Missing, presumed

Sophie Mattisse’s paintings look familiar, but can be hard to place – until one realizes that they are famous works from which the people have been removed. I heard about her work in a piece (MP3) on the Studio 360 radio show. (Read more in an artnet article; the Feigen gallery has more images.)

It led me to think about what text would be like, stripped of people.

Here are the first two chapters of Jane Austen’s Pride and Prejudice, with the dialog removed:

It is a truth universally acknowledged, that a single man in possession of a good fortune must be in want of a wife.

However little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered as the rightful property of some one or other of their daughters.

Mr. Bennet was among the earliest of those who waited on Mr. Bingley. He had always intended to visit him, though to the last always assuring his wife that he should not go; and till the evening after the visit was paid, she had no knowledge of it. It was then disclosed in the following manner.

Mrs. Bennet deigned not to make any reply; but unable to contain herself, began scolding one of her daughters.

Mary wished to say something very sensible, but knew not how.

This is not quite the same as Matisse’s work, since references to people remain. If one keeps only sentences which don’t refer to people, very little remains. Here are the first five chapters, so abridged:

It was then disclosed in the following manner.

Nothing could be more delightful!

Such amiable qualities must speak for themselves.

The distinction had perhaps been felt too strongly.

Joseph Conrad’s Heart of Darkness is a very different book; much remains of the first few pages when references to people are removed. This kind of excision is a way to characterize a novel. Little will remain for novelists who are concerned with people; much will remain for those concerned with setting.

The Nellie, a cruising yawl, swung to her anchor without a flutter of the sails, and was at rest. The flood had made, the wind was nearly calm, and being bound down the river, the only thing for it was to come to and wait for the turn of the tide.

The sea-reach of the Thames stretched before us like the beginning of an interminable waterway. In the offing the sea and the sky were welded together without a joint, and in the luminous space the tanned sails of the barges drifting up with the tide seemed to stand still in red clusters of canvas sharply peaked, with gleams of varnished sprits. A haze rested on the low shores that ran out to sea in vanishing flatness. The air was dark above Gravesend, and farther back still seemed condensed into a mournful gloom, brooding motionless over the biggest, and the greatest, town on earth.

Afterwards there was silence on board the yacht.

The day was ending in a serenity of still and exquisite brilliance. The water shone pacifically; the sky, without a speck, was a benign immensity of unstained light; the very mist on the Essex marshes was like a gauzy and radiant fabric, hung from the wooded rises inland, and draping the low shores in diaphanous folds. Only the gloom to the west, brooding over the upper reaches, became more somber every minute, as if angered by the approach of the sun.

Forthwith a change came over the waters, and the serenity became less brilliant but more profound. The old river in its broad reach rested unruffled at the decline of day, after ages of good service done to the race that peopled its banks, spread out in the tranquil dignity of a waterway leading to the uttermost ends of the earth.

This exercise set me wondering: What would be left if one drained the venom from political speech? This is harder to do; what should be cut? Only personal attacks and sarcasm, or snide remarks and irony, too?

I tried it out on 'Chocolate City' Sprinkled With Nuts, picked at random from the Ann Coulter archive on townhall.com, by keeping only what seemed to be factual statements. The original 807 words boiled down to these 81:

So Hillary Clinton thinks the House of Representatives is being "run like a plantation." And, she added, "you know what I'm talking about."

As Hillary explained, the House "has been run in a way so that nobody with a contrary view has had a chance to present legislation, to make an argument, to be heard."

His mother immediately told the press, "Of course he's against abortion."

He had expressed support for the Reagan administration's positions on abortion in a 1985 memo.

No, taking out the ad hominem takes out all the fun – which can’t, thankfully, be said for Sophie Matisse’s work.

Are experienced meditators better investors?

People often make bad investment decisions, at least by the lights of classic economic models. They become attached to the status quo, whether good or bad. In a classic experiment, students given coffee mugs worth $6 wouldn’t sell them for less than $5.25, but those who hadn’t received mugs wouldn’t buy them for more than $2.75. Since the recipients were chosen at random, one would expect that the very same people who wouldn’t part with a mug for more than $5 would be unwilling to pay even $3 for the same object. The only difference was ownership.

Applied to investment, this means that investors tend to be unwilling to liquidate a losing position in a market, even though it would be the “rational” thing to do. We become attached to things, to an unwise degree.

