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Calculus is topology.

The reason is that the matrix of the exterior derivative is equivalent to the transpose of the matrix of the boundary operator. That fact has been known for some time, but its practical consequences have only been understood recently.

[S]uppose you know the boundary of each k-cell in a cell complex in terms of (k−1)-cells, i.e., the boundary operator. Then you also know the exterior derivative of all discrete differential forms (i.e., cochains). So, you know calculus. Smooth or discrete.

Peter Saveliev

(Source: inperc.com)




Climate Statistics

  • httpness: (studying statistics) Can there be a different standard deviation up and down?
  • isomorphisms: Yes. it's called a semideviation. (Or a quasinorm.) There are a lot of people who argue that semideviations and quasinorms are more natural than standard deviation and norms.
  • httpness: So that's not a normal distribution?
  • isomorphisms: Whatever distribution you're using, there are different measures of dispersion on that -- standard deviation, downside risk / semideviation, interquartile range, kurtosis, etc.
  • httpness: I was just thinking about temperatures. The standard deviation changes depending on the time of year, and the chance of unseasonably warm or cold days changes too.
  • httpness: Here's an example of what I mean. let's say during the summer there _is_ a standard deviation and it's the same up and down. But at another time of year there could be more chance of a very warm day, and at a third time of year there could be more chance of an unseasonably cold day.













William Thurston, geometrizer of manifolds

Sometimes I like to spend an hour looking at something I barely understand. The inside of this guy’s mind has got to be so interesting, but it’s been shaped by geometry rather than words, so it’s very hard for him to express it. The geometry shaping it is also quite less limited than the square space we hit baseballs in, so it’s hard to draw as well.

I can offer some help on grokking what he’s saying, but there’s simply no way to absorb this stuff quickly. That said, I wouldn’t mind being able to imagine the platonic forms inside Bill Thurston’s head.

 

GLOSSARY-LIKE DISCUSSION

  1. Topology. You want to understand why identifying the left and right side of a wide rectangle (declaring left = right, so that when you leave the left side of the Mario screen you appear again on the right) is the same as cutting a long strip of paper and taping the two ends together.

    (There’s a slight variation on that game that results in a famously weird space—the one-sided, single-edged Möbius strip).



  2. Quotient spaces. You want to understand what it means to quotient a space. I can give a few examples. ℝ/ℤ would be the unit real interval [0,1) — kind of a microcosm of the real numbers themselves. The Western chromatic musical scale quotients 13 notes into an octave. It’s not that A4 is “equal” to A8, but it shares the same structural relationship as do E4 and E8.



    An orbifold is a manifold that’s been quotiented. Like if you took the plane and made an equivalence class of the vertical [0,1)’s with all the [1,2)’s and [2,3)’s and etc., you would be looking at an infinitely wide strip with all the verticality “wrapped up” in [0,1) — not gone, just wrapped up into one microcosm.

    You could also think about Groundhog Day (the analogy doesn’t work precisely). He’s living through the same span of time over and over because it’s been quotiented along the time dimension (the result of the division is a length of one day)

    Oh … equivalence classes are another thing you have to know about. I haven’t written about them yet. WVO Quine came up with a sensible definition of “what is 2” using the concept. And as Terence Tao wrote, when one uses a “noise-tolerant” definition — like if a lot of different ways of saying something can be taken to mean the same thing — that’s another example of an equivalence class.

    Back to the music theory for a second—there are multiple ways you could set up equivalence classes.
    • octaves — it’s not as if all “G♯” notes sound the same — but when we talk about octaves it’s usually with reference to the same-sounding-ness of twice-as-fast frequencies
    • inversion — I can do a major A triad as AEG, GAE, EGA … it’s a combinatorics thing; 3! ways. No, they don’t all sound the same, but when I use the word “triad” I am equivalence-classing over the kind of sameness that they do have.
    • enharmonics — Sure, D♯♯ and F♭ sound the same — but conceptually they’re very different, and the notes around D♯♯ will be different than the notes around F♭.
    • slight errors — players of the cello or the voice know that pitch is a continuous variable—however we might reasonably call 398 Hz = 400 Hz = A4.
    • transposition — Certainly composers choose the key of D (or, if they’re Stephen Sondheim, F♯) for a reason — but if a song isn’t within your vocal range you can always subtract or add a certain fixed pitch (in notes-space, not in Hz-space!) from every note and the piece will sound “the same” — not exactly the same, but it will recognisably be a pub song — I mean, the US national anthem


    If I say “Hand me that glass”, I don’t mean to reference the glass at a particular orientation, rotation, or place in the room—I mean to equivalence-class ∀ such configurations of the glass—they all mean “that glass”. And if I say “Hand me a glass” — “Which glass? This glass?” — “Any glass!” then I’m equivalence-classing ∀ glasses within a certain distance from you.

