Trump vs. a Japanese whale

The story of Akio Kashiwagi, drawn from Trump’s memoirs and news accounts from the day, offers a revealing window into Trump’s instincts. It shows that Trump isn’t just a one-time casino owner—he’s also a gambler, prone to impulsive, even reckless action. In The Art of the Comeback, published in 1997, Trump explains that until he met Kashiwagi, he saw himself as an investor who dealt only in facts and reason. But his duel with the great whale in action made him realize “that I had become a gambler, something I never thought I was.”
 

Perhaps just as important, when gambling failed him, Trump didn't quit: He doubled down. But he did it shrewdly, summoning a RAND Corporation mathematician to devise a plan that would maximize his chance of fleecing his Japanese guest.
 

And it worked. Kind of. In Trump’s recollection, which he shared for this story, his showdown with Kashiwagi was another one of his many great wins. Just don’t look too hard at the ledger.
 

A bizarre and nutty tale of the time Donald Trump hired a RAND Corp mathematician to try to win back money a Japanese gambler took from one of his hotels in a hot night of Baccarat.

Before reading the piece, I thought perhaps they had changed the rules of the game somehow to raise the house edge. But no, they just changed the terms under which Kahiwagi to play, counting on the house edge to manifest over the long run.

Behind the trademark bluster, however, Trump grew more calculated. Having looked in the mirror and seen a gambler, he reverted to careful strategy. Trump consulted Jess Marcum, a mathematical probabilities expert who co-founded the Rand Corporation—a government-affiliated think tank then better known for modeling nuclear war with the Soviet Union—on how to maximize his odds in a second showdown with Kashiwagi. Marcum knew the only way to compensate for the house’s very slight baccarat advantage, of just over one percent, was to keep the game going for as long as possible. Time was on Trump’s side.
 

So Marcum and an Atlantic City casino insider named Al Glasgow prepared a report for Trump proposing a “freeze out” agreement. Under the deal, Kashiwagi would bring $12 million to the table and play until he had either doubled it—or lost everything. Even with huge bets, that would take a long time. Marcum simulated the match in detailed hand written notes. Kashiwagi might surge ahead early, he estimated, but after 75 hours at the table – far longer than he had stayed the first time - his chances of winning would fall to 15 percent. The key was to prevent a repeat of Kashiwagi’s first visit, when he had walked out while ahead.
 

Kashiwagi, presumably fuzzier on the probabilities, agreed to the terms. There was no legal way to hold him to such a deal but Trump felt the men were honor-bound. “Gamblers are honorable, in their own way—at least about gambling,” he later wrote.
 

The peculiar thing about Trump is that, as offended as I am but so many of the things he says, I'm not convinced he actually believes half the things he spews with such gusto. Yes, he's a politician, and they're always churning out rhetoric for reasons of positioning, but Trump exceeds even other politicians in his commitment to artifice.

Because of that, when he says something I disagree with, I'm more offended by the casual way he tosses around such damaging ideas than the ideas themselves, and when he says something I agree with—which is, admittedly, rare—I don't give him much credit.

He needs no exaggeration to be rendered a caricature, because he has done it himself, both figuratively and literally, like one of those figures in Pinocchio who becomes the physical embodiment of its own hubris. If you were a cartoonist on assignment to lampoon him, you could just snap a photo and collect a full day's pay.

Optimal pricing for bread and circuses

A survey (pdf) by Anthony Krautmann and David Berri has found that most fans in many popular sports pay less for their tickets than conventional economic theory would predict.
 

Which poses the question: are team owners therefore irrational?
 

Not necessarily. There are (at least?) four justifications for such apparent under-pricing.
 

Lots of things in the real world are underpriced. Most popular concerts and sporting contests lose some volume of revenue to aftermarket transactions on sites like StubHub and SeatGeek. It's nearly impossible to get a reservation at some of the most popular restaurants in San Francisco like State Bird Provisions. There's a waiting list for NOMA Sydney that's 27,000 people long.

If you were pricing to maximize revenue, to match supply and demand exactly, you'd boost prices or perhaps auction off all the seats. What would NOMA Sydney have to charge until its waiting list dropped to zero? I can't even begin toguess, but would it surprise you if it was well north of $2,000 a head for dinner?

Given all of that, I was curious to see what this author thought might explain football ticket underpricing.

The first argument is that underpricing tickets leaves more revenue to be gathered through ancillary sales like souvenirs or overpriced concessions. Without data, I'm skeptical. My instinct is that concession and souvenir sales are less elastic with ticket prices than hypothesized.

The second point is that it's better to have a full stadium for team morale and to influence the officiating. But again, you could sell tickets via a mechanism like a Dutch Auction and maximize revenue while still filling the stadium.

The other two arguments are more convincing.

