Jobs, Thiel, and Stasis Theory

One way to win an argument is to change its stasis. The stasis of an argument is the mental category that it fits into. Some common examples of stases are fact (“Did you come in after curfew?”), definition (“Was the curfew 11 or 12?”), quality (“I have a good reason…”), policy (“What is the appropriate response?”), or jurisdiction (“Who should decide?”).

Stasis theory is commonly taught in law school; the ability to change the type of argument you are engaged in can be the difference between a win and a loss in the courtroom. Peter Thiel’s class notes exemplify how setting the context is important in discussions of monopoly. Most entrepreneurs would rather have a monopoly than to compete in the market, he argues, but you have to convince the government that you are not a monopoly:

One problem is that if you have a monopoly, you probably don’t want to talk about it…. You don’t just not say that you are a monopoly; you shout from the rooftops that you’re not, even if you are.

But since blatant lies can be undermined by the truth, you have to be a little more clever:

Let’s drill down on search engine market share. The big question of whether Google is a monopoly or not depends on what market it’s in. If you say that Google is a search engine, you would conclude that it has 66.4% of the search market….

But suppose you say that Google is an advertising company, not a search company. That changes things. U.S. search advertising is a $16b market. U.S. online advertising is a $31b market. U.S. advertising generally is a $144b market. And global advertising is a $412b market. So you would conclude that, even if Google dominated the $16b U.S. search advertising market, it would have less than 4% of the global advertising market. Now, Google looks less like a monopoly and more like a small player in a very competitive world.

By changing the context of the argument, you can leverage the truth in your favor. This was a lesson Steve Jobs knew well. When the iPhone 4 was released, the metal case caused a problem with the antenna, which led to dropped calls. The problem quickly got out of hand, and Jobs returned from a family vacation in Hawaii to do damage control:

At the press event that Friday… [Jobs] did not grovel or apologize, yet he was quick to defuse the problem by showing that Apple understood it and would try to make it right. Then he transformed the framework of the discussion, saying that all cell phones had some problems…. He captured it in four short, declarative sentences: “We’re not perfect. Phones are not perfect. We all know that. But we want to make our users happy.” (Isaacson biography, p.522)

Not only did Jobs change the context, as Thiel does in the example above, he also shifts the standard to a false perfect/not perfect dichotomy. It’s a rhetorical trick, but it worked: iPhone 4’s were returned at less than one-third the rate that the 3GS model was, despite the antenna problem. It pays to know a little bit about stasis theory.

Steve Jobs and the Value of Time

Steve Jobs’ attitude toward time comes through clearly in the Walter Isaacson biography. This trait seems to have been ingrained well before his years spent suffering from cancer. According to Steve Wozniak, as early as 1985 Jobs acknowledged that his drive to create stemmed from a sense that he might die young. Two particular attitudes toward time show up in the book: respect and focus.

Respect for others was not a trait Jobs had in spades–you were either a genius or an idiot in his dichotomous view of the world–but he respected time. He was particularly reluctant to waste users’s time. Even a small delay becomes immense when aggregated across many users:

One day Jobs came into the cubicle of Larry Kenyon, an engineer who was working on the Macintosh operating system, and complained that it was taking too long to boot up. Kenyon started to explain, but Jobs cut him off. “If it could save a person’s life, would you find a way to shave ten seconds off the boot time?” he asked. Kenyon allowed that he probably could. Jobs… showed that if there were five million people using the Mac, and it took ten seconds extra to turn it on every day, that added up to three hundred million or so hours per year that people would save, which was the equivalent of at least one hundred lifetimes per year. (p. 123)

Jobs also mastered an ability to focus. “Deciding what not to do is as important as deciding what to do,” he said. For example, Jobs implemented this practice when he simplified Apple’s bloated product line down to a simple 2×2 grid (consumer/pro and desktop/portable). Nietzsche also highlighted this trait in Human, All Too Human (1878):

In reality the imagination of the good artist or thinker produces continuously good, mediocre, or bad things, but his judgment, trained and sharpened to a fine point rejects, selects, connects… All great artists and thinkers are great workers, indefatigable not only in inventing, but also in rejecting, sifting, transforming, ordering. (quoted in Jonah Lehrer’s recent Imagine, p. 75)

Placing value on time, then, leads us to cut away the unnecessary.

Sports and the International System

On a recent drive through western North Carolina I heard an interview with James Dodson, author of American Triumvirate (audio). The book is a history of modern golf focusing on three key figures–Sam Snead, Byron Nelson, and Ben Hogan. I am not much of a golfer, but Dodson made a comment that was very interesting. He argued that three dominant players was the perfect number for golf’s popularity: it gave a sense of rivalry and competition while maintaining continuity at the top. (The trio had a combined total of 198 PGA tour wins, including 21 majors.)

