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Models of Projective Geometry

datePosted on 24 July 2012 by cjf

For me the most enticing facet of projective geometry is the profound way in which it treats duality. Duality is the notion that certain fundamental distinctions have similar structure in their complementary forms. In comparing a form with its dual, the basic structure remains even though the roles of the forms reverse. Inside and outside. Convex and concave. Yin and yang. In 2D (two-dimensional) projective geometry, point is dual with line; in 3D point is dual with plane while lines are self-dual. The relationship of duality is so penetrating and pervasive in projective geometry, that we might consider it the geometry of fundamental duality. It provides a geometrical stage upon which duality can be studied in a pure form.

Another profound aspect of projective geometry is its elementary treatment of incidence where one considers the join (∨) and meet or intersection (∧) of two basic geometrical objects such as point, line, plane, and hyperplane. The most fundamental correspondence of geometrical forms associates points and lines in dual arrangement: the points on a line form a range and the lines through a point form a pencil. The correspondence between a pencil and a range is a basic projection. Next a perspective relation joins a pencil with two ranges or a range with two pencils; that is, by combining two elementary projections. Such a perspectivity maps points to points, or dually, lines to lines as shown in the figure. Point and Line Perspective

In the essay “Design Strategy” in Buckminster Fuller’s book Utopia or Oblivion, he includes projective geometry in his list of recommendations for a curriculum of design science. The connection between projective geometry and design thinking is an area that deserves more attention.

A Catalog of Models of Projective Geometry

The rest of this essay tersely describes a broad listing of some of the more basic models of projective geometry. Models are a powerful tool for learning and for understanding as explained in my essay about the Importance of Model Thinking (based on Scott E. Page’s course). The models included below should provide an introduction to and an overview of projective geometry for those new to the subject (Note: some of these models require background knowledge that is not explained here. They are indicated with a Orange Asterisk from http://www.fatcow.com/free-icons licensed under a Creative Commons Attribution 3.0 License. I encourage you to skim or skip such models, but to read on as later models may be more tractable.) I hope the experts will find the succinct summary and references useful. Although this list is fairly comprehensive, there are many models that are necessarily omitted. If you have a favorite model, please post a comment about it.

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The view that randomness impacts and shapes our lives in profound ways has been gaining traction since 2002 when Daniel Kahneman won the Nobel prize in Economics for his work with Amos Tversky in characterizing human weaknesses when facing uncertainty. My thinking on the subject was first awakened by reading Nassim Nicholas Taleb’s book Fooled by Randomness which will give anyone who imagines they can think “rationally” a healthy dose of humble pie. A more helpful discussion can be found in Jonah Lehrer’s How We Decide which The Drunkard's Walk by Leonard Mlodinow pays heed to our brain’s strengths while acknowledging our weaknesses. As I relayed in a post on the brain, mind and thinking, Lehrer recommends thinking about your thinking process to strengthen its decision-making function. Recently I finished reading Leonard Mlodinow’s The Drunkard’s Walk: How Randomness Rules our Lives which provides an accessible, historically detailed, and elementary introduction to the sciences of randomness and uncertainty and shows how they rule our lives.

These books have started to change my thinking about the nature of reality itself: I see now that randomness and uncertainty have an essential role to play. Interestingly, I shunned probability and statistics, the sciences of randomness and uncertainty, in college because I was steeped in Euclid, logic, and Buckminster Fuller’s “generalized principles” in Synergetics. I wanted to design destiny with deliberate application of knowledge … to worship at the altar of scientific determinism. Fortunately, Bucky taught me to “dare to be naïve” so I have been open to the new evidence about randomness. Now I suspect that Bucky and I were a little off about this subtle subject. It isn’t surprising, probability and statistics are among the newer branches of mathematics having developed mostly after the calculus was well established. They have not had enough time to pervade our collective consciousness.

Do you think the world is fundamentally deterministic or random? What influences have shaped your thinking and biases about the subjects of randomness, uncertainty, probability, and statistics? Do you think the increasing focus on the role of randomness and uncertainty in our lives is an important trend?

Randomness Rules Our Lives

Is Mlodinow’s thesis that randomness rules our lives really so convincing? Evidently so. Mlodinow finds dramatic evidence of randomness in our economic lives. He retells the poignant story of Sherry Lansing who led Paramount Pictures to huge successes in seven consecutive phenomenal years. Then after three years of bad results, she left the company. Did Paramount let her go too quickly? Evidently so because the pipline she left behind was full of new hits that restored Paramount’s revenue and market share. Shouldn’t seven years of success earn the right to forgive a few bad years? What if another great leader happened to have their three consecutive bad years at the beginning of their tenure? Do we replace them before their ship comes in? Mlodinow cites many other examples including the fact that “And to Think That I Saw It on Mulberry Street” was rejected by publishers some 27 times before Dr. Seuss’ career launched. Mlodinow also shows that student grades are often random and independent of their skill and knowledge.

