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Pierre Bourdieu: “The way élites stay in power is not only by controlling the means of production, but by controlling ideas.”

Gillian Tett: “What if—just as I had studied the gap between rhetoric and reality among Tajik Muslims living in an atheist state (USSR)—I pretended I was a foreigner who had just landed in London,and in the same way looked at the gap between rhetoric in finance, and actual practice?”

  • the things we don’t talk about

Bourdieu: “The most powerful form of ideological effects, are those which need nothing more than a complicit silence.”

Upton Sinclair: “The hardest thing to get a man to understand, is what his job depends upon not understanding.”

  • a lot of media discussion of stock markets
  • some media discussion of currency markets
  • no discussion of bond markets
  • no discussion of the derivatives market

Gillian Tett: ”By studying the other, we can sometimes figure out something about ourselves.”

(Source: BBC)




Lucas’ “rational expectations” revolution in macroeconomics has been tied to the ending of stagflation in the world’s largest economy, and to the reintroduction of “psychology” into finance and economics. However, I never felt like the models of “expectation” I’ve seen in economics seem like my own personal experience of living in ignorance. I’d like to share the sketch of an idea that feels more lifelike to me.

http://www.olivierlanglois.net/images/voro2.jpg

First, let me disambiguate: the unfortunate term-overlap with “statistical expectation” (= mean = average = total over count = ∑ᵢᴺ•/N = a map from N dimensions to 1 dimension) indicates nothing psychological whatever. It doesn’t even correspond to “What you should expect”.

If I find out someone is a white non-Hispanic Estadounidense (somehow not getting any hints of which state, which race, which accent, which social class, which career track…so it’s an artificial scenario), I shouldn’t “expect” the family to be worth $630,000. I “expect” (if indeed my expectation is not a distribution but rather just one number) them to be worth $155,000. (scroll down to green)

Nor, if I go to a casino with 99% chance of losing €10,000 and 1% chance of winning €1,000,000 (remember the break-even point is €990,000). “On average” this is a great bet. But that ignores convergence to the average, which would be slow. I’d need to play this game a lot to get the statistics working in my favour, and I mightn’t stay solvent (I’d need to get tens of millions of AUM—with lockdown conditions—to even consider this game). No, the “statistical expectation” refers to a long-run or wide-space convergence number. Not “what’s typical”.

Not only is the statistical expectation quite reductive, it doesn’t resemble what I’ve introspected about uncertainty, information, disinformation, beliefs, and expectations in my life.

File:Coloured Voronoi 3D slice.svg

A better idea, I think, comes from the definition of Riemann integration over 2+ dimensions. Imagine covering a surface with a coarse mesh. The mesh partitions the surface. A scalar is assigned to each of the interior regions inscribed by the mesh. The mesh is then refined (no lines taken away, only some more added—so some regions get smaller/more precise and no regions get larger/less precise), new scalars are computed with more precise information about the scalar field on the surface.
a scalar field

NB: The usual Expectation operator 𝔼 is little more than an integral over “possibilities” (whatever that means!).

(In the definitions of Riemann integral I’ve seen the mesh is square, but Voronoi pictures look awesomer & more suggestive of topological generality. Plus I’m not going to be talking about infinitary convergence—no one ever becomes fully knowledgeable of everything—so why do I need the convenience of squares?)

I want to make two changes to the Riemannian-integral mesh.

image
image

 

First I’d like to replace the scalars with some more general kind of fibre. Let’s say a bundle of words and associations.

(You can tell a lot about someone’s perspective fro the words they use. I’ll have to link up “Obverse Words”, which has been in my drafts folder for over a year, once I finish it—but you can imagine examples of people using words with opposite connotation to denote the same thing, indicating their attitude toward the thing.)

http://i780.photobucket.com/albums/yy90/AlexMLeo/felixsbrain.jpg

Second, I’d like to use the topology or covering maps to encode the ignorance somehow. In my example below: at a certain point I knew “Rails goes with Ruby” and “Django goes with Python” and “Git goes with Github” but didn’t really understand the lay of the land. I didn’t know about git’s competitors, that you can host your own github, that Github has competitors, the more complex relationship between ruby and python (it’s not just two disjoint sets), and so on.

