Archive for May, 2012

May 31, 2012

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


Smart Risks

May 31, 2012

One misconception I got from the academic theory of finance is that risk and reward go together. You take on more risk, you get more reward. This is formalised in CAPM theory as a higher expected return associated with a higher standard deviation of investment returns.

In reality, ∃ many stupid risks—mistakes, bad ideas, not doing your homework, believing people you shouldn’t believe, taking on a job without negotiating a floor for your own compensation first, or investing in a company that was bound to tank.

Recently, academics have undercut the premise that risk goes hand-in-hand with reward. Perhaps this pill is easier to swallow after seeing “dumb money in Düsseldorf” vacuum up synthetic CDO pyrite (AAA mortgage bonds) spun from BBB bonds—and then find out, publicly, along with the rest of investment Narnia, that the rewards were nowhere near commensurate with the risks.

I’ve seen this play out a little more in private equity, where models of price paths are less influential than common sense, gut reactions, and balance-sheet research.

I don’t know as much about trading. But I’ve read between the lines on the EliteTrader forum and its cousins, and got the sense that, as academic papers that study the matter report: most day-traders lose money on expectation. Their trading capital approaches $0 faster than would be expected merely by the drag of trading fees on a statistical mean of zero profit.


Warren Buffett, the world’s best living investor, is in a business where risk and reward are inverted from the CAPM model. (He’s written about it plenty so I won’t repeat him.)

Insurance and reinsurance companies, though they may serve a social function, aren’t actually concerned with actuarially converting risk into reward. They’re interested in collecting as many large premia as possible for risks that will never harm their balance sheet. Why do you think they have three times as many claims adjusters as actuaries? Si guarda al fine.

Michael Price, one of the stars of The Vulture Investors, bought a loan to a bankrupt company for 47¢ on the dollar, covered 15¢ immediately with cash, plus 45¢ in bonds plus 23% of the post-bankruptcy company. He needed the bargaining skills and the capital to buy out other bondholders and negotiate a good rate for 

One last classic example: McDonald’s. Ray Kroc saw a huge return on investment but only took smart risks, doing less of the hard work and spending more time being successful. Mr. Kroc didn’t finish college with a bright-eyed hope to be the world’s greatest entrepreneur (cf. YCombinator). He sold Dixie cups for 17 years before he saw an opportunity—in a B2B space—with high returns and low costs. (Selling malt mixing machines back when malts were the profit centre for burger joints—a malt might cost as much as sandwich + fries, or even as much as sandwich+fries+coffee.) The malt mixer business was a classic play; it would earn 100% checkmarks from a Business 101 textbook. Only after Ray Kroc saw another opportunity related to the business he was in, did he buy up the MacDonald Brothers’ restaurant and multiply it out. Again, this is a textbook private-equity move: find a proven business where somebody has completely figured out how to make money hand over fist, such that the only other thing they need is more money. (Obviously this is very different from an entrepreneur with an idea who just wants some money or thinks their failing idea would be saved if only they had more money.) You provide the money and collect the multiplied profits, i.e. you take on the easy part of the problem, negotiate the terms so you get a huge return on solving it, and then you’ve done little work for great reward. That’s a “smart risk”, not a correlation of risk and reward.


We could probably go back and forth with examples of titanic companies. (Sure, Ted Turner threw massive sums into a money pit for over a decade before seeing TNT and its siblings become profitable.)

But still I think the overall message of risk~reward is wrong. There are smart risks, and there are dumb risks. Don’t expect that just because you did something risky, that the return will be good. Work smart, not hard. Cover your *rse and check yourself before you wreck yourself.

May 30, 2012

In loving Care by Lisa G Bauer

May 30, 2012


An installation by Barbara Kruger near Los Angeles. via wobbles

May 29, 2012

I’m tired of things improving incrementally through experience. I just want to achieve a symbolic success and then ride off into the sunset….

The problem, though, is that sunset turns into night, and then the next day is just a regular day again, and you still have to cut your toenails and save up for retirement.

Josh Gondelman

May 29, 2012

What is the world made of?There are twelve basic building blocks.

Six of these are quarks—- they go by the interesting names of up, down, charm, strange, bottom and top. (A proton, for instance, is made of two up quarks and one down quark.) The other six are leptons—- these include the electron and its two heavier siblings, the muon and the tauon, as well as three neutrinos.

There are four fundamental forces in the universe: gravity, electromagnetism, and the weak and strong nuclear forces. Each of these is produced by fundamental particles that act as carriers of the force…: …photon…graviton…eight…gluons…three…W+, … W- , … Z.

The behavior of all of these particles and forces is described with impeccable precision by the Standard Model, with one notable exception: gravity.

Alberto Güijosa

May 28, 2012

The Equation of Life

  • s/rent/mortgage/ or
  • s/rent/landowner's share of tenant's crops/
  • et cetera, et cetera, mutatis mutandi, et ceteraaaa, et ceteraaa

May 28, 2012

@IgorCarron blogs recent applications of compressive sensing and matrix factorisation every week.

(Compressive sensing solves underdetermined systems of equations, for example trying to fill in missing data, by L₁-norm minimisation.)

This week: reverse-engineering biochemical pathways and complex systems analysis.