     
  3. Hyperbolic geometry.  In square space, four right angles  add up to the whole shebang 360°. But in the logical abstract it needn’t be that way. What if “space” consisted of 3 right angles , or 12? Something to think about.

    Oh — and what if it took one number of azimuthal ∢ right-angles to make the whole pie round, and took a different number of planar right-angles to make that whole pie round? Yeah, that would be weird too.



  4.  Watch the 20-minute movie Not Knot, where they explain that links—knots made of several (closed/looped/circular) ropes rather than just one rope—biject uniquely to the complement of some hyperbolic geometrical space.

    Since hyperbolic geometric spaces had already been explored a bit before the 1980’s, now everyone had a fun tool to unite concepts and ad-lib toward new ones. The new bijection opened up the gates to some easy logical shortcuts. I drew a picture of the way this kind of logic goes in talking about a clever way someone thought of to generate random normals with little computation.

    But this is in general how mathematicians solve impossible-sounding problems. I use a little bit of logic in domain X, as long as it’s easy there. Then I use this equivalence that somebody figured out to port the stuff into domain Y. Then I do that’s easy in domain Y. Then I either go back to my original domain or maybe I use some more equivalences to do easy stuff in domain ℤ, ℚ, Linear, and so on—always only using “obvious” logic in the particular domain, and letting the equivalences keep me right as I convert the problem across domains. The “link”-to-hyperbolic-complement-space was one such. Other examples include Fourier-to-regular domain, polynomials-to-sequences, equivalences-across-NP-complete-problems, graphs-to-matrices, matrices-to-characters, Lie-groups-to-matrices, …..

    Oops — just used another common maths word without defining it. Bijections are one-to-one mappings from the source domain onto the entire whole of the target domain. For example a strictly monotonic function from ℝ→ℝ uniquely assigns members ∈ℝ to other members ∈ℝ — in such a way that no value is reused and every value is used.

    A strictly monotone function injects the source into && surjects the source onto the target—which means it can be inverted. (By contrast, a non-monotonic, up-and-down-looking function, re-uses values, so going in reverse you couldn’t tell which usage the 3 had come from.)

    If ∃ a bijection between X and Y, then ∃ a correspondence between X and Y. When mathematicians are trying to speak casually, they will often say something like “You can’t comb a hedgehog” or “You can turn any 3-manifold into a 3-sphere”. “You can do” is their way of saying ∃ a bijecting function that relates the two: ƒ(X)=Y. If ∄ a bijection, then it’s impossible to put X and Y into correspondence — there’s no earthly or heavenly way in which these two things could be made to look alike. For example, maps must fail to correctly show the globe because ∄ a bijection between a globe and a plane. (They also fail because of distortions; that would be asking for a conformal, area-preserving bijection instead of merely a bijection.)

    They also show how spaces-with-stuff-removed can biject to completely unexpected things. A punctured plane is equivalent to the surface of a cylinder, for instance. (?!?!) The punctured surface of a ball is equivalent to a (not-punctured) plane, for instance. (‽‽) Hey, I don’t make this stuff up, I’m just reporting the facts.

    I guess in this talk he is showing different pictures of the associated geometry of various links. 

     
  5. Look up Hopf fibrations, one-point compactification, nilgeometry, solvegeometry, Lie groups (they’re groups, but continuous rather than discrete), Hopf circles,  …. on Wikipedia. Be forewarned: this may turn into a months-long reading project.




  6.  Complements. Not Knot talks confusingly on this topic (“it’s not empty space, it’s space that’s not even there” … I think that way of talking only makes sense to mathematicians).

    As I said in (2), spaces-with-stuff-removed can be homeomorphic to something completely unexpected. If you remove a point from the plane you introduce cylindricity around that point. Kind of unexpected that poking a hole in a square space makes a circular space, but that’s logic for you—always pointing out that illogical-sounding things are in fact inescapably true. 