Should we insist that our students, our schools, and our business leaders perform, perform, and perform with no “bad” years allowed? Do you believe that performance results are somewhat random? We invest a lot in exam and executive performance. Given the evidence, is that wise?

Venn Diagram of sets A, B, and COne part of Kahneman’s Nobel-prize winning work addressed the conjunction fallacy. Let A, B, and C be statements represented by a colored circle in the venn diagram to the right. The only case in which they can be simultaneously true is in the small area where all three colors overlap. So it is much less likely (less area) for three statements to be simultaneously true than for any one of them to be true. However, when someone weaves a story filled with a lot of concrete details, it seems more vivid and hence more believable than the statements considered separately: that’s the conjunction fallacy. Evidence of people falling for this fallacy has been documented widely even in medicine and the court room. We humans are easily duped by a good story!

It is surprising that the Nobel prize for the work showing how “blind” humans are to the elementary logic of the conjunction fallacy was only awarded one decade ago! Humanity has only just yesterday identified this basic weakness in our cognitive function! Add to the conjunction fallacy the many other fallacies and biases that Taleb, Lehrer, and Mlodinow show us to be subject to and one can see that Emanuel Lasker who was world chess champion for 27 years got it right: “In life we are all duffers”!

What is the significance of our weakness in understanding uncertainty? Do these weaknesses of the human mind subject us to the ravages of randomness? Are they a consequence of an inherent randomness in reality? Or do they simply lead to the appearance of randomness?

Our weakness extends to our sensory organs and perception as well. Mlodinow notes

Human perception … is not a direct consequence of reality but rather an act of imagination. Perception requires imagination because the data people encounter in their lives are never complete and always equivocal.

Mlodinow illustrates the problem by explaining that the human visual system sends “the brain a shaky, badly pixelated picture with a hole in it” (due to the relative weakness of our vision outside the fovea and the blind spot). In addition to conjunction bias, the sharp shooter effect, the hot-hand fallacy, availability bias, confirmation bias, and more, it becomes evident that “When we look closely, we find that many of the assumptions of modern society are based … on shared illusions.” And his conclusion

It is important in our own lives to take the long view and understand that streaks and other patterns that don’t appear random can indeed happen by pure chance. It is also important, when assessing others, to recognize that among a large group of people it would be very odd if one of them didn’t experience a long streak of successes or failures.

What shared illusions do we hold? How often are our lives subject to pure chance events? How important is serendipity? Do you believe that a long series of failures or successes is just the result of luck? When is it luck and when is it skill? How can we tell the difference?

The problem of randomness is deeper still: even machine-enhanced human sensing and measurement are fundamentally random! In Walter Lewin’s excellent video introducing physics and measurement in MIT OCW’s Physics I course, he says “Any measurement that you make without any knowledge of the uncertainty is meaningless.” Understanding uncertainty is at the heart of scientific measurement. No physics experiment ever found an exact match between theory and the laws of nature: data points always appear at random! Then add in effects like Heisenberg’s uncertainty principle and we see that randomness and uncertainty are vital elements of experience: they are pervasive.

In view of the elementary role of uncertainty in our perceptual and physical experience, what can we say about reality? What is reality if experience is so imprecise, fuzzy, uncertain, and fallible?

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Glibert Strang’s 18.06 Linear Algebra course at MIT OpenCourseWare is exquisite! Jeannie and I went through it about a year ago. Strang’s approach to the material and engaging teaching style make the course a joy. Unlike other OER (Open Educational Resources) courses that we have taken, I cannot recommend just watching the videos. Instead one needs to really think about the concepts which is greatly facilitated by doing the exercises: this is typical for mathematics. It took significant effort to master the content. The material builds quickly and it was essential for us to work hard on each lecture. On several occasions we re-watched the videos and had to read the text carefully and collate with other on-line resources (some provided or referenced in the wonderful OpenCourseWare materials but some found by web searching, Wikipedia, etc.). We also used resources from our library (personal and public).

Jeannie and I were able to enjoy the whole course but it definitely took significant effort. This is the only video course that we have thoroughly studied and it was worth it!

Are there any other OER video courses on linear algebra? I have not found any which is a shame for such important material.

Linear algebra is one of the most useful branches of mathematics beyond introductory (high school) algebra and geometry. It is the algebraic study of intersections of complexes of lines, planes and hyperplanes and therefore has a strong geometrical component. Since each coordinate in the Cartesian representation of a line can be thought of as a variable, linear algebra provides a first order or “linear” approximation to multivariable systems. It is therefore a widespread and fundamental tool. Linear algebra has found applications in business, economics, engineering, genetics, computer graphics, social sciences, graph theory and much more. It is essential for anyone wanting to understand advanced mathematics. The matrix or an array of numbers is the basic object of study in linear algebra. So it is sometimes called matrix theory. Vector spaces are the abstract form of linear algebra.

Is that a good characterization of linear algebra? How would you improve it?

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