May 28, 2012

May 27, 2012

Why is it that the standard of proof in software development theory is “Some famous person said it’s cool and I can think of an analogy to a Malcolm Gladwell book so therefore i’m right”?

  • Standards of proof in medicine—scurvy, limeys
  • Control groups and test groups.
  • Yes, all studies are flawed.
  • My Comment: The difference between nitpicking and valid criticism of a study is this. Any study is going to miss some data points (patchy, piecemeal coverage of the space we’re interested in) and the measurements are going to be wrong ε. ε may even be piped through a function (automorphism) such that the whole study is ruined. (for example you measured the wrong people or you measured the wrong way or you introduced bias or you measured a special proper subset but wrongly generalised the special property to the general case)

    We know this going in. The challenge is to distinguish ε’s that are deadly to the conclusion from epsilon;’s that are inconvenient and imperfect but don’t phase-change the conclusion. (think bounded perturbation versus sign change)

    This is why it’s nice to be able to make arguments using order-of-magnitude or sign. If you observe multiple orders of magnitude difference in beta; between the control group and the test group, then as long as your sample wasn’t horrifically terrible, the conclusion that A>B is still going to hold up.

  • Rank speculation versus data.
  • Look at the data yourself. Do you see a connexion between vaccination and autism?
  • “It’s almost impossible to get a paper published in a top journal without having actually tried it in the real world.”
  • “All the work that’s been done to date on [estimating software development time] is pretty much worthless. … The engineers are just going to tell us what they think we want to hear.” (or their answer will be driven by anchoring effects)
  • Garbage in, garbage out. (He gives an example where some garbage estimates of how long a software project would take are used as the base data, before being thrown through a gleaming shimmering algorithm that makes them look meaningful.)
  • “If [developers are more accurate estimating how long a project will take on an hours-scale than a months-scale,] then that’s a powerful argument for using agile methods. I’ve heard many people make that argument, but we don’t have any data to back it up.
  • You’ve all heard “Great programmers are 28 times more productive than OK programmers. Or 40, 50, 100. I just pick a number that’s big enough to be impressive but not so large that you’re going to doubt me.”
  • My question: I would love to see a #BigData person scrape the web for all such claims.
  • “All of the claims you’re reading about the relative productivity of programmers can be traced back to: 12 people in 1968 [batch programming] for an afternoon, or 54 people [in the 1980’s] for an hour. How confident are you in those claims now?
  • “The best programmer I ever met — his only higher education was two years at a rabbinical college.”
  • Claims he could take $150M in research money and make 5% of $1,000B. (A classic appeal to the “No number can be less than 1%” theorem. Textbook VC-pitch logic.)
  • Klaus _____; “Regardless of language” (scheme, assembly, java, python, …) “programmers produce the same number of lines-of-code per hour.”
  • Boehm 1975: “Most bugs are introduced in the design & analysis phase.”
  • “The sooner a bug is caught in the development process, the less it costs to fix” (he seems to indicate the cost growth is exponential with time)
  • “Adding a feature doubles production time. (Conversely, if the developers can say “no” to a few features, development time goes down convexly.)”
  • “If you have to rewrite more than 25% of the code, you’re better off starting from scratch.”
  • Minute 33: “Hour for hour, the fastest way to fix code is to read it. Not to run it. Not to write unit tests. Sitting down and having someone else go through your code. 60%-90% of bugs can be removed before the code is even run.”
  • “But it turns out that most of the value comes from the first reader in the first hour of looking at the code.” That’s a couple hundred lines.
  • Conway’s Law is true.
  • A big-data statistical analysis of how Windows Vista was built, trying to find regressors that predict the fault rate.
  1. Physical co-location of programmers did not matter.
  2. Distance in organisational chart does matter.
  • Stereotypical anti-religious view of scientific progress. (“The difference between science and religion is…”). Blah. Not very evidence-based in an evidence-based lecture.
  • “Ask a successful start-up what’s the reason for their success and they’re going to get it wrong.” Up to academics to figure out what makes for success across various cases. (I agree; this is the point of management science. And he mentions the Harvard Business Review as a touchstone.)
  • Rob Pike: “I don’t think I’ve ever seen beautiful code.”
  • Typical self-perception of a rich programmer / white collar / professor. Whatev.
  • Some words about becoming an adult. Someday there will be no higher authority you can appeal to other than the people who were 18 when you were.
  • Making decisions when nobody knows the necessary information. (Seems out of place in a talk where he acts like his views are all backed-up by data. Whatever, though. I think we can all agree that being right is better than being wrong and that one should change one’s opinion whenever “objective evidence says you’re wrong” (whatever that means). And, obviously, as a leader you usually don’t get as much information as you would like—but you have to decide something anyway.)
  • The difference between the Bolsheviks and the Trotskyists is that whilst Bolsheiks believe the masses have to take to the streets to effect change, Trotskyists believe a handful of focussed people who get on the right committees can change the world. Examples: the teaching of evolution; abortion. Don’t write a blog (oops) or start a Facebook group to effect political change, go to the pressure point. Put on a suit, try to sound like an adult, and make your case calmly and rationally to the people in charge.

Hat tip to @gnycl.