When I didn’t know about Economics or Business or Accounting or Finance, I classed them all together. But now they’re so clearly very very different. I don’t even see Historical Economists or Bayesian Econometricians or Instrumental Econometricians or Dynamical Macroeconomists or Monetary Economists or Development Economists as being very alike. (Which must imply that my perspective has narrowed relative to everyone else! Like tattoo artists and yogi masters and poppy farmers must all be quite different to the entire class of Economists—and look even from my words how much coarse generalisation I use to describe the non-econ’s versus refinement among the econ’s.
image
These meshes can have a negative curvature (with, perhaps a memory) if you like. You know when you think that property actuaries are nothing at all like health actuaries that your frame-of-reference has become very refined among actuary-distinguishment. Which might mean a coarse partitioning of all the other people! Like Bobby Fischer’s use of the term “weakies” for any non-chess player—they must all be the same! Or at least they’re the same to me.)

image

Besides the natural embedding of negatively-curved judgment grids, here are some more pluses to the “refinement regions” view of ignorance:

  1. You could derive a natural “conservation law” using some combination of e.g. ability, difficulty, how good your teachers are, and time input to learning, how many “refinements” you get to make. No one can know everything.

    (Yet somehow we all are supposed to function in a global economy together—how do we figure out how to fit ourselves together efficiently?

    And what if people use your lack of perspective to suggest you should pay them to teach you something which “evaluates to valuable” from your coarse refinement, but upon closer inspection, doesn’t integrate to valuable?)
  2. Maybe this can relate to the story of Tony—how we’re always in a state of ignorance even as we choose what to become less ignorant about. It would be nice to be able to model the fact that one can’t escape one’s biases or context or history.
  3. And we could get a fairly nice representation of “incompatible perspectives”. If the topology of your covering maps is “very hard” to match up to mine because you speak dialectics and power structures but I speak equilibria and optima, that sounds like an accurate depiction. Or when you talk to someone who’s just so noobish in something you’re so expert in, it can feel like a very blanket statement over so many refinements that you don’t want to generalise over (and from “looking up to” an expert it can also feel like they “see” much more detail of the interesting landscape.)
  4. Ignorance of one’s own ignorance is already baked into the pie! As is the beginner’s luck. If I “integrate over the regions” to get my expected value of a certain coarse region, my uninformed answer may have a lot of correctness to it. At the same time, the topological restrictions mean that my information and my perspective on it aren’t “over there” in some L2-distance sense, rather they’re far away in a more appropriately incompatible-with-others sense.

In conclusion, I’m sure everyone on Earth can agree that this is a Really Nifty and Cool Idea.

File:ApproximateVoronoiDiagram.png

 

I’ll try to give a colourful example using computers and internet stuff since that’s an area I’ve learned a lot more about over the past couple years.

A tiny portion of Doug Hofstadter’s “semantic network”.  via jewcrew728, structure of entropy

First, what does ignorance sound like?

  • (someone who has never seen or interacted with a computer—let’s say from a non-technological society or a non-computery elderly rich person. I’ve never personally seen this)
  • "Sure, programming, I know a little about that. A little HMTL, sure!”
  • "Well, of course any programming you’re going to be doing, whether it’s for mobile or desktop, is going to use HTML. The question is how.

OK, but I wasn’t that bad. In workplaces I’ve been the person to ask about computers. I even briefly worked in I.T. But the distance from “normal people” (no computer knowledge) to me seems very small now compared to the distance between me and people who really know what’s up.

A few years ago, when I started seriously thinking about trying to make some kind of internet company (sorry, I refuse to use the word “startup” because it’s perverted), I considered myself a “power user” of computers. I used keyboard shortcuts, I downloaded and played with lots of programs, I had taken a C++ course in the 90’s, I knew about C:\progra~1 and how to get to the hidden files in the App packages on a Mac.

My knowledge of internet business was a scatty array of:

  • Mark Zuckerberg
  • "venture capital"
  • programer kid internet millionaires
  • Kayak.com — very nice interface!
  • perl.
    Regular Expressions
    11th Grade
  • mIRC
  • TechCrunch
  • There seem to be way more programming going on to impress other programmers than to make the stuff I wanted!
  • I had used Windows, Mac, and Linux (!! Linux! Dang I must be good)
  • I knew that “Java and Javascript are alike the way car and carpet are alike”—but didn’t know a bit of either language.
  • I used Alpine to check my gmail. That’s a lot of confusing settings to configure! And plus I’m checking email in text mode, which is not only faster but also way more cooly nerdy sexy screeny.
  • Object-Oriented, that’s some kind of important thing. Some languages are Object-Oriented and some aren’t.
  • "Python is for science; Ruby is for web"
  • sudo apt-get install
    Sandwich
  • I had run at least a few programs from the command line.
  • I had done a PHP tutorial at W3CSchools … that counts as “knowing a little PHP”, right?