A related behavior is loss aversion: people tend to strongly prefer avoiding losses over acquiring gains. It’s not just people; an experiment with capuchin monkeys showed the same behavior. The monkeys were asked to choose between spending a token on one apple, though half the time the experimenter gave them two instead of one; or to spend it on two visible apples, when half the time the experimenter only gave them one. Economic theory predicts that rational consumers should not care which of these outcomes they receive since they are essentially both 50-50 shots at one or two pieces of food. The capuchins, however, vastly preferred the first gamble, which is essentially a half chance at a bonus, than the second gamble, which is essentially a half chance at a loss. Just like humans, they avoided the possibility of a loss.

The Buddha taught that suffering is caused by attachment – this is the second of the “Four Noble Truths”. Meditation training seeks to weaken the cravings and aversions which are the symptoms of attachment. If such training is effective, then experienced meditators should show less loss aversion and status quo bias than average people.

Neuroscientists have become interested in studying how meditation affects the brain. For example, experimenters at Massachusetts General Hospital in Boston have found that meditating increases the thickness of the cortex in areas involved in attention and sensory processing. I look forward to experiments that investigate the intersection of meditation and economics.

100% factual, 100% false

There’s a huge billboard on the 405 in Los Angeles advertising Bacardi and Diet Coke as

0 Carbs, 0 Sugar

Even if this were a true statement, it is still wonderfully misleading: alcohol (which has neither carbs nor sugars) still has tons of calories.

¡Verdad Libre!

Africa's farms need knowledge workers

You need land, water and fertilizer to grow crops – but more than that, you need know-how. Glyvyns Chinkhuntha is a Malawian accountant-turned-farmer who has used insight and determination to create an idyllic farm in an area where most residents haven’t had a good meal since June.

The Christian Science Monitor reports [1] that Chikhuntha uses low-tech methods like plots two feet below ground level (putting roots closer to the water table) and narrow channels the width of a hoe (so that water can be redirected by moving a clod of dirt) to succeed where big-dollar donor programs have failed.

Here’s the Monitor’s analysis:

What prevents more African farmers from using such a system? Sitting under a shade tree, clad in a crisp white oxford shirt and matching baseball cap, Chinkhuntha answers by remembering that, as a child, his father often pointed to hunched-over old farmers and warned, "If you don't go to school you'll end up like that man."

Across Africa, he says, "People go to school to run away from farming." It's a cultural preference with disastrous consequences: The vast majority of Africans consider themselves farmers, but very few have analytical skills, agricultural savvy, or basic resources to produce much food.

More education is one answer. But if the continent's already-educated people picked up farming, "Africa would have plenty to eat," says Chinkhuntha, who was trained as an accountant. Instead, "all the education and knowledge is tucked up in offices" with people who "are not interested in touching the soil."

This confirms that the key factor of production is knowledge, not “land, labor, or capital” (cf. ), but the implication is counter-intuitive: Africa needs more knowledge workers, indeed, but they should be on the farms, not in the cities.

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[1] A home-grown solution to African hunger, Christian Science Monitor, 1 February 2006 (fee may be required)

Hard Intangibles

Imagine a square drawn on a plane surface; now imagine a cube, which is a square in three dimensions. How many edges does the square have? The cube? Now imagine a hypercube, a square in four dimensions – how many edges does it have?

Answering the first two questions is easy, since we can visualize squares and cubes; answering the third is harder, since we can’t imagine four dimensions. Of course, calculating the number of edges for an n-dimensional hypercube is trivial if you know a little maths (it’s n.2^(n-1)).

I divide hard problems into two categories: those that are hard for human intuition to handle (called human-hard problems) and those that are hard for reasons intrinsic to the problem (called plain ol’ hard problems). Figuring the number of edges for an n-dimensional hypercube is human-hard for n bigger than 3, but isn’t plain ol’ hard.

Human-hard Problems

I define human-hard problems as ones that we find hard to solve by intuition (which is partly innate and partly learned).

The entire field of behavioral economics is devoted to ways in which people are not “rational agents” in economic terms. We find it hard to come up with solutions which seem obvious technical perspective of classical economics. Here’s an example from Mullainathan & Thaler’s entry on Behavioral Economics for the International Encyclopedia of the Social and Behavioral Sciences:

An example involving loss aversion and mental accounting is Camerer et al’s (1997) study of New York City taxi cab drivers. These cab drivers pay a fixed fee to rent their cabs for twelve hours and then keep all their revenues. They must decide how long to drive each day. A maximizing strategy is to work longer hours on good days (days with high earnings per hour such as rainy days or days with a big convention in town) and to quit early on bad days. However, suppose cabbies set a target earnings level for each day, and treat shortfalls relative to that target as a loss. Then, they will end up quitting early on good days and working longer on bad days, precisely the opposite of the rational strategy. This is exactly what Camerer et al find in their empirical work.