    The symbology for complements looks too similar to the symbology for quotients. Sorry, not my decision. ℝ\ℚ = the irrationals ℚ∁. ℂ\ℚ∁ = the curliness of √−1 without the ridiculously, insanely thick thickness of the continuum. A manageable space in which not all sequences converge. ℝ\Transcendentals = Algebraics. Another eminently reasonable number system that does everything you’d want without the messy insanity.

    ℚ\0 = all fractions, minus zero. This is a punctured thing. ℝ²\0 = the punctured plane. ℝ³\0 = the cubic solid we seem to live in (Newton’s rigid rods) minus a point in the center of the universe. I don’t know if ℝ³\0 bijects to a looped thing like ℝ²\0.


    The world\Snoopy. Logically it’s equivalent to the punctured cubic thing I just described. Kind of boring, I thought removing Snoopy would be more devastating.

    BTW, you can also adjoin things, like ℚ adjoin i = ℂ\ℚ∁ mentioned above. I like this one if you can’t tell. ℝ adjoin ∞ is the one-point compactification of the line (as long as ∞ is defined to be ± ∞ so you can get there from the left or right)

  7. Symmetries.  The peace sign has a 3-way symmetry. Mirror images are 2-way symmetries. You could draw a flower with a 5-fold symmetry or a 12-fold symmetry and so on. The concept itself isn’t confusing, but the way Thurston and Not Knot talk fluidly, assuming without making explicit the implications of identification, quotienting by symmetry, topological gluing, point/line removal, and complementation together, is overwhelming.




Even though Lil’ Wayne has been a thing for half a decade, I only just now listened to a song of his: Hustler Musik. I like it.

I think this video is juxtaposing different people’s work lives—unemployed responsible guy, cop, drug dealer, stripper.

And check this out at 3:09, 3:17, 3:30 and 3:54 — one of the strippers is reading Tensor Calculus by Synge & Schild.

Also a quantum chemistry book (can’t make out the author).

  • From the main girl’s facial expression at 3:54, I think it isn’t her book. But then again, she  counts her money on it which suggests it is hers.
  • Is the reader currently enrolled in a degree programme? 
  • Both books look in excellent condition — a little too unbent for her to be very far through them. (the softback cover would lift up more if she had made it to chapter 3)
  • How much down-time do you have between dances? I would think there’s some other “duty” or else they would send you home. Maybe working the crowd to sell private dances or trying to get guys to buy drinks.
  • Then again, these dense books are easy to fill up on quickly. When I was working as an artists’ model I would read a bit of maths before I started posing, that way I would have plenty to think about while I stood/sat there.
  • Even though it is a stereotype for a sex worker to say “I’m doing this to put myself through university” — because of the common belief that university is good and valid, much more so than just reading about quantum chemistry because you’re inherently interested in the universe — I don’t think it’s at all unrealistic to show a beautiful woman being into scientific / mathematical erudition. I hate the “attractive people are stupid” stereotype even more than I hate the “nerds rule the world” stereotype. And I actually know a girl who used to dance and at the time had attained an even higher level of mathematical erudition than this girl.
  • A young, attractive girl is much more likely to be able to make good money dancing than by knowing about quantum chemistry. Dancing is also a pick-up job in a way that, for example, working at Fermilab is not. I expect life is freer when you’re doing something like that. Also you don’t have to dance 40-60 hours/week, which leaves plenty of time for intellectual pursuits. I am never surprised to learn that someone with a lot of mathematical erudition is working in a job completely lacking university pre-requisites.
  • I actually have a copy of Synge & Schild — it was recommended supplementary reading in differential geometry class. The writing is good, but for pleasure reading I prefer the diagrams of Solid Shape—a book I’ve extolled in these pages before.
 