So I knew I didn’t know everything, but it was very hard to quantify how much I did know, how far I had to go.

image

A mediocre picture of some things I knew about at various levels. It’s supposed to get across a more refined knowledge of, for example, econometrics, than of programming. Programming is lumped in with Linux and rich programmer kids and “that kind of stuff” (a coarse mesh). But statistical things have a much richer set of vocabulary and, if I could draw the topology better, refined “personal categories” those words belong to.

Which is why it’s easier to “quantify” my lack of knowledge by simply listing words from the neighbourhood of my state of knowledge.

Unfortunately, knowing how long a project should take and its chances of success or potential pitfalls, is crucial to making an organised plan to complete it. “If you have no port of destination, there is no favourable wind”. (Then again, no adverse wind either. But in an entropic environment—with more ways to screw up than to succeed—turning the Rubik’s cube randomly won’t help you at all. Your “ship” might run out of supplies, or the backers murder you, etc.)

File:2Ddim-L2norm-10site.png

Here are some of the words I learned early on (and many more refinements since then):

  • Rails
  • Django
  • IronPython
  • Jython
  • JSLint
  • MVC
  • Agile
  • STL
  • pointers
  • data structures
  • frameworks
  • SDK’s
  • Apache
  • /etc/.httpd
  • Hadoop
  • regex
  • nginx
  • memcached
  • JVM
  • RVM
  • vi, emacs
  • sed, awk
  • gdb
  • screen
  • tcl/tk, cocoa, gtk, ncurses
  • GPG keys
  • ppa’s
  • lspci
  • decorators
  • virtual functions
  • ~/.bashrc, ~/.bash_profile, ~/.profile
  • echo $SHELL, echo $PATH
  • "scripting languages"
  • "automagically"
  • sprintf
  • xargs
  • strptime, strftime
  • dynamic allocation
  • parser, linker, lexer
  • /env, /usr, /dev,/sbin
  • GRUB, LILO
  • virtual consoles
  • Xorg
  • cron
  • ssh, X forwarding
  • UDP
  • CNAME, A record
  • LLVM
  • curl.haxx.se
  • the difference between jQuery and JSON (they’re not even the same kind of thing, despite the “J” actually referring to Javascript in both cases)
  • OAuth2
  • XSALT, XPath, XML

http://www.financialiceberg.com/uploads/iceberg340.jpg
http://www.emeraldinsight.com/content_images/fig/1100190504002.png


http://www.preventa.ca/images/im_risk_anatomy.jpg

This is only—as they say—“the tip of the iceberg”. I didn’t know a ton of server admin stuff. I didn’t understand that libraries and frameworks are super crucial to real-world programming. (Imagine if you “knew English” but had a vocabulary of 1,000 words. Except libraries and frameworks are even better than a large vocabulary because they actually do work for you. You don’t need to “learn all the vocabulary” to use it—just enough words to call the library’s much larger program that, say, writes to the screen, or scrapes from the web, or does machine learning, for you.)

The path should go something like: at first knowing programming languages ⊃ ruby. Then knowing programming languages ⊃ ruby ⊃ rubinius, groovy, JRuby. At some point uncovering topological connections (neighbourhood relationships) to other things (a comparison to node.js; a comparison to perl; a lack of comparability to machine learning; etc.)

