Category: Games
Why does the United States keep killing #2 in ISIS?
On Friday morning, a US air strike killed Abu Alaa al-Afri, a senior leader in ISIS, whom the US says it considers the organization’s second-ranked leader.
This isn’t the first time that al-Afri has been reported dead — though the US government has allegedly verified his death.
But if (as seems likely) al-Afri is dead, this will be yet another instance in which ISIS’s number two official has been killed. In August of last year, for example, a US airstrike killed Fadhil Ahmad al-Hayali, then identified as the group’s number two.
This continues a trend that news consumers may recognize from counterterrorism efforts against al-Qaeda, in which the group seemed to lose one third-in-command (after Osama bin Laden and Ayman al-Zawahiri) after another.
As some Twitter wags noted, this all hearkens back to a 2006 Onion article, “Eighty Percent Of Al-Qaeda No. 2s Now Dead.”
As Zack mentions, there may be reasons why the #1 is harder to find and kill, but I would suggest a complementary hypothesis. At many points in time there is more than one #2, just as corporations may have a variety of Executive Vice Presidents.
If you a leader of a terror group, do you really want a well-defined #2 who is a focal alternative and who can move to overthrow you? Or do you prefer seven competing #2s, with somewhat unclear status, whom you can play off against each other, or make compete against each other, and offer various sticks and carrots and promotions of influence against each other?
And let’s say that one of these #2’s is killed. How will the United States report this? “One of seven #2’s has been killed”? Or perhaps the easier to communicate and more important sounding “We have taken out number two.”
On Wikipedia, the (a?) previous #2 of ISIS is described as “Deputy leader of ISIL?“, question mark in the original.
Here is one look at the command structure of ISIS (or try this alternative). I cannot vouch for its accuracy, but it is one quick way to see that “#2” is perhaps not always so well-defined:
One concrete implication is this: the more number twos there are, the more likely you can kill one of them. And those are exactly the same circumstances when killing a number two has a low marginal return. Keep in mind that the kill is endogenous, and it could be indicating that one of the stronger #2s has strengthened his hand by betraying one of the weakies and getting the West to do the dirty work. And that kind of competition across subordinates may be precisely what strengthens the hand of the leader.
Russia Bans Queue
Kolejka (queue) is a Polish board game based on life under communism.
The players line up their pawns in front of the shops without knowing which shop will have a delivery. Tension mounts as the product delivery cards are uncovered and it turns out that there will be enough product cards only for the lucky few standing closest to the door of a store. Since everyone wants to be first, the queue starts to push up against the door. To get ahead, the people in the queue use a range of queuing cards, such as “Mother carrying small child”, “This is not your place, sir”, or “Under-the-counter goods”. But they have to watch out for “Closed for stocktaking”, “Delivery error”, and for the black pawns – the speculators – standing in the queue. Only those players who make the best use of the queuing cards in their hand will come home with full shopping bags.
…In this realistic game you really have to be savvy to get the goods.
The game was initially developed by Poland’s Institute for National Remembrance to teach about life under communism but the game became an unexpected hit and has since been translated into English, French, Japanese and Russian among other languages.
The Russian government, however, is not amused and have banned the game.
IPN reported that Russia’s consumer watchdog Rospotrebnadzor warned that the game is perceived as “anti-Russian” and excessively critical of the Soviet system. Russian authorities asked Trefl, the company who bought the game’s license from IPN, to either remove the direct historical references from it or risk getting the product banned.
“IPN did not agree to the implementation of these changes and that is why Kolejka is no longer in Russian shops,” a statement by IPN reads.
I imagine the Russians wouldn’t like Kremlin either.
Addendum: A lot of Americans could use some reminders about price controls and socialism, fortunately you can download and print Kolejka in English here (scroll down).
Monopoly markets in everything, negative interest rate edition
That’s Monopoly the board game. Here is the latest:
We may be used to paying for goods at the touch of a card or phone in shops, but now quick and easy electronic payments are making their way to the Monopoly board too.