Thirdly, higher ticket prices can have adverse compositional effects: they might price out younger and poorer fans but replace them with tourists – the sort who buy those half-and-half scarves and should, therefore be shot on sight. This increases uncertainty about longer-term revenues: a potentially life-long loyal young supporter is lost and a more fickle one is gained. It also diminishes home advantage: refs are more likely to give dodgy decisions in front of thousands of screaming Scousers than in front quiet Japanese tourists.
 

I went to a couple games at the old Chicago Stadium, during Jordan's early years with the Bulls, and that place was loud. When they moved to the United Center and the ticket prices went way up, the crowd felt different. More wealthy, and definitely not as loud. It could just be the acoustics of the new space, but anecdotally, I saw fewer fans standing and screaming. Also, the rise of the smartphone means more of the dead moments in a game are filled with people scrolling on their phones, quietly.

Fourthly, high ticket prices can make life harder for owners. They raise fans’ expectations: if you’re spending £50 to see a game you’ll expect better football than if you spend just £10: I suspect that a big reason why Arsene Wenger has been criticised so much in recent years is not so much that Arsenal’s performances have been poor but because high prices have raised expectations. 
 

It's hard to lower prices. Some sports teams may have done it at some point, but I've never seen it. You can raise prices when the team is good and on the rise, but those prices tend to stick when the team declines, and that's when stadiums start to empty out.

Saison is the restaurant in San Francisco that feels closest to pricing to match supply and demand. When I first moved to San Francisco, I had a meal there for $79. The next time there, the meal price had jumped over $100. Then the next time, it was up to $149. Later I heard the tasting menu had risen yet again to $248. The last time I went, thankfully on some banker's expense account, the price was $398 for dinner.

The dining room is usually full, but it's usually possible to get a table the same week. It feels like they've finally reached a price that about as close as you can get to where the supply and demand curves meet. Since the number of seats and turns is limited each night, perhaps this is revenue maximizing pricing, but the margin of error is razor thin.

My guess is that optimal pricing is somewhere below the price that matches supply and demand perfectly. Always being sold out adds a feeling of exclusivity, and no one knows how sold out you are, so being just sold out may be as good from a perception standpoint as being having a massive waiting list.

At the same time, I have a sneaking suspicion continuing to raise the price of a dinner would actually raise demand at some high end restaurants. There may be some Veblen-like qualities to restaurant pricing.

If the glove kinda fits, do not acquit?

So I wrote down the simplest model I could think of — a model too simple to give useful numerical cutoffs, but still a starting point — and I learned something surprising. Namely (at least in this very simple model), the harsher the prospective punishment, the laxer you should be about reasonable doubt. Or to say this another way: When the penalty is a year in jail, you should vote to convict only when the evidence is very strong. When the penalty is 50 years, you should vote to convict even when it’s pretty weak.
 

(The standard here for what you “should” do is this: When you lower your standards, you increase the chance that Mr. or Ms. Average will be convicted of a crime, and lower the chance that the same Mr. or Ms. Average will become a crime victim. The right standard is the one that balances those risks in the way that Mr. or Ms. Average finds the least distasteful.)
 

Here (I think) is what’s going on: A weak penalty has very little deterrent effect — so little that it’s not worth convicting an innocent person over. But a strong penalty can have such a large deterrent effect that it’s worth tolerating a lot of false convictions to get a few true ones.
 

Steven Landsburg lands on a counter-intuitive conclusion: you should lower your standards for conviction the harsher the punishment.

It seems as if Landsburg's model argues for convicting any number of people who surpass some lowered threshold of evidence for a crime. Several people all seem like they could have committed a crime, so convict all of them, even if only one could have committed the crime. Perhaps I'm misunderstanding the implications, others can help verify Landsburg's model.

Also, how often are there N people who all seem equally guilty of a crime? I'm at a disadvantage here in not having seen Making a Murderer, but perhaps Landsburg's model here applies equally as well to that case as it does to Serial Season 1.

Let's broaden the conversation and bring in Alex Tabarrok, discussing one area in which fellow economist Gary Becker may have been wrong.

Becker isn’t here to defend himself on the particulars of that evening but you can see the idea in his great paper, Crime and Punishment: An Economic Approach. In a famous section he argues that an optimal punishment system would combine a low probability of being punished with a high level of punishment if caught:
 

If the supply of offenses depended only on pf—offenders were risk neutral — a reduction in p “compensated” by an equal percentage increase in f would leave unchanged pf…
 

..an increased probability of conviction obviously absorbs public and private resources in the form of more policemen, judges, juries, and so forth. Consequently, a “compensated” reduction in this probability obviously reduces expenditures on combating crime, and, since the expected punishment is unchanged, there is no “obvious” offsetting increase in either the amount of damages or the cost of punishments. The result can easily be continuous political pressure to keep police and other expenditures relatively low and to compensate by meting out strong punishments to those convicted.
 