I could make some analogies to maths as well. I think there are some identifiable points across some broad range of individuals’ progress in mathematics, such as:

  • when you learn about distributions and realise this is so much better than single numbers!

    a rug plot or carpet plot is like a barcode on the bottom of your plot to show the marginal (one-dimension only) distribution of data

    who is faster, men or women?
  • when you learn about Gaussians and see them everywhere
    Central Limit Theorem  A nice illustration of the Central Limit Theorem by convolution.in R:  Heaviside <- function(x) {      ifelse(x>0,1,0) }HH <- convolve( Heaviside(x), rev(Heaviside(x)),        type = "open"   )HHHH <- convolve(HH, rev(HH),   type = "open"   )HHHHHHHH <- convolve(HHHH, rev(HHHH),   type = "open"   )etc.  What I really like about this dimostrazione is that it’s not a proof, rather an experiment carried out on a computer.  This empiricism is especially cool since the Bell Curve, 80/20 Rule, etc, have become such a religion.NERD NOTE:  Which weapon is better, a 1d10 longsword, or a 2d4 oaken staff? Sometimes the damage is written as 1-10 longsword and 2-8 quarterstaff. However, these ranges disregard the greater likelihood of the quarterstaff scoring 4,5,6 damage than 1,2,7,8. The longsword’s distribution 1d10 ~Uniform[1,10], while 2d4 looks like a Λ.  (To see this another way, think of the combinatorics.)
  • when you learn that Gaussians are not actually everywhere
    kernel density plot of Oxford boys' heights.

    histogram of Oxford boys' heights, drawn with ggplot.A (bimodal) probability distribution with distinct mean, median, and mode.
  • in talking about probability and randomness, you get stuck on discussions of “what is true randomness?” “Does randomness come from quantum mechanics?” and such whilst ignorant of stochastic processes and probability distributions in general.
  • (not saying the more refined understanding is the better place to be!)
  • A brilliant fellow (who now works for Google) was describing his past ignorance to us one time. He remembered the moment he realised “Space could be discrete! Wait, what if spacetime is discrete?!?!?! I am a genius and the first person who has ever thought of this!!!!” Humility often comes with the refinement.
  • when you start understanding symbols like ∫ , ‖•‖, {x | p} — there might be a point at which chalkboards full of multiple integrals look like the pinnacle of mathematical smartness—
    http://www.niemanlab.org/images/math-formula-chalkboard.jpg
  • but then, notice how real mathematicians’ chalkboards in their offices never contain a restatement of Physics 103!
    Kirby topology 2012
    http://whatsonmyblackboard.files.wordpress.com/2011/06/21june2011.jpg
    A parsimonious statement like “a local ring is regular iff its  global dimension is finite” is so, so much higher on the maths ladder than a tortuous sequence of u-substitutions.
  • and so on … I’m sure I’ve tipped my hand well enough all over isomorphismes.tumblr.com that those who have a more refined knowledge can place me on the path. (eg it’s clear that I don’t understand sheaves or topoi but I expect they hold some awesome perspectives.) And it’s no judgment because everyone has to go through some “lower” levels to get to “higher” levels. It’s not a race and no one’s born with the infinite knowledge.
 

I think you’ll agree with me here: the more one learns, the more one finds out how little one knows. One can’t leave one’s context or have knowledge one doesn’t have. And all choices are embedded in this framework.




The origins of mass & the feebleness of gravity by Frank Wilczek

tl,dr:

  • dark matter & dark energy
  • "Even though protons, neutrons, and electrons comprise only 3% of the universe’s mass as a whole, I hope you’ll agree that it’s a particularly significant part of the mass." lol
  • "Just because you can say words and they make sense grammatically doesn’t mean they make sense conceptually. What does it mean to talk about ‘the origin of mass’?”
  • "Origin of mass" is meaningless in Newtonian mechanics. It was a primitive, primary, irreducible concept.
  • Conservation is the zeroth law of classical mechanics.
  • F=MA relates the dynamical concept of force to a kinematic quantity and a conversion factor (mass).
  • rewriting equations and they “say” something different
  • the US Army field guide for radio engineers describes “Ohm’s three laws”: V=IR, I=V/R, and a third one which I’ll leave it as an exercise for you to deduce”
  • m=E/c²
  • Einstein’s original paper Does the inertia of a body depend on its energy content? uses this ^ form
  • You could go back and think through Einstein’s problem (knowing the solution) in terms of free variables. In order to unite systems of equations with uncommon terms, you need a conversion factor converting a ∈ Sys_1 to b ∈ Sys_2.
  • Min 13:30 “the body and soul of QCD
    http://www.lightnessofbeingbook.com/LOBColorPlates/images/Plate01_ThreeJet.jpg
    http://www.fnal.gov/pub/today/images11/CMSResult042211figure1.jpg
    image
    img_lrg/jet.jpg not found