The Monopoly Ultimate Edition replaces fake notes with an ATM and every part of the game is ‘swipe-able or scan-able’, to bring the board game into the 21st century.
The battery-operated system is designed to speed up the process of making payments to other ruthless players, as well as cut down on cheating.
By the way, there is no great stagnation:
It is not the first time Hasbro has launched an electronic edition of the iconic board game, with two previous versions on sale.
But reviews criticised the firm for slowing the game down, in part due to players having to manually enter sums of money on a fiddly ATM keypad.
The Ultimate Banking Edition will cost $25 (£29.99) when it goes on sale in the autumn.
The full story is here, via the excellent Mark Thorson.
What can finance learn from sports betting?
Tobias J. Moskowitz has a recent paper on this question, the results are illuminating:
I use sports betting markets as a laboratory to test behavioral theories of cross-sectional asset pricing anomalies. Two unique features of these markets provide a distinguishing test of behavioral theories: 1) the bets are completely idiosyncratic and therefore not confounded by rational theories; 2) the contracts have a known and short termination date where uncertainty is resolved that allows any mispricing to be detected. Analyzing more than a hundred thousand contracts spanning two decades across four major professional sports (NBA, NFL, MLB, and NHL), I find momentum and value effects that move betting prices from the open to the close of betting, that are then completely reversed by the game outcome. These findings are consistent with delayed overreaction theories of asset pricing. In addition, a novel implication of overreaction uncovered in sports betting markets is shown to also predict momentum and value returns in financial markets. Finally, momentum and value effects in betting markets appear smaller than in financial markets and are not large enough to overcome trading costs, limiting the ability to arbitrage them away.
SSRN and video versions of the paper are here. The underlying idea here is neat. The marginal utility of consumption is unlikely to be correlated with the outcomes of sporting events, so we can test some propositions of finance theory without having to worry much about those risk factors. Lo and behold, a version of momentum results still holds up. And if you would like an exposition of that approach, do see my earlier dialogue with Cliff Asness. And here is Cliff on Fama on momentum.
Do experts make more errors when they are losing or behind?
Although Stockfish and Komodo have differences in their evaluation scales—happily less pronounced than they were 1 and 2 years ago—they agree that the world’s elite made six times more large errors when on the lower side of equality.
We don’t know how general this phenomenon is, but interestingly it seems to hold much more strongly for top players than for weak players. That is from chess of course.
Here is much more detail from Ken Regan, along with some suggested hypotheses and resolutions.
Go Has Been Broken
Tic-tac-toe fell in 1952, checkers in 1994, chess in 1997 and it now looks like Go, the ancient Chinese game that has a search space many, many times greater than chess, has fallen to a new AI from Google.
…our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
Importantly, AlphaGo isn’t based primarily on searching a huge space but on deep neural networks that learned first from human players and then from simulated play with itself. The techniques, therefore, are not limited to Go.
AlphaGo will face its greatest challenge in March.
AlphaGo’s next challenge will be to play the top Go player in the world over the last decade, Lee Sedol. The match will take place this March in Seoul, South Korea.
Win or lose, I will bet that Lee Sedol is the last human champion the world will ever know.
Why is it hard to measure the value of soccer players?
Max Mendez Beck emails me:
Given the advent of statistics in sports that occurred in the last five years, I am struck by how well soccer works as a metaphor for current epistemological debates regarding the use (and primacy) of quantitative versus qualitative data in social science research. While the three major American sports (football, basketball, and baseball) have been overtaken by a quantitative obsession (count how many tables and numbers you see on an average ESPN show), soccer is emblematic of a sport that is quite difficult to measure quantitatively.