WTF is a tensor? I have a much longer post about the topic in my Drafts folder (along with 1150 others), but here’s a quickie preview:

  1. A matrix has two subscripts (row & column); a tensor has three or more subscripts.
  2. Just like the number of rows and columns in a matrix tell you “how many input dimensions” and “how many output dimensions”, tensors can also input/output vectors, matrices, 9-tensors, and so on. A weighted inner product looks like a (0,2) tensor, for example. A matrix looks like a (1,1) tensor, a vector looks like a (0,1) tensor, and a 1-form looks like a (1,0) tensor.
  3. A typical example of a tensor is the stress/strain tensor:

    The piece I have in my drafts folder is talking about foreign exchange rates.
  4. And back to the girl’s pair of textbooks—the two texts do go together. If you think about stress/strain tensors acting on a bridge or something—well if we were talking at a small scale then the forces could be electrical rather than mechanical and operating on a tetrahedron-shaped methane molecule. Tensors are the normal way to combine lots of different forces on different faces of an object.  

To understand tensors, I would recommend looking at the Wikipedia page and Chris Tiee’s essay Covariance, Contravariance, Densities, and All That and maybe also the fourth chapter of MIT OCW’s intro to geophysics lecture notes (that’s on the stress/strain tensors).




If I google for “probability distribution” I find the following extremely bad picture:

bad picture of a probability dist

It’s bad because it conflates ideas and oversimplifies how variable probability distributions can generally be.

  • Most distributions are not unimodal.
  • Most distributions are not symmetric.
  • Most distributions do not have mean = median = mode.
  • Most distributions are not Gaussian, Poisson, binomial, or anything famous at all.
    famous distributions 
  • If this is the example you give to your students of “a distribution”, why in the world would they be surprised at the Central Limit Theorem? The reason it’s interesting is that things that don’t look like the above, sum to look like the above.
  • People already mistakenly assume that everything is bell curved. Don’t reinforce the notion!
 

Here is a better picture to use in exposition. In R I defined

bimodal <- function(x) {
3 * dnorm(x, mean=0, sd=1)   +   dnorm(x, mean=3, sd=.3) / 4
                         }
.

That’s what you see here, plotted with plot( bimodal, -3, 5, lwd=3, col="#333333", yaxt="n" ).

A probability distribution.

Here’s how I calculated the mean, median, and mode:

  • mean is the most familiar  Definition of mean.. To calculate this in R I defined bimodal.x <- function(x) { x * 3 * dnorm(x, mean=0, sd=1)   +   x * dnorm(x, mean=3, sd=.3) / 4  } and did integrate(bimodal.x, lower=-Inf, upper=Inf).

    The output is .75, that’s the mean.
  • mode is the x where the highest point is. That’s obviously zero. In fancy scary notation one writes “the argument of the highest probability” Definition of mode.
  • median is the most useful but also the hardest one to write the formulaic definition. Median has 50% of the probability mass to the left and 50% of the probability mass to the right. So Definition of median.In R I had to plug in lots of values to integrate( bimodal, lower = -Inf, upper = ... ) and integrate( bimodal, upper = Inf, lower = ...) until I got them to be equal. I could have been a little smarter and tried to make the difference equal zero but the way I did it made sense and was quick enough.

    The answer is roughly .12.
    > integrate( bimodal, lower = -Inf, upper = .12 )
    1.643275 with absolute error < 1.8e-08
    > integrate( bimodal, upper = Inf, lower = .12 )
    1.606725 with absolute error < 0.0000027

    (I could have even found the exact value in Excel using the solver. But I felt lazy, please excuse me.)  

A (bimodal) probability distribution with distinct mean, median, and mode.

Notice that I drew the numbers as vertical lines rather than points on the curve. And I eliminated the vertical axis labels. That’s because the mean, median, and mode are all x values and have nothing whatever to do with the vertical value. If I could have figured out how to draw a coloured dot at the bottom, I would have. You could also argue that I should have shown more humps or made the mean and median diverge even more.

Here’s how I drew the above:

png("some bimodal dist.png")
leg.text <- c("mean", "median", "mode")
leg.col <- c("red", "purple", "turquoise")
par(lwd=3, col="#333333")
plot( bimodal, -5, 5, main = "Some distribution", yaxt="n" )
abline(v = 0, col = "turquoise")
abline(v = .12, col = "purple")
abline(v = .75, col = "red")
legend(x = "topright", legend = leg.text, fill = leg.col, border="white", bty="n", cex = 2, text.col = "#666666")
dev.off() 

Lastly, it’s not that hard in the computer era to get an actual distribution drawn from facts. The nlme package has actually recorded heights of boys from Oxford:

require(nlme); data(Oxboys); plot( density( Oxboys$height), main = "height of boys from Oxford", yaxt="n", lwd=3, col="#333333")

and boom:

kernel density plot of Oxford boys' heights.

or in histogram form with ggplot, run require(ggplot2); qplot( data = Oxboys, x = height ) and get:

histogram of Oxford boys' heights, drawn with ggplot.

the heights look Gaussian-ish, without mistakenly giving students the impression that real-world data follows perfect bell-shaped patterns.