We have now tried that experiment and it didn’t work. Beginning in the 1980s we dramatically increased the punishment for crime in the United States but we did so more by increasing sentence length than by increasing the probability of being punished. In theory, this should have reduced crime, reduced the costs of crime control and led to fewer people in prison. In practice, crime rose and then fell mostly for reasons other than imprisonment. Most spectacularly, the experiment with greater punishment led to more spending on crime control and many more people in prison.
 

Why did the experiment fail? Longer sentences didn’t reduce crime as much as expected because criminals aren’t good at thinking about the future; criminal types have problems forecasting and they have difficulty regulating their emotions and controlling their impulses. In the heat of the moment, the threat of future punishment vanishes from the calculus of decision. Thus, rather than deterring (much) crime, longer sentences simply filled the prisons. As if that weren’t bad enough, by exposing more people to criminal peers and by making it increasingly difficult for felons to reintegrate into civil society, longer sentences increased recidivism.
 

It's a great post by Tabarrok. He does give Becker, one of my economics idols, credit.

Let’s give Becker and the rational choice theory its due. When Becker first wrote many criminologists were flat out denying that punishment deterred. As late as 1994, for example, the noted criminologist David Bayley could write:
 

The police do not prevent crime. This is one of the best kept secrets of modern life. Experts know it, the police know it, but the public does not know it. Yet the police pretend that they are society’s best defense against crime. This is a myth
 

Inspired by Becker, a large, credible, empirical literature–including my own work on police (and prisons)–has demonstrated that this is no myth, the police deter. Score one for rational choice theory. It’s a far cry, however, from police deter to twenty years in prison deters twice as much as ten years in prison. The rational choice theory was pushed beyond its limits and in so doing not only was punishment pushed too far we also lost sight of alternative policies that could reduce crime without the social disruption and injustice caused by mass incarceration.
 

The problem with annual reviews in companies is not necessarily with an annual review process but with lack of immediate feedback in between those reviews. The most useful thing I learned from the 10,000 hour rule wasn't that you needed 10,000 hours to become an expert, it was that people improve with deliberate practice if feedback on their work is immediate.

For effective parenting and coaching, shorten the time between performance and feedback, and be consistent.

Information tech and variety

Abstract:      

Using the food truck industry as the setting, we provide direct evidence for how information technology can complement consumption variety in cities by reducing spatial information frictions associated with locally produced goods. We document the following facts: 1) food trucks use technology to overcome a spatial information friction; 2) proliferation of technology is related to growth in food trucks; 3) food trucks use their mobility to respond to consumer taste-for-variety; and 4) growth in food trucks is positively correlated with growth in food expenditures away from home. Taken together, our results illustrate how information technology can provide a meaningful increase in variety for urban consumers.
 

Research paper titled Information Technology and Product Variety in the City: The Case of Food Trucks.

It's not just food variety that's increased thanks to information technology, though food trucks are one of the more peculiar instances. I lived in LA from 2006-2011, and that city's lower flatter, more dispersed distribution of retail and people might have made it an optimal ground zero for the food truck boom.

Amazon has increased our retail variety expectations. The internet and the web have increased the variety of information we expect to find with a query typed into a search engine. Information technology plus urban density are an intertwined network that overcomes much of the spatial friction of the past, which is why it's so odd to me that it's still so hard to find good versions of so many types of ethnic food in San Francisco.

The lady had dropped her napkin

The lady had dropped her napkin.
 

More accurately, she had hurled it to the floor in a fit of disillusionment, her small protest against the slow creep of mediocrity and missed cues during a four-hour dinner at Per Se that would cost the four of us close to $3,000. Some time later, a passing server picked up the napkin without pausing to see whose lap it was missing from, neatly embodying the oblivious sleepwalking that had pushed my guest to this point.
 

Such is Per Se’s mystique that I briefly wondered if the failure to bring her a new napkin could have been intentional. The restaurant’s identity, to the extent that it has one distinct from that of its owner and chef, Thomas Keller, is based on fastidiously minding the tiniest details. This is the place, after all, that brought in a ballet dancer to help servers slip around the tables with poise. So I had to consider the chance that the server was just making a thoughtful accommodation to a diner with a napkin allergy.
 

But in three meals this fall and winter, enough other things have gone awry in the kitchen and dining room to make that theory seem unlikely. Enough, also, to make the perception of Per Se as one of the country’s great restaurants, which I shared after visits in the past, appear out of date. Enough to suggest that the four-star rating it received from Sam Sifton in 2011, its most recent review in The New York Times, needs a hard look.
 

Pete Wells of the NYTimes drops Per Se from 4 stars to 2.

I have no idea if Wells is right or not, but I can't think of too many other food writers who can make a restaurant review as pleasurable to read. Writing about food is like writing about music; language can feel like an inadequate medium for describing something which we experience through our senses, bypassing the symbolic representations of words. Wells avoids those traps by, in large part, not trying to describe tastes.