Consider how easy it is to determine who did well in an average NBA game without needing to even watch it. You can just look at points, assists, rebounds, steals, turnovers, etc. In soccer, individual statistics are almost nonexistent. Even as major sports channels have attempted to incorporate quantitative measures into their soccer broadcasts–for example, by showing the number of kilometers a player has covered when he gets subbed out (a pretty uninformative statistic on its own)–these numbers have not caught on with the regular fan.
While in basketball everyone debates about who “the best ever” is by referring to their career averages in points, field goal percentage, PER, etc. In soccer the only statistic that is ever used is goals scores, and goals scored is only one small dimension of a player, even smaller if he is not a striker. It would be silly to judge Andrés Iniesta or Zinedine Zidane on how many goals they scored in a season.
So what is it about soccer that makes it so hard to quantify? Or what makes American sports so easy to measure? One obvious answer is the length of the units that can be easily separated and analyzed. In basketball its a maximum of 24 seconds, in baseball its essentially a pitch (or an at bat), and in football its each snap. For soccer, the only apparent unit to separate out is the 45 minute halftime mark. Changes in possession could be another measure, but even then a team’s single possession could be several minutes long.
However, the real challenge comes in measuring individual accomplishments. Just recently I was watching a Barcelona game and Iniesta clearly was having an amazing game (as was mentioned several times by the announcer), and yet the things that made him have a great game were only describable in words and not numbers. There was a beautiful and sudden “regate” or dribble around a defender before he passed it on to a teammate for a quick counter attack. There was the beautiful pass between defenders that led to an assist for the first goal. There was the sudden change in direction and over the top pass to the other side of the field that put the defenders on their heels. Many of these moves are incredibly situational; they have to do with the rhythm of the game and the need to speed it up or slow it down. Nothing in the boxscore could truly capture these attributes.
So the question is: Is soccer something that can’t be measured in numbers?
Here are various readings on the topic.
A Bitcoin Magic Trick
I love this scene from Digital Gold, Nathaniel Popper’s entertaining book on the history of and people behind Bitcoin. Wences Casares, a successful internet entrepreneur and Bitcoin enthusiast, is at a party of millionaires and billionaires and he wants to impress the crowd:
To prove how easy this all was, Wences asked Blodget to take out his phone and helped him to create an empty Bitcoin wallet. Once it was up, and Wences had Blodget’s new Bitcoin address, Wences used the wallet on his own phone to send Blodget $250,000..the money was then passed to the phones of other people around the table once they had set up wallets. Anyone could have run off with Wences’s $250,000, but that wasn’t a risk with this particular crowd. Instead, as the money went around, Wences saw the guests’ laughter and wide-eyed amazement at what they were watching.
Wences is something of a character. Russ Roberts did a good interview with him on EconTalk.
Do Tinder and related services increase assortative mating?
Under one model, Tinder teaches you the joys of tussling with those from “the other side of the tracks,” and pulls you away from marrying a fellow Ph.d. — “once you’ve tried Mack, you’ll never go back.”
My intuition differs. You use Tinder in bars, venues, and neighborhoods you have chosen. So you end up tussling with, or mating with, or just chatting with, the more attractive members of your own preferred socioeconomic group. If your group wasn’t on average so sexy to begin with, well at least at the top end it just got a big upgrade in terms of your actual access to attractiveness. So on net the high socioeconomic groups become sexier, at least for those “at the top” with the most choice.
(I am assuming by the way that male photos can to some extent signal status, income, and education, and not just looks; furthermore the male follow-up can demonstrate this readily. And many “connection” services post this information in one form or another as part of the profile. If need be, in general equilibrium bars can adjust their exclusiveness levels to match better to a world-with-Tinder, so that bar patrons are not lured into socioeconomically mistaken “honey pot” marriages.)
Most of all, Tinder gets you out more. You sample more people, even if you don’t end up meeting them through the Tinder app itself. Going to a bar or public space is a better way to spend time than before, and that draws others out too. That’s right, “thick market externalities.” The resulting extra meetings tend to favor assortative mating, just compare such plenitude to a corner solution where you meet only one potential spouse your entire life, namely the proverbial girl next door.