Doug Hofstadter is an inveterate observer of his own mind. His wonderful satirical essay on sexist language builds on a multi-year compilation of sexist words: every time he heard a word that privileges males (like “chairman”, “clergyman”, “foreman”, “manpower”) he’d write it down. After years (maybe decades?) of acutely observing language for this phenomenon, he wrote that killer essay. Only someone like that could explain the personal-ness of ideas and metaphors so well.

Around minute 33, he cuenta la cuenta of how an image/feeling from his past was called up years later, mogrified, contrasted, or intertwined with something else that he was experiencing at the time.

He also talks about what I would call “personal slang”. For example Doug says he thinks of the words “sourgrapes” as just one word. I would take this farther and say that, for me, entire stories can sometimes take up “just one mental unit”. For instance ∃ an economic argument that harsh factory labour in the Philippines is good because it’s better than picking trash out of a garbage heap. I could rattle this argument off so quickly in my head that I would be still spitting it out in person for five to ten minutes after I finished the thought to myself. What I mean is that since the argument is encoded as a single unit in my mind, it just takes me a millisecond to conceive the whole thing. So if I’m reading something on-topic that misses the point of that argument, I can quickly call up the entire speech and dismiss what I’m reading in the next millisecond.

I have many other personal metaphors (some of them are more original than repeating a popular argument). Some of them come from a mathematics textbook. And from these, a blog is born.

 

Do you ever watch a really awesome musical and wish, when you’re leaving the theatre, that people in regular life would burst out in choreographed song-and-dance? Or that no-one would look askance at you if you belted a song while you’re walking down the street, feeling intensely cheerful or melancholic? …Well, I’ve wished that before…and I’ve also wished that I could use mathematics in my speech (it’s sometimes possible with the right crowd, if there’s a chalkboard handy). And that’s what i’m trying to accomplish here.

A few months before I started this blog, I was auditing a class on the applications of differential geometry to psychology. I realised that due to X years of mathematical modelling and reading books about median voter theorems, hyperfunctions or Fourier transforms, my vision of the world had become totally abnormal.

Over the past several years I’ve felt increasingly like I need to share these shapes. You know, because it’s extremely weird to want other human beings to understand what you’re thinking / feeling on a deeper level, and stuff. And I feel like there are certain ideas that I have where some abstract idea from a maths book interpenetrate with whatever else is going on in my life, such that I think these unshareable thoughts like:

The difference between this blog and what you’d read on Wikipedia or in a paper or textbook is, I’m trying really hard to not be didactic. Sometimes I do define something or share an “aha!” moment in case it’s also the “aha!” for someone else. But when I do that it’s as preparation for a story I want to tell later. Like my wish that people would spontaneously break out in song-and-dance, I wish that I could just talk normally using these metaphors—like at a party or something, and people would know what i mean, and it wouldn’t seem either braggy or academic.

 

In Plato’s vision of an ideal government, The Republic, the “gold” people—the philosopher-kings—would train during their youth in corporalità and geometry, among other things. Geometry was supposed to teach the philosopher-kings abstract reasoning and about the true forms of the world.

Well, mathematics came a long way in the 19th & 20th centuries, such that I find the Platonic ideal much more believable now. For me, I want to understand quasimetrics, homotopy, cohomology, CW-complexes—those are the imagination-tools I want to have to see the world. If a deep understanding of surgery theory is more valuable to Gregory Perelman than a million bucks—that’s pretty convincing proof, to me, that this is personal language worth acquiring.




[I]n the late 1920’s and early 1930’s…. There were lots of deep thoughts [in economics], but a lack of quantitative results. … It is usually not of very great practical or even scientific interest to know whether the [causal] influence [of some factor] is positive or negative, if one does not know anything about the strength.


But much worse is the situation when an [outcome] is determined by many different factors at the same time, some factors working in one direction, others in the opposite directions. One could write long papers about so-called tendencies explaining how this … might work…. But what is the … total net effect of all the factors? This question cannot be answered without measures of … strength….