  • Protons and neutrons are built up from quarks that are moving around in circles, continuously being deflected by small amounts. (chaotic initial value problem)
  • supercomputer development spurred forward by desire to do QCD computations
  • Min 25:30 “The error bounds were quite optimistic, but the pattern was correct”
  • A model with two parameters that runs for years on a teraflop machine.
  • Min 27:20 The origin of mass is this (N≡nucleon in the diagram): QCD predicts that energetic-but-massless quarks & gluons should find stable equilibria around .9 GeV:
    Full-size image (27 K)

    http://www.yorku.ca/lewisr/images/Yspectrum.png
    image
    Or said alternately, the origin of mass is the balance of quark/gluon dynamics. (and we may have to revise a bit if whatever succeeds QCD makes a different suggestion…but it shouldn’t be too different)
  • OK, that was QCD Lite. But the assumptions / simplifications / idealisations make only 5% difference so we’ll still explain 90% of the reason where mass comes from.
  • Computer ∋ 10^27 neutrons & protons
  • The supercomputer can calculate masses, but not decays or scattering. Fragile.
  • Minute 36. quantum Yang-Mills theory, Fourier transform, and an analogy from { a stormcloud discharging electrical charge into its surroundings } to { a "single quark" alone in empty space would generate a shower of quark-antiquark virtual pairs in order to keep a balanced strong charge }
  • Minute 37. but just like in QM, it “costs” (∃ a symplectic, conserved quantity that must be traded off against its complement) to localise a particle (against Heisenberg uncertainty of momentum). And here’s where the Fourier transform comes in. FT embeds a frequency=time/space=locality tradeoff at a given energy (“GDP" in economic theory). The “probability waves" or whatever—spread-out waveparticlequarkthings—couldn’t be exactly on top of each other, they’ll settle in some middle range of the Fourier tradeoff.
  • "quasi-stable compromises"
  • This is similar to how the hydrogen atom gets stable in quantum mechanics. Coulomb field would like to pull the electron on top of the proton, but the quantum keeps them apart.
    http://upload.wikimedia.org/wikipedia/commons/thumb/5/55/Bohr-atom-PAR.svg/500px-Bohr-atom-PAR.svg.png
    http://upload.wikimedia.org/wikipedia/commons/a/a4/HydrogenOrbitalsN6L0M0.png
  • "the highest form of musicality"
  • Quantum mechanics uses the mathematics of musical notes (vibrating harmonics).
  • Quantum chromodynamics uses the mathematics of chords, specifically triads since 3 colour forces act on each other at once.
    image
    image
  • Particles are nothing more than stable tradeoffs that can be made between localisation costs (per energy) from QM and colour forces.
  • (Aside to quote Wikipedia: “Mathematically, QCD is a non-Abeliangauge theory based on a local (gauge) symmetry group called SU(3).”)

  • Minute 40. Because the compromises can’t be evened out exactly due to quanta, there’s some leftover energy. It’s the same for a particular kind of quark-gluon interaction (again, because of the quanta). The .9 GeV overshoot | disbalance | asymmetry in some particular quark-gluon attempts to balance creates the neutrons and protons. And that’s the origin of mass.

Minute 42. Feebleness of gravity.

