vast beach of knowledge. I think he’d find some even prettier ones now, and better ways to collect them.

Not to stray to far off topic, the Greeks were not the first to use symbols and most certainly not the best. Do you forget the Egyptians and what they accomplished through their symbology, which most people still don’t grasp. I can help you understand some of the amazing sophistication of the Egyptian use of symbols to model and merge both scientific and philosophical concepts.

For example, the symbols linking Egypt and the Hebrew wisdom materials have long encoded ancient knowledge of the existence of 11 dimensions and how they actually relate to and form our reality.

My book can be downloaded and read for free. It delves into many other topics, but proof of the merger of ancient symbolized wisdom with advanced science and precisely what was being modeled, and why, is a pivotal focus of my research and writing.

The symbology of the ancients was used to redundantly and precisely model things that modern scientists, philosophers, and theologians are still haggling over.

What is the nature of conceptual analysis? Analogies and stories? These are fuzzy areas. Symbolic math and its rules help restrict the solution spaces we explore. There are a million incorrect analogies and stories about how spacetime works. There are even more analogies and stories about how to build new propulsion systems using free zero-point energy and other w00-tech.

Conceptual analysis is of limited utility without something to bring people in out of the weeds.

Also, all kinds of propulsion systems are known but unused, often for non-technical reasons. We could have flying cars, personal helicopters, nuclear-powered space ships, perhaps even flying cities.

It is economic, social and political issues that now constrain these things. There are 40,000 road deaths in the US each year, but at least when you’re home you are relatively safe from things like flying cars falling through the ceiling. We let the government regulate transportation technology to keep things from getting any crazier than they already are.

One reason environmentalists opposed nuclear power was the idea of making electricity “too cheap to meter”. Giving everybody access to arbitrary amounts of power in a liberal society seems dangerous.

Many other limiting factors apply to this field: telephones, the internet and jet airplanes mean we rarely really need to be somewhere, and if we do we can usually be there in a day at most.

I think the assertion that progress or dependence on symbolic mathematics is in some way the cause of the recent technology slowdown in areas like transportation is false.

In the case of Einstein, I might even give the opposite interpretation – special relativity as an idea was waiting for Lorentz transformations. The symbolic math was a necessary language for him to make his concepts concrete.

In terms of what we should be teaching our kids, I agree that few people use the quadratic formula, matrices, or calculus once their schooling is done. I think this misinterprets the purpose of schooling, though. Math grades serve to measure the individual’s ability to work with complex formal systems.

Even on this point they are fairly limited – you wouldn’t be able to tell the difference between Ramanujan and any of 100,000 high school seniors based on their grades in AP Calculus. You can, however, weed out those who are constitutionally incapable of working with complex formal systems (or participating in authoritative work settings without short-term economic incentives). To that end, maybe teaching symbolic math is sufficient.

How does flying car driving relate to the law of Conservation of Energy? Like applied in the eurocopter algorithms of fuel consumption.

~r

Erm… does anyone actually suffer from sybmolomania? As far as I can tell, most working mathematicians, and theoretical physicists, for that matter, write down symbols *after* they’ve figured out what they’re going to do with them, in order to check that their intuitions are correct. That’s certainly how I do it.

Who exactly is this article aimed at?

I LOLed at the lack of “new car smell” comment.