A similar question has challenged students of international politics: what number of major-power states is optimal for stability? The answers vary: “unipolar” theorists argue that one powerful state is best, “bipolarity” advocates argue that two major powers can balance each other out, and “multipolar” scholars see an oligarchy of states as the most stable international regime. The question is difficult (if not impossible) to answer due to lack of data–we can only observe one historical record.

But sports gives us another opportunity to explore the question, with far more data points. What number of dominant players/teams is optimal for a sport’s popularity? If Dodson is right, for golf the answer is three. Baseball, football, soccer, and other sports may be most popular with a different number of teams at the top. If the numbers are different, what is it about each sport that makes this so? Is it the structure of the league, the type of play, media and advertising issues, or something else entirely?

Attempting to answer this question presents a few challenges of its own. First, how does one establish who the dominant players/teams are at any given time? Second, is competition data available for the time period of interest? Third, how should we measure the popularity of a sport over time? If these challenges can be overcome, this might make for an interesting and fun research project.

New Feature: Reading Lists

Several colleagues have recently asked me to curate reading lists for them, either to brush up on an unfamiliar subject or as the basis for part of a survey course. I have decided to include these lists on a new page, and to generate new lists from time to time.

To kick off this feature, I have included two lists. The first is a basic overview of economics and economic history. This list is intended for someone who already has a basic familiarity with the topic, but wants to refresh their knowledge or consider some other perspectives. The second is a brief survey of the security studies literature, with multiple options in each sub-category. It is intended as an introduction to the topic at the undergraduate level. Graduate students or professors who use this list should note that many of the works on the list are controversial.

Suggestions for amending the existing lists or topics for future lists are welcome in the comments section.

Wednesday Nerd Fun: Futbol Data

The intersection of sports and social science has been an increasingly interesting place, with books like Soccernomics applying research tools to understand sporting success. Now you can try your hand at this type of analysis, thanks to Manchester City’s release of data from every Premier League game last season. (Clubs keep data on performance from Under-9 teams all the way up to the Premier League). According to the club’s head performance analyst Gavin Fleig, in an interview with The Guardian:

Bill James kick-started the analytics revolution in baseball. That made a real difference and has become integrated in that sport. Somewhere in the world there is football’s Bill James, who has all the skills and wants to use them but hasn’t got the data. We want to help find that Bill James, not necessarily for Manchester City but for the benefit of analytics in football. I don’t want to be at another analytics conference in five years’ time talking to people who would love to analyse the data but cannot develop their own concepts because all the data is not publicly available.

The Guardian‘s excellent DataBlog also did some preliminary analysis of the data, resulting in visualizations like the one above. There’s still a lot of room for improvement, and I would welcome submissions of or links to better visualizations or analyses. You can download the data directly from Manchester City here.

Micro-Institutions Everywhere: Jury Duty

I could have also called this post “The Math of Jury Duty,” but it would have been hard to find two things that more Americans hate more in a single title. Readers who have responded to a jury summons will know how tedious the process can be. If you take the trouble to show up to the courthouse, you could spend the entire day waiting around only to be sent home. Many people would rather find ways to avoid showing up in the first place. This leads to a problem–how many summons does the court need to send?

On the surface the problem seems relatively straightforward. Judges submit their cases weeks in advance, so the district clerk’s office knows what’s coming. If a case needs twelve jurors, and each side can strike up to six people during jury selection, then a total of twenty-four potential jurors are needed.

Unfortunately, the math isn’t quite that easy. Some people who receive a summons are excused for hardship, as in the case of a single parent with young children at home. Others don’t qualify because of criminal records. An individual receiving a summons has the option of postponing the date of service. A summons may be mailed to an incorrect address. In Harris County, the number of returned summonses runs into the double digits — Houstonians tend to move a lot in the three years since their last call to service. And, in spite of the fact it’s a crime, many people simply ignore a summons when it arrives. The numbers add up. Roughly forty percent of people who are sent a summons don’t respond to it.

This leads to a need for “overbooking,” as airlines do with tickets. In fact, according to the post from the University of Houston’s Andrew Boyd, Harris County has to send out three to four times as many summons as they need jurors. (Boyd’s explanation is also available in an audio version.) But the court has the inverse problem of the airlines, since they need to have a minimum number of people whereas the airlines want the maximum number of people.

Further complicating the court’s process is the fact that they cannot compensate people for showing up but not serving in the same way that airlines pay passengers to wait for the next flight. Jurors that respond in Harris County are paid $6 for their first day of service, and $28 thereafter. As commissioner Jerry Eversole said, “At $28 you can pay your parking and have lunch, and that’s pretty much what the jury day consists of.”

So how do the courts get the people they need?