  • (first of all, gravity is weak—notice that a paperclip sticks to a magnet rather than falling to the floor)
  • (muscular forces are the result of a lot of ATP conversions and such. That just happens to be even weaker—but if you think of how far removed those biochemical electropulses and cell fibres are from the fundamental foundation, maybe that’s not so surprising.)
  • Gravity is 40 orders of magnitude weaker than the electrical force. Not forty times, forty orders of magnitude.
  • Planck’s vision; necessary conversion; a theory of the universe with only numbers.
  • The Planck distance, even for nuclear physicists, is about 20 orders of magnitude too small.
  • The clunkiness of Planck’s constants mocks dimensional analysis. “If you measure natural objects in natural units, you should get something of the order of unity”.
  • "If you agree that the proton is a natural object and the Planck scale is a natural unit, you’d be off by 18 orders of magnitude".
  • Suppose gravity is a primitive. Then the question becomes: “Why is the proton so light?” Which now we can answer. (see above)
    http://www.grin.com/object/external_document.243440/7414ac37090135af37b2813c60318981_LARGE.png
    http://www-cdf.fnal.gov/physics/new/qcd/diphoton_2012/diphoton_xsec_10fb/plots/TXSec_massQLSSV01_07Jun12.gif
    http://cms.web.cern.ch/sites/cms.web.cern.ch/files/styles/large/public/field/image/Fig3-MassFactSoBWeightedMass.png
  • Simple physics (local interactions, basic = atomic = fundamental = primitive behaviours) should occur at Planck scales. (More complex behaviours then should “emerge” out of this reduction.)
  • So that should be, in terms of energy & momentum, 10^18 proton masses, where the fundamental interactions happen.
  • The value of the quark-gluon interaction at the Planck scale. “Smart” dimensional analysis says the quantum level that makes protons from the gluon-quark interactions then gets us to ½, “which I hope you’ll agree is a lot closer to unity than 10^−18”.
  • Minute 57. “A lot of what we know about the deep structure of the Standard Model is summarised on this slide”
  • weak force causes beta decay
  • standard model not so great on neutrino masses
  • SO(10)’s spinor representation has all the standard model’s symmetries as subgroups
  • Minute 67. Trips my regression-analysis circuits. Slopes & intercepts. Affine!
  • Supersymmetry would have changed the clouds and made everything line up real nicely. (The talk was in 2004 and this week, in 2012, the BBC reported that SuSy was kneecapped by the latest LHC evidence.)
  • "If low-energy supersymmetry turns out to be false, I’ll be very disappointed and we’ll have to think of something else."

(Source: mit.tv)




As to longitude, I declare that I found so much difficulty in determining it that I was put to great pains to ascertain the east-west distance I had covered. The final result of my labours was that I found nothing better to do than to watch for and take observations at night of the conjunction of one planet with another, and especially of the conjunction of the moon with the other planets….

After I had made experiments many nights, one night, the twenty-third of August 1499, there was a conjunction of the moon with Mars, which according to the almanac was to occur at midnight or a half hour before. I found that…at midnight Mars’s position was three and a half degrees to the east.

Amerigo Vespucci

Good gosh. Can you imagine having travelled so far on the globe—without a swift means of return, of course—that you literally had no idea where you were?

And what’s more, science to save you. You can’t ask anyone around you for the answer. Many of the people around you not only have never heard of Europe, but can’t even conceive of such a thing.

Nobody knows the answer. ∄ books that purport to have the answer. ∄ communication channels back to home. You’re all alone, mentally. To figure out what’s going on all you have to go on is reason and facts. And if you get the answer right, whom are you going to tell?

(Source: Wikipedia)




Mathematicians will never have enough time to read all the discoveries in Geometry (a quantity which is increasing from day to day and seems likely in this scientific age to develop to enormous proportions) if they continue to be presented in a rigorous form according to the manner of the ancients.

Christiaan Huygens, AD 1659

(from John Stillwell p 146)




The seeds of my dissent from economic orthodoxy were pretty much sown for me by my 1st professor on the 1st day of my 1st economics class.

This prof had gone to a great personal trouble to begin our exposure to the dismal science with a very down-to-earth and super-important lesson. She went so far as to spend her own cash on some things from the store, of varying cost, and gave us all at the beginning of the class random items. Some people got candy, some got socks, one or two got things of greater value.

This was a masterful teaching stroke, by someone who cared deeply about her subject and teaching it to newbies: she would have us all participate in voluntary trade within the classroom and end up than we started. Gains from trade—the fundamental point about economics—are really “the only thing we know about welfare”. Sure, some people start off with more—more wealth, more smarts, better looks, genes that will make them grow taller so they can reach the mayonnaise jars from the back—but hey, at least we can make all of them better off and not hurt anyone by allowing them to trade freely.

Right?

We each reported, on a scale of 1 to 10, how satisfied we were with the Stuff we had been randomly given at the beginning of the class, and the prof wrote these scores down on the board. Then we were asked to stand up, walk about the room, and see if anyone would voluntarily exchange Stuff with us. Multiple transactions were allowed, even encouraged—and after a few minutes of cluelessly blitzing with each other, the trading day was closed and we resumed our seats.

The prof asked our scores again, fully expecting that ∀i in the class, utility before utility after.

But one girl reported a lower score.