Put it this way: George Clooney or a Silicon Valley billionaire can do better — especially better, compared to others — choosing from 500 people than from five. He (she) has a very good chance of getting his (her) absolute top favorite pick, or close to it. The local milkman also does better from a larger sample size, if only because of match and compatibility issues, but can’t expect to move up so much and of course the pool as a whole can’t “move up” at all. (If you wish, break this down into a positive-sum compatibility component and a competitive zero-sum component; unlike Clooney the milkman may not gain on the latter.)
Finally, Tinder may make it easier for married people to find casual sex, again if they have the right qualifications. Therefore those marrieds may, earlier on, decide to choose a spouse on the grounds of IQ and education, again boosting assortative mating in terms of those features.
In sum, I expect Tinder to boost assortative mating, at least at the top end of the distribution in terms of IQ and education.
And please note, I suspect this increase in assortative mating is a good thing. The abilities of top achievers have a disproportionate impact on the quality of our lives, due to innovation being a public good.
In any case, file under speculative.
Addendum: An interesting twist on the model is to assume that men have some willingness to marry down in terms of education, in return for beauty or other forms of household production, but women do not. An increase in the total sampling of potential partners therefore boosts the marriage prospects of very beautiful women, at the expense of less beautiful women of a given level of educational attainment. In percentage terms, very beautiful women of decent but not extraordinary educational achievement gain the most. Men who are indifferent to such forms of female beauty end up with the smartest children.
Second addendum: Yesterday brought an ongoing twitter exchange on these issues, you might start with tweets by @ninjaeconomics and @modestproposal.
We truly live in an insane world
I can’t say I understand this FT article so well, but I suppose that is the point. Which are two groups/persons implicated in buying oil from ISIS, or otherwise enabling such trades to take place?
First, Syria. Or is that “Syria.”
Second, the head of the world chess federation, namely Kirsan Ilyumzhinov: “he is best known for his belief in aliens — he has repeatedly recounted an instance when he was abducted in 1997 by “people in yellow spacesuits”.” And this:
Mr Ilyumzhinov has a diverse business empire, stretching from sugar to banking, and a network of contacts to match. He regularly meets the Dalai Lama, and he played chess with Libyan president Muammer Gaddafi shortly before his overthrow.
He also has been working with the Syrian central bank. Here is NYT coverage, here are other sources. As the old Haitian proverb states, if you’re not confused, you don’t know what’s going on…
Can we determine how honesty varies across countries?
The paper is by David Hugh-Jones, and this is from the research summary:
The study examined whether people from different countries were more or less honest and how this related to a country’s economic development. More than 1500 participants from 15 countries took part in an online survey involving two incentivised experiments, designed to measure honest behaviour.
Firstly, they were asked to flip a coin and state whether it landed on ‘heads’ or ‘tails’. They knew if they reported that it landed on heads, they would be rewarded with $3 or $5. If the proportion reporting heads was more than 50 per cent in a given country, this indicated that people were being dishonest…
The countries studied – Brazil, China, Greece, Japan, Russia, Switzerland, Turkey, the United States, Argentina, Denmark, the United Kingdom, India, Portugal, South Africa, and South Korea – were chosen to provide a mix of regions, levels of development and levels of social trust.
The study’s author Dr David Hugh-Jones, of UEA’s School of Economics, found evidence for dishonesty in all the countries, but that levels varied significantly across them. For example, estimated dishonesty in the coin flip ranged from 3.4 per cent in the UK to 70 per cent in China. In the quiz, respondents in Japan were the most honest, followed by the UK, while those in Turkey were the least truthful. Participants were also asked to predict the average honesty of those from other countries by guessing how many respondents out of 100 from a particular country would report heads in the coin flip test. However, participants’ beliefs about other countries’ honesty did not reflect reality.