As a rule, courts haven’t reached that level of sophistication [NB: of airline overbooking], but many employ some level of statistical analysis. In Harris County, for example, it’s known that people respond to jury summonses at different rates at different times of the year — something the district clerk’s office takes into account when issuing summonses. And there’s another big cause of uncertainty: settling a case on the courthouse steps. If last minute negotiations lead to a settlement on the day of the trial, there’s no need for any jurors in that court. Even this type of uncertainty can be accounted for in mathematical models, but that’s uncommon.

So the next time you find yourself sitting in the jury assembly room waiting for your number to be called, remember that the courts face a challenging engineering problem with a lot of uncertainty. And don’t forget to bring a good book.

The Politics of Children’s Literature

From Tales for Little Rebels:

From the Puritans to the present day, the didactic tendency of books for young children suggests that adults have no problem prescribing a moral framework for the young. Yet there is the tendency to fear that ‘political propaganda’ will taint a young child’s ‘innocence.’ […] Teaching children to obey a moral authority can be understood as a moral lesson, but it can also be understood as a political lesson.

[Via Brain Picker.]

Wednesday Nerd Fun: Sudoku on the Richter Scale

Sudoku Richter Scale from MIT

I have wanted to write a post on Sudoku for a while now–especially computer programs that can solve puzzles or evaluate solutions. This week’s Nerd Fun post gives me a chance to bring up the topic, thanks to a recent post at Technology Review.

Sudoku puzzles are generally classified as easy, medium or hard with puzzles having more starting clues generally but not always easier to solve. But quantifying the difficulty mathematically is hard.

Now Ercsey-Ravasz and Toroczkai say they’ve worked out a way to do it using algorithmic complexity theory. They point out that it’s easy to design an algorithm that solves Sudoku by testing every combination of digits to find the one that works. That kind of brute force solution guarantees you an answer but not very quickly.

Instead, algorithm designers look for cleverer ways of finding solutions that exploit the structure and constraints of the problem. These algorithms and their behaviour are are more complex but they get an answer more quickly.

The central point of Ercsey-Ravasz and Toroczkai argument is that because an algorithm reflects the structure of the problem, its behaviour–the twists and turns that it follows through state space–is a good measure of the difficulty of the problem.

To quantify the difficulty of Sudoku puzzles, Ercsey-Ravasz and Toroczkai evaluate the complexity of the problem as the solution progresses. A puzzle does not have a static state of difficulty: the solution gets chaotic before it starts to coalesce. The end result is a type of “Richter scale” for puzzles, although it doesn’t go all the way to 10–or even 4, at least not yet.

They say this scale correlates surprisingly well with the subjective human ratings with 1 corresponding to easy puzzles, 2 to medium puzzles and 3 to hard puzzles. The platinum blond has a difficulty of 3.5789.

An interesting corollary is that no Sudoku puzzle is known with a difficulty of 4.  And the number of clues is not always a good measure of difficulty either.  Ercsey-Ravasz and Toroczkai say they tested many puzzles including several with the 17 clues, the minimum number, and a few with 18 clues.

These were all easier to solve than the platinum blond, which has 21 clues. That’s because the hardness of the puzzle depends not only on the number of clues but also on their position as well.

For and Against Open Journals

As a follow-up to last week’s post on open data, here is a recent article from The Economist on the argument for open journals:

Criticism of journal publishers usually boils down to two things. One is that their processes take months, when the internet could allow them to take days. The other is that because each paper is like a mini-monopoly, which workers in the field have to read if they are to advance their own research, there is no incentive to keep the price down. The publishers thus have scientists—or, more accurately, their universities, which pay the subscriptions—in an armlock. That, combined with the fact that the raw material (manuscripts of papers) is free, leads to generous returns. In 2011 Elsevier, a large Dutch publisher, made a profit of £768m on revenues of £2.06 billion—a margin of 37%. Indeed, Elsevier’s profits are thought so egregious by many people that 12,000 researchers have signed up to a boycott of the company’s journals.

For those who are less familiar with the process, academic journals work something like this. Researchers are eager to publish in journals because that is their main measure of output (“publish or perish,” the saying goes). Although publishing articles is important professional currency for academics, it almost never translates into getting paid for the content they generate. Instead, the journal publisher provides the service of taking articles generated, reviewed, edited by academics for free and distributing them to libraries. For this service, they charge the libraries substantial amounts of money.

I am not complaining about the process, but I am just sharing my understanding of how it works. The main competitor to this model is not one in which academics will receive greater financial remuneration. The new model would exploit the fact that distribution has become much cheaper to provide some sort of open journal.

There are many non-trivial details to be worked out, however. Editors and reviewers would have to get on board. The journal would have to pay careful attention to quality assurance in order to gain respect. Authors would have to choose to submit to that journal over other more established outlets. These challenges are not insurmountable, but they require someone to make the first move. Not to mention that there are already too many journals.