Trygve Haavelmo

Bank of Sweden pseudo-Dynamite Prize Laureate 1989, for work in econometrics

(Source: nobelprize.org)







I view a mathematics library the same way an archaeologist views a prime digging site. There are all these wonderful treasures that are buried there and hidden from the rest of the world.

If you pick up a typical book on sheaf theory, for example, it’s unreadable. But it’s full of stuff that is very, very important to solving really difficult problems.

And I have this vision of digging through the obscure text and finding these gems and exporting them over to the engineering college and other domains where these tools can find utility.




[G]eometry and number[s]…are unified by the concept of a coordinate system, which allows one to convert geometric objects to numeric ones or vice versa. …

[O]ne can view the length ❘AB❘ of a line segment AB not as a number (which requires one to select a unit of length), but more abstractly as the equivalence class of all line segments that are congruent to AB.

With this perspective, ❘AB❘ no longer lies in the standard semigroup ℝ⁺, but in a more abstract semigroup (the space of line segments quotiented by congruence), with addition now defined geometrically (by concatenation of intervals) rather than numerically.

A unit of length can now be viewed as just one of many different isomorphisms Φ: ℒ → ℝ⁺ between and ℝ⁺, but one can abandon … units and just work with directly. Many statements in Euclidean geometry … can be phrased in this manner.

(Indeed, this is basically how the ancient Greeks…viewed geometry, though of course without the assistance of such modern terminology as “semigroup” or “bilinear”.)
Terence Tao

(Source: terrytao.wordpress.com)




All right, Φ is straight-up crazy.

  • Φ² = Φ + 1
  •  ⅟Φ = Φ − 1

recursive definition of ¦




I’m working on a longish post about dichotomies. It’s going to be about mathematical objects that can serve as metaphors to think beyond binary opposition.

In researching the article, I found the following in the Internet Encyclopedia:

According to Jacques Derrida,[citation needed] meaning in the West is defined in terms of binary oppositions, “a violent hierarchy” where “one of the two terms governs the other.”

I don’t know if Derrida actually said that. But I can already think of a counterexample from mathematics.

 

The number √−1 is logically equivalent to √−1. In other words i and −i are indistinguishable.

Doug Hofstadter was fond of making this point to us.

  • Complex conjugation would work the same.
  • Addition, subtraction, multiplication, and division would work the same.
  • The anticlockwise direction in the complex plane is arbitrary. If the “southern” i were the one we currently call +i, then we’d do things clockwise and everything would work out the same.
  • So integration and differentiation would work the same as well.
  • (On the other hand, −1 is not the same as +1−1 instantiates an “alternating” pattern whereas +1 instantiates a “stay the same” pattern, under multiplication.)
  • It’s like group theory. Say we’re talking about the group P₃. Any of the atoms could be called “first”, “second”, or “third”. It wouldn’t matter.

    What matters is the structure, the relationships, the way they do things. Neither is “worse”, “better”, “before”, “after”, or “dominated by” the others—they simply relate to each other in the P₃ way.

So right there, you’ve got a binary opposition where neither term governs the other.




Leonardo da Vinci&#8217;s ability to embrace uncertainty, ambiguity, and paradox was a critical characteristic of his genius. &#8212;J Michael Gelb
Say you want to use a mathematical metaphor, but you don&#8217;t want to be really precise. Here are some ways to do that:
Tack a +ε onto the end of an equation.
Use bounds (&#8220;I expect to make less than a trillion dollars over my lifetime and more than $0.&#8221;)
Speak about a general class without specifying which member of the class you&#8217;re talking about. (The members all share some property like, being feminists, without necessarily having other properties like, being women or being angry.)
Use fuzzy logic (the ∈ membership relation gets a percent attached to it: &#8220;I 30%-belong-to the class of feminists | vegetarians | successful people.&#8221;).
Use a specific probability distribution like Gaussian, Cauchy, Weibull.
Use a tempered distribution a.k.a. a Schwartz function.
Tempered distributions are my favourite way of thinking mathematically imprecisely.
Tempered distributions have exact upper and lower bounds but an inexact mean and variance. T.D.&#8217;s also shoot down very fast (like exp{−x²} the gaussian) which makes them tractable.
For example I can talk about the temperature in the room (there is not just one temperature since there are several moles of air molecules in the room), the position of a quantum particle, my fuzzy inclusion in the set of vegetarians, my confidence level in a business forecast, &#8230;.. with a definite, imprecise meaning.
Classroom mathematics usually involves precise formulas but the level of generality achieved by 20th century mathematicians allows us to talk about a cobordism between two things without knowing everything precisely about them.
It&#8217;s funny; the more advanced and general the mathematics, the more casual it can become. Like stingy stickler things that build up to a chummy, whatever-it&#8217;s-all-good.
 