This mismatch between the behavior of humans and the mythical Homo economicus wouldn’t matter if economics were only a model. However, society has used the models of classical economics to create artifacts like financial markets and lotteries where the mathematics, not human experience, is the defining principle. Human biases like anchoring, risk aversion, confirmation bias, false consensus effect, the illusion of control have real-world consequences in such worlds. Acting rationally in places premised on classical economics is human-hard.

To take another example of human-hard problems, Kurzweil has argued eloquently (and, in this case, persuasively) that we are unable to grasp the implications of exponential growth because our intuitions lead us to linear extrapolations of technological progress.

Humans also struggle to deal with very small and very large numbers, from grasping the relative sizes of atoms and nuclei, to reasoning about the budget deficit. Dealing with such numbers is not a “plain ol’ hard” problem; we have mathematical notation and tools for dealing with them trivially. However, the problem arises when we try to reason about their meaning using metaphors and concepts operating at human scale.

More difficulties arise when small and large numbers are multiplied, and when non-numerical considerations matter, eg in risk-magnitude assessments. There are notable divergences between expert and public assessment of risk because the public expands the concept of risk to include various non-damage attributes. The very same risk — as experts see these things — would be understood quite differently by the lay public depending on how it weights considerations like familiar/unfamiliar, chronic/acute, or immediate/delayed, which not usually included in quantitative risk assessments.

I suspect that thinking about software and other digital goods are human-hard problems. It’s clear that people behave as if email communications and blog posts are as private and ephemeral as those on paper (see my Dad, how dare you read my xanga?). They treat knowledge as if it’s a physical thing, leading to bad business judgments (see my Extending software patents: Those who live by the sword, die by the sword). I’ll have much more to say on this topic; stay tuned.

Plain ol’ hard Problems

Plain ol’ hard problems are ones that are difficult to solve whether you’re human or not.

One example is solving non-linear equations. When outcomes are exquisitely sensitive to the initial variables, predictions will be lousy (cf chaos theory). That’s why weather forecasts don’t go much further out than three days. Throwing more computer power at the problem doesn’t extend the time scale, though it can give you finer physical resolution (eg hyper-local weather forecasts for a sports venue vs. a whole city). The halting problem in computer science is another example; it’s impossible to provide a general method for deciding whether a computer program will terminate in a finite time.

Gray zones

For now I’m going to skate blithely over the complication that the only problems we can think about are human-conceived problems. The maths we might use to show that a human-hard problem is technically trivial is a human artifact itself. Further, a plain ol’ hard problem may not be intrinsically hard; it may only be hard because the maths we’ve been able to come up with can’t handle it. Factoring large prime numbers is very hard to do as far as we know, and is the basis of many cryptographic systems. However, some mathematical genius may suddenly stumble on a way to do it easily; we just don’t know.

Human-hard problems are difficult to pin down because they’re dependent on formulation. People can analyze complex scenarios very easily when they’re described in terms of people and social power, but they do poorly when they’re abstracted. This is as one would expect – we probably have built-in mechanisms for “calculating” social relationships.

I wouldn’t put dry stone walling or cooking in either of the above two categories. These activities are hard, but learnable. The key distinction for my purposes is that they’re both physical activities; within the limit of variation of hand-eye coordination, most people can learn to do them. By contrast, activities that are mostly intellectual like math and software development show striking divergences between the few really good developers, and the rest. In civil engineering, according to my in-house coastal engineer, good people are better than average people, but by a factor of 2, not a factor of 100s or 1,000s as in software.

Summary

Human beings mostly think about abstract notions in concrete terms, using ideas and modes of reasoning grounded in their senses, muscles, and experience. Our thinking is embodied, that is, shaped by the structure of our brains, our bodies, and everyday interactions in the world. We have developed tools like mathematics for thinking about abstract things like hypercubes, but our intuitions about them tend to be concrete. As the knowledge economy fill our world with more abstractions, I would expect the number of human-hard problems to multiply.

Dad, how dare you read my xanga?

Kids are using blogs in ways that make their parents’ and teachers’ hair stand on end [1]. They give out personal information to all comers, post sexy pictures of themselves, and make racist comments that they’ll regret in a job interview ten years down the line.

One would have thought that the Internet generation would be more savvy about the medium they’re grown up with. According to a study by the Pew Internet & American Life Project (PDF) more than half of teens aged 12-17 create content for the Internet, and about 1 in 5 keep a blog [2]. However, even kids behave as if the Internet is a physical medium, rather than a universally accessible and indelibly permanent abstraction. It shows that the innate mental models we’ve evolved over millennia in small social groups will trump an intellectual understanding of the digital world – even among the young.

