Posts tagged with measure theory

Lebesgue’s approach to integration was summarized in a letter to Paul Montel. He writes:

I have to pay a certain sum, which I have collected in my pocket. I take the bills and coins out of my pocket and give them to the creditor in the order I find them until I have reached the total sum. This is the Riemann integral. But I can proceed differently. After I have taken all the money out of my pocket I order the bills and coins according to identical values and then I pay the several heaps one after the other to the creditor. This is my integral.
Siegmund-Schultze, Reinhard (2008), “Henri Lebesgue”, in Timothy Gowers, June Barrow-Green, Imre Leader, Princeton Companion to Mathematics

(Source: Wikipedia)

## One

Counting generates from the programmer’s successor function `++` and the number one. (You might argue that to get out to infinity requires also repetition. Well every category comes with composition by default, which includes composition of ƒ∘ƒ∘ƒ∘….)

But getting to one is nontrivial. Besides the mystical implications of 1, it’s not always easy to draw a boundary around “one thing”. Looking at snow (without the advantage of modern optical science) I couldn’t find “one snow”. Even where it is cut off by a plowed street it’s still from the same snowfall.

And if you got around on skis a lot of your life you wouldn’t care about one snow-flake (a reductive way to define “one” snow), at least not for transport, because one flake amounts to zero ability to travel anywhere. Could we talk about one inch of snow? One hour of snow? One night of snow?

Speaking of the cold, how about temperature? It has no inherent units; all of our human scales pick endpoints and define a continuum in between. That’s the same as in measure theory which gave (along with martingales) at least an illusion of technical respectability to the science of chances. If you use Kolmogorov’s axioms then the difficult (impossible?) questions—what the “likelihood” of a one-shot event (like a US presidential election) actually means or how you could measure it—can be swept under the rug whilst one computes random walks on trees or Gaussian copulæ. Meanwhile the sum-total of everything that could possibly happen `Ω` is called 1.

With water or other liquids as well. Or gases. You can have one grain of powder or grain (granular solids can flow like a fluid) but you can’t have one gas or one water. (Well, again you can with modern science—but with even more moderner science you can’t, because you just find a QCD dynamical field balancing (see video) and anyway none of the “one” things are strictly local.)

And in my more favourite realm, the realm of ideas. I have a really hard time figuring out where I can break off one idea for a blogpost. These paragraphs were a stalactite growth off a blobular self-rant that keeps jackhammering away inside my head on the topic of mathematical modelling and equivalence classes. I’ve been trying to write something called “To equivalence class” and I’ve also been trying to write something called “Statistics for People Who Program Computers” and as I was talking this out to myself, another rant squeezed out between my fingers and I knew if I dropped the other two I could pull One off it could be sculpted into a readable microtract. Leaving “To Equivalence Class”, like so many of the harder-to-write things, in the refrigerator—to marinate or to mould, I don’t know which.

But notice that I couldn’t fully disconnect this one from other shared-or-not-shared referents. (Shared being English language and maybe a lot of unspoken assumptions we both hold. Unshared being my own personal jargon—some of which I’ve tried to share in this space—and rants that continually obsess me such as the fallaciousness of probabilistic statements and of certain economic debates.) This is why I like writing on the Web: I can plug in a picture from Wikipedia or point back to somewhere else I’ve talked on the other tangent so I don’t ride off on the connecting track and end up away from where I tried to head.

The difficulty of drawing a firm boundary of "one" to begin the process of counting may be an inverse of the "full" paradox or it may be that certain things (like liquid) don’t lend themselves to counting in an obvious way—in jargon, they don’t map nicely onto the natural numbers (the simplest kind of number). If that’s a motivation to move from discrete things to continuous when necessary, then I feel a similar motivation to move from Euclidean to Hausdorff, or from line to poset. Not that the simpler things don’t deserve as well a place at the table.

We thinkers are fairly free to look at things in different ways—to quotient and equivalence-class creatively or at varying scales. And that’s also a truth of mathematical modelling. Even if maths seems one-right-answer from the classroom, the same piece of reality can bear multiple models—some refining each other, some partially overlapping, some mutually disjoint.

## Conflicting Ideas of Probability & Likelihood in the Wikipedia article on Monopoles

In the Public Encyclopedia’s (present) discussion of the hypothetical existence of a magnetic monopole

in nature, among the possible fundamental particles, exemplifies both (and maybe >2) “sides” in the debate over what probability means:

Magnetism in bar magnets and electromagnets does not arise from magnetic monopoles, and in fact there is no conclusive experimental evidence that magnetic monopoles exist at all in the universe.

Since Dirac’s 1931 paper[8] , several systematic monopole searches have been performed. Experiments in 1975[10] and 1982[11] produced candidate events that were initially interpreted as monopoles, but are now regarded as inconclusive.[12]Therefore, it remains an open question whether or not monopoles exist.