Our knowledge of the world is not only piecemeal, but also vague and imprecise. To link mathematics to our conceptions of the real world, therefore, requires imprecision.
I want the option of thinking about my life, commerce, the natural world, art, and ideas using manifolds, metrics, functors, topological connections, lattices, orthogonality, linear spans, categories, geometry, and any other metaphor, if I wish.

Leonardo da Vinci’s ability to embrace uncertainty, ambiguity, and paradox was a critical characteristic of his genius. —J Michael Gelb

Say you want to use a mathematical metaphor, but you don’t want to be really precise. Here are some ways to do that:

  • Tack a onto the end of an equation.
  • Use bounds (“I expect to make less than a trillion dollars over my lifetime and more than $0.”)
  • Speak about a general class without specifying which member of the class you’re talking about. (The members all share some property like, being feminists, without necessarily having other properties like, being women or being angry.)
  • Use fuzzy logic (the  membership relation gets a percent attached to it: “I 30%-belong-to the class of feminists | vegetarians | successful people.”).
  • Use a specific probability distribution like Gaussian, Cauchy, Weibull.
  • Use a tempered distribution a.k.a. a Schwartz function.

Tempered distributions are my favourite way of thinking mathematically imprecisely.

Tempered distributions have exact upper and lower bounds but an inexact mean and variance. T.D.’s also shoot down very fast (like exp{−x²} the gaussian) which makes them tractable.

For example I can talk about the temperature in the room (there is not just one temperature since there are several moles of air molecules in the room), the position of a quantum particle, my fuzzy inclusion in the set of vegetarians, my confidence level in a business forecast, ….. with a definite, imprecise meaning.

Classroom mathematics usually involves precise formulas but the level of generality achieved by 20th century mathematicians allows us to talk about a cobordism between two things without knowing everything precisely about them.

It’s funny; the more advanced and general the mathematics, the more casual it can become. Like stingy stickler things that build up to a chummy, whatever-it’s-all-good.

 

Our knowledge of the world is not only piecemeal, but also vague and imprecise. To link mathematics to our conceptions of the real world, therefore, requires imprecision.

I want the option of thinking about my life, commerce, the natural world, art, and ideas using manifolds, metrics, functors, topological connections, lattices, orthogonality, linear spans, categories, geometry, and any other metaphor, if I wish.




  • The shape of the continents depends on the global temperature. (Cold locks ice in polar caps.) Google “Morse theory”.
  • The price of housing always rises, until it doesn’t.
     
  • You develop a system of habits to discipline yourself; maxims for self-motivation; then the working world changes on you. Loyalty is no longer rewarded. Hard work is less valued than the ability to make PlentyOfFish.com.
  • For years the normal trading range of [insert spread, instrument, or security] is X, until one day sufficiently many (external) parameters shift. The market changes and you see a 20-sigma event. Heroes only.

  • Whoever coded your profile website (chi.mp, flavors.me, tumblr), wrote a route that takes a string as parameter. Entering the name isomorphismes into this function fetches this webdata. Entering your name fetches your webdata. All part of one and the same formula.
  • The Lotka-Volterra equations of a large ecosystem, dancing as the sliders shift around in their hypercube. Death and life hang in the balance. And it’s literally a balance. If the fulcrum moves so far that the lever hits the ground, a species will either become extinct or overpopulate the ecosystem (like an algal bloom)—either phase change being irreversible. (Er, at least anti-entropic.)
     
  • You think you know yourself, until you step into a new context—new country, new career, new city—and latent aspects of you become dominant.

    Who was I before? If I was her then and am this now, what is the underlying me?

    Self as a function of circumstance. Perhaps just as constant at root, but reactive; responsive; springy; primed for change.