Instead of taking this as evidence against her belief that transactions are always mutually beneficial—a cornerstone of normative economic theory—the prof instead scolded the girl. "Well, what’d ya do that for?!”

By the way, this was not a prof who prepended test questions with the phrase “According to the theory we learned in class,” which means I still dispute that I got that one about the lobstermongers right! (Since it asked about “What would happen” not “what the theory says would happen”.)

At the time I thought the outburst a bit rude and over the years to come I remembered the episode. (well, obviously) I still think of it as a microcosm of certain intellectual misdeeds by economists. The framework is too important to hold onto; if anyone undermines then you get angry and yell at them! It’s a plausibility war, after all.

Not too far off from real comments by economists: But if you took away the mutually-beneficial assumption, then you’d have no theory at all! (Regardless of whether nullset is the only true theory we have.)

The assumptions about what goes on in transactions are so appealing that even when you see them violated in front of your eyes, they’re still so implausible and—hey—what about all this stuff I learned about indifference curves? If I saw so many graphs with them not overlapping or going backwards, then that has to be the truth, because maths!

Nevermind that people don’t always know what they want, or maybe it’s contradictory or impossible, and even in well-defined classroom experiments they may just, um, do it wrong.

Happy Independence Day. Here’s to hoping you don’t use the independence to shoot yourself in the foot.




Why the scientists aren’t combining data from two experiments to get 5-sigma evidence (= “proof”) for the Higgs boson

The Higgs field was postulated nearly 50 years ago, the LHC was proposed about 30 years ago, the experiments have been in design and development for about 20 years, and we’ve been taking data for about 18 months. Rushing to get a result a few weeks early [would be dumb].


The reason we have two experiments at the LHC looking for the Higgs boson is because if one experiment makes a discovery then the other experiment can confirm or refute the discovery. This is why we have both D0 and CDF, both Belle and BaBar, both ATLAS and CMS, both UA2 and UA1

Why the scientists aren’t combining data from two experiments to get 5-sigma evidence (= “proof”) for the Higgs boson

The Higgs field was postulated nearly 50 years ago, the LHC was proposed about 30 years ago, the experiments have been in design and development for about 20 years, and we’ve been taking data for about 18 months. Rushing to get a result a few weeks early [would be dumb].

The reason we have two experiments at the LHC looking for the Higgs boson is because if one experiment makes a discovery then the other experiment can confirm or refute the discovery. This is why we have both D0 and CDF, both Belle and BaBar, both ATLAS and CMS, both UA2 and UA1


hi-res




We [cannot] persist in seeing Leonardo [da Vinci] as an artist on the one hand and a scientist and technologist on the other. The common response is to suggest that he recognised no divisions between the two….

This doesn’t quite hit the mark, however, because it tacitly accepts that ‘art’ and ‘science’ had the same connotations in Leonardo’s day as they do now. What Leonardo considered arte was the business of making things. Paintings were made by arte, but so were the apothecaries’ drugs and the weavers’ cloth.

…the people who made [paintings] were tradesmen paid to do a job, and manual workers at that. Leonardo … strove to raise the status of painting so that it might rank among the ‘intellectual’ or liberal arts, such as geometry, music, and astronomy.

Scienza, in contrast, was knowledge—but not necessarily that obtained by … experiment…. Medieval scholastics had insisted that knowledge was what appeared in the books of Euclid, Aristotle, Ptolemy, and other ancient writers, and that the learned man was one who had memorized these texts.


The celebrated humanism of the Renaissance did not challenge this idea but merely refreshed it, insisting on returning to the original sources rather than relying on Arabic and medieval glosses.

Philip Ball, Flow: Nature’s Patterns

Tie-in 1: During my lifetime, progress is taken for granted.

  • "The pace of technological change is getting faster and faster"
  • "We can’t keep up with our own progress!"
  • "The world is more interconnected and dynamic than ever"
  • "Growth just happens"
  • "Equities for the long run"

But during the first millennium ano domini, and pretty much until the Renaissance, it was generally assumed (by European Christians) that the World was in a Fallen State. That we left Eden long ago, and that now the very bones of the Earth were decayed and decrepit. (according to Tom Holland)

Tie-in 2: In an upcoming article I’m going to pull out some of the best quotes from discussion of the Galileo Affair on Thony Christie’s website. My history of science was wrong.




  1. X
  2. ???
  3. Profit!




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.