Further advances in theoretical particle physics, particularly developments in grand unified theories and quantum gravity, have led to more compelling arguments[which?] that monopoles do exist. Joseph Polchinski, a string-theorist, described the existence of monopoles as "one of the safest bets that one can make about physics not yet seen”.[13]These theories are not necessarily inconsistent with the experimental evidence. In some theoretical models, magnetic monopoles are unlikely to be observed, because they are too massive[why?] to be created in particle accelerators, and also too rare in the Universe to enter a particle detector with much probability.[13] (According to these models, there may be as few as one monopole in the entire visible universe.[14])

Here are a few potential explanations of how one is to arrive at a probability number:

• opinion — it’s just Joseph Polchinski’s opinion
• frequentism — Europeans never observed a black swan before exploring the New World, therefore black swans have 0% chance of existing.
• frequentism + how hard you’ve searched — the probability comes attached with a confidence number. If you’ve stayed within the city limits of Minneapolis your entire life, you should attach a low confidence to your search for tarantulas the size of your head. But we’ve tried very hard to find monopoles, and haven’t. So a “more confident” zero on that one.
• Dutch Books — could we arbitrage Joseph Polchinski’s “sure thing” bet?
• authority, credibility, expertise — who exactly is this Joseph Polchinski character, anyway? And who says he’s such an expert? Is he an interested party? I don’t believe what vested interests and biased sources say, even if it happens to be true.
• propensity — good gravy, I don’t even get to invoke the famous “coin has an innate propensity to tend to certain heads/tails ratio” because it would get us nowhere in terms of “Do monopoles have a propensity to exist or not?”. Anyway propensity merely passes the buck even in the cases where it does make sense.
• reason & facts — there is no conclusive evidence that monopoles exist, yet they haven’t been proven impossible. I will withhold my opinion and it would be unreasonable to assign a probability mass to either alternative. We’re simply somewhere ∈ [0%, 100%] at this time.
• model strength — some of these models sound suspect. It’s constructed “just so” that there’s only one monopole in the universe? Very convenient for you, when you want to say monopoles exist and we just haven’t seen them yet. Pull the other one!

All of the stochastic maths is done with the Kolmogorov axioms, i.e. it’s done with measure spaces with a fixed | finite | constant measure (= 100% of the probability mass) without connecting that to “how likely” a one-off event is. (Much like some maths you could pass off as financial modelling “is just" the theory of martingales = fair repeated bets.) But it needn’t have be called “likelihood”, it could have been “fuzzy truthiness” or “believability” or “motions of a fixed-volume-but-infinitely-divisible liquid”. As Cosma Shalizi puts it here:

Probabilities are numbers that tell us how often things happen.

Mathematicians are anxious to get on with talking about ergodicity, Markov transition matrices, and large-deviations theory. What you’re seeing in this block quote is the handoff between mathematicians and philosophers—essentially the mathmos say “You take it from here to the firm foundation” and philosophers, so far, haven’t been able to.

` `

Is there a problem in practice due to not having a sound foundation on our concept of probability? Yes. It’s not secure to move forward with the rear flank uncovered. The lax attitude toward probability and “We’ll do the best with what we can” lets us make up numbers for the `{pessimistic, neutral, optimistic}` scenarios of our forecasting spreadsheets.

Think about when some consequential decision by a powerful group depends on the value of one parameter. It could be

• the likelihood of Floridian home prices decreasing by more than 5% in a year,
• the likelihood of [foreign country X] attacking "us" in response to Y,
• the likelihood of RHIC creating a strangelet and swallowing the world in a minisecond,
• the likelihood of construction on the new power plant going over budget,
• the likelihood of borrowing rates staying this low for another 5 years,
• the likelihood of real GDP rising at least 2%/year during the next 10 years,
• the likelihood of our borrowing rate quadrupling
• the likelihood that your college degree will “be worth it” to you
• the likelihood of this whole startup thing actually working.

and I get to either rely on

• historical data (“home prices have always gone up before”, “we haven’t seen any problems with financial derivatives yet”, “correlation with a Gaussian copula has always worked so far”),
• reason and facts (and multiply an endless debate among the experts),
• or gut (throw in some numbers that sound pessimistic, optimistic, and neutral, and we’ll see how the forecast behaves).

We got nuthin’.

## Measure: Sizing up the Continuum

For those not in the know, here’s what mathematicians mean by the word “measurable”:

1. The problem of measure is to assign a ℝ size `≥ 0` to a set. (The points not necessarily contiguous.) In other words, to answer the question:
How big is that?
2. Why is this hard? Well just think about the problem of sizing up a contiguous ℝ subinterval between `0` and `1`.