Some excerpts from the Monitor story illustrate the point. Kids model their web communications as taking place in physical proximity with physical means, where privacy can be assumed:

"The key thing is that young people appear to be totally oblivious to the fact that everything they post in these sites is public, permanent, accessible from throughout the world, and easily transmittable to anybody," says Nancy Willard, director of the Center for Safe and Responsible Internet Use in Eugene, Ore. When adults read the sites, "teens argue that you're invading my privacy," Ms. Willard says. "That's just the point. It's not private."

.....

"Kids used to pass notes around in school," says Parry Aftab, director of Wiredsafety.org. "Now they're putting it onto pages with 42 million users."

They are also modeling the web as being as ephemeral as the physical world, and having the same short memory that people do:

Bernard Piel, a history teacher and assistant to the dean at Norman Thomas High School in New York, recently talked to a student who posted provocative photos. "I want you to imagine that you're 24 years old, you're trying to get a job somewhere, HR does a background check, and these things come up," he told her.

It turns out that it’s not just 40-something executives that have to be constantly reminded not to write anything they wouldn’t want to see on the front page of the New York Times.

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[1] Schools grapple with policing students' online journals, Christian Science Monitor, 2 February 2006 (fee may be required for archive content)

[2] The Monitor points out that kids don’t call them blogs. Rather, they use their brand names: Xanga, MySpace, LiveJournal, and Facebook are the most common.

Software’s knowledge architecture

Picture a software product as lots of boxes connected by lines; each box is a chunk of code, and the lines are inter-dependencies. Now imagine that you have a pot of red paint, which is the set of claims of some software patent. How would you color the block diagram?

For some patents, all the claims might fall inside one module. You’d paint one block is bright red, but the rest remain white. For other patents, the claims may be so general that all the blocks are some shade of pink.

It would be much easier to work around an infringement claim in the case with only one red block. If all the blocks are pink, every part of the product would infringe, and it would be harder to avoid paying licensing fees.

Of course, more than one patent is probably involved: you now have pots of many colors, let’s say red, yellow and blue. Some blocks may be a single color, while others are various rainbow shades.

A color pattern gives one a sense of how patents map to a product. It will show areas where just one patent is in play, and modules where there are many.

One can play a similar game with product families and groups of patents. Now each box represents a product, and each paint color is a patent owner. A manufacturer could then tell for which parts of their line-up a they would have to strike licensing deals, and where they control their intellectual property destiny.

Patents themselves have an architecture, too, and can be represented as block diagrams: they depend on each other, and can be nested one within the other. One can imagine an overlay where a patent block diagram is distorted to map onto a software block diagram. A very distorted mapping is equivalent to kaleidoscopic coloring.

A range of questions present themselves:

1. Can one divide patents into categories: those whose scope is limited to a module, vs. those who cover the whole product?
2. Are these categories specific to a product, and/or tied to a type of patent?
3. Do different kinds of software have characteristic “patent colorings”?
4. Are there regularities in how patents, or categories of patents, apply to software designs? Can one use this information to guide a software architecture to be minimally at risk of infringement – perhaps even in advance of knowing what the specific outside patents might be?
5. There are other industries where products contain a large number of patentable elements; for example, computer and telecommunications equipment. Are there systematic differences between the patent/product mappings for different industries?
6. Specifically, does the highly interconnected nature of software, and/or its intangibility, lead to differences in the way patents are structured relative to products compared to physical goods?

It is difficult to do such an analysis in practice because of the willful infringement rule: if you are aware of a patent and are then later found to have infringed on it, a court can award punitive triple damages. Consequently, companies are reluctant to know too much about patents which might apply to their products. The people who are best placed to assess whether a patent applies to code, the developers, are those who create the biggest risk to their employers by making this very assessment. For example, Open Source Risk Management (OSRM) declined to list the 283 issued, but not yet court-validated, software patents in the Linux kernel which it announced in August 2004, because it would expose Open Source developers to litigation risk.

One way to work around this difficulty is to swap figure and ground, that is, to map pieces of knowledge that are in the public domain. For example, one could look for commonly used algorithms, or the distribution of important software ideas, like stacks, semaphores, security algorithms.

In fact, patents are just a useful crutch in formulating this problem. My core goal is to understand the structure of knowledge as embodied in software. Patents, for all their faults, are a well-understood way of delimiting pieces of knowledge that are used in products. Ultimately one wants to explore the generality of the problem by replacing “patent” with “any knowledge chunk” in the above discussion.