• It’s obvious that `[.4, .6]` is `.2` long and that
• `[0, .8]` has a length of `.8`.
• I don’t know what the length of `[¼√2, √π/3]` is but … it should be easy enough to figure out.
• But real numbers can go on forever: `.2816209287162381682365...1828361...1984...77280278254...`.
• Most of them (the transcendentals) we don’t even have words or notation for.
• So there are a potentially infinite number of digits in each of these real numbers — which is essentially why the real numbers are so f#cked up — and therefore ∃ an infinitely infinite number of numbers just between 0% and 100%.

Yeah, I said infinitely infinite, and I meant that. More real numbers exist in-between `.999999999999999999999999` and `1` than there are atoms in the universe. There are more real numbers just in that teensy sub-interval than there are integers (and there are integers).

In other words, if you filled a set with all of the things between `.99999999999999999999` and `1`, there would be infinity things inside. And not a nice, tame infinity either. This infinity is an infinity that just snorted a football helmet filled with coke, punched a stripper, and is now running around in the streets wearing her golden sparkly thong and brandishing a chainsaw:

Talking still of that particular infinity: in a set-theoretic continuum sense, ∃ infinite number of points between Barcelona and Vladivostok, but also an infinite number of points between my toe and my nose. Well, now the simple and obvious has become not very clear at all!

So it’s a problem of infinities, a problem of sets, and a problem of the continuum being such an infernal taskmaster that it took until the 20th century for mathematicians to whip-crack the real numbers into shape.
3. If you can define “size” on the `[0,1]` interval, you can define it on the `[−535,19^19]` interval as well, by extension.

If you can’t even define “size” on the `[0,1]` interval — how do you think you’re going to define it on all of ℝ? Punk.
4. A reasonable definition of “size” (measure) should work for non-contiguous subsets of ℝ such as “just the rational numbers” or “all solutions to `cos² x = 0`(they’re not next to each other) as well.

Just another problem to add to the heap.
5. Nevertheless, the monstrosity has more-or-less been tamed. Epsilons, deltas, open sets, Dedekind cuts, Cauchy sequences, well-orderings, and metric spaces had to be invented in order to bazooka the beast into submission, but mostly-satisfactory answers have now been obtained.

It just takes a sequence of 4-5 university-level maths classes to get to those mostly-satisfactory answers.

One is reminded of the hypermathematicians from The Hitchhiker’s Guide to the Galaxy who time-warp themselves through several lives of study before they begin their real work.

For a readable summary of the reasoning & results of Henri Lebesgue's measure theory, I recommend this 4-page PDF by G.H. Meisters. (NB: His weird ∁ symbol means complement.)

That doesn’t cover the measurement of probability spaces, functional spaces, or even more abstract spaces. But I don’t have an equally great reference for those.

Oh, I forgot to say: why does anyone care about measurability? Measure theory is just a highly technical prerequisite to true understanding of a lot of cool subjects — like complexity, signal processing, functional analysis, Wiener processes, dynamical systems, Sobolev spaces, and other interesting and relevant such stuff.

It’s hard to do very much mathematics with those sorts of things if you can’t even say how big they are.

## Sigma Algebras

Are mathematicians deliberately obscure? Or is it really so hard for them to write prose?

Check out this description of σ-algebras from Wik***dia.

In mathematics, a σ-algebra (also sigma-algebraσ-fieldsigma-field) is a technical concept for a collection of sets satisfying certain properties.[1] The main use of σ-algebras is in the definition of measures; specifically, a σ-algebra is the collection of sets over which a measure is defined.

No sh_t? It’s technical? And it satisfies properties. You don’t say.

The kernel of that paragraph is just one sentence.
GOOD:

In mathematics, a σ-algebra is a measurable collection of sets.

I changed the W*****dia page at 8:40pm on 3 Mar ‘11. Let’s see if I get in trouble. (I bet if I do it will be for “not being rigorous” or “original research”.)

Yes this is a specialist topic, but that doesn’t require gobbledegook. A σ-algebra is measurable like , but is not ℝ. Why can’t we just use normal words?

UPDATE: It hurts to be this right. My changes were reverted about an hour after I put them up. Am I wrong here?

I’m reminded of a story Doug Hofstadter told us about a friend of his who submitted an article in clear, everyday language to an academic journal. According to DH, the journal’s editors rejected the piece, saying it was too unprofessional. They confused jargon with sophistication, bombast with wisdom.

I don’t know the friend’s name or the journal’s name, and I half-wonder if I am just being a pr\$ck about this Wikipedia article. But no, think about how people react to the word “maths”. This has got to be the reason—this and boring maths classes. Mathematicians literally refuse to write simply.

UPDATE 2: Another offender is the article on compact topological spaces. I’m actually removing some text from the garbled lede when I say:

In mathematics, specifically general topology, a compact topology is a topological space whose topology has the compactness property.

I think I’ve found a new candidate for worst sentences in the English language. Does anyone have George Orwell’s e-mail address?