This is interesting:
“Differences in honesty were found between countries, but this did not necessarily correspond to what people expected,” he said. “Beliefs about honesty seem to be driven by psychological features, such as self-projection. Surprisingly, people were more pessimistic about the honesty of people in their own country than of people in other countries.
And consider this from Hugh Jones:
“I suggest that the relationship between honesty and economic growth has been weaker over the past 60 years and there is little evidence for a link between current growth and honesty,” said Dr Hugh-Jones. “One explanation is that when institutions and technology are underdeveloped, honesty is important as a substitute for formal contract enforcement. Countries that develop cultures putting a high value on honesty are able to reap economic gains. Later, this economic growth itself improves institutions and technology, making contracts easier to monitor and enforce, so that a culture of honesty is no longer necessary for further growth.”
The research paper is here, and for the pointers I thank Charles Klingman and Samir Varma.
Jones, by the way, makes it clear there are a variety of kinds of honesty, and inferences from any single test should be limited. For Japan in particular the measured level of honesty depends critically on which test is applied. The real lesson of the study may simply be that most groups are dishonest, and people are not even honest with themselves about their views of the dishonesty of others. Honesty depends a good deal on context too. On some of these questions, see some skeptical comments from Kevin Drum.
If you are looking for simple correlations: “…at individual level, there is no evidence that religious adherence is associated with honesty.” How about having a Ph.d.?
But how exactly are they cheating?
A man who sold himself a $1,000,000 winning D.C. Lottery ticket is just one of many retailers a WUSA9 investigation found winning the lottery at rates statisticians say border on impossible.
At least three retailers won the lottery around 100 times according to an analysis of D.C. Lottery records obtained under the Freedom of Information Act.
“$10,000, $5,000,” Lounes Issaad said about some of his 27 payouts that averaged $30,000 each. “I don’t have nothing to hide.”
…Our investigation found lottery retailers make up at least three of the top five D.C. Lottery frequent winners – all with about 100 wins or more.
There is more here (the link makes some noise), via Michael Rosenwald.
Yelp for people
You can already rate restaurants, hotels, movies, college classes, government agencies and bowel movements online.
So the most surprising thing about Peeple — basically Yelp, but for humans — may be the fact that no one has yet had the gall to launch something like it.
When the app does launch, probably in late November, you will be able to assign reviews and one- to five-star ratings to everyone you know: your exes, your co-workers, the old guy who lives next door. You can’t opt out — once someone puts your name in the Peeple system, it’s there unless you violate the site’s terms of service. And you can’t delete bad, inaccurate or biased reviews — that would defeat the whole purpose.
Imagine every interaction you’ve ever had suddenly open to the scrutiny of the Internet public.
The piece is by the excellent Caitlin Dewey. Currently the company is valued at $7.6 million.
Which words in the English language most reliably mean their opposites?
Is there a word in the English language that more reliably means its opposite than ‘amicable’?
Twitter responses included: “moot,” “humbled,” “nice,” “my friend,” “nonplussed,” “cordial,” “priceless,” “tolerance,” “literally,” “spry,” “sincerely,” “honest,” “pal,” “sure,” and “”Fine” particularly when given as a one word answer.”
My favorite was “spry.” Is there a word for such words? Are there other examples?
Observers expect forecasters to show high confidence
Observers seem to focus on the target event and not its complement. Bagchi and Ince have a new paper on this question:
Consumers routinely rely on forecasters to make predictions about uncertain events (e.g., sporting contests, stock fluctuations). The authors demonstrate that when forecasts are higher versus lower (e.g., a 70% vs. 30% chance of team A winning a game) consumers infer that the forecaster is more confident in her prediction, has conducted more in-depth analyses, and is more trustworthy. The prediction is also judged as more accurate. This occurs because forecasts are evaluated based on how well they predict the target event occurring (team A winning). Higher forecasts indicate greater likelihood of the target event occurring, and signal a confident analyst, while lower forecasts indicate lower likelihood and lower confidence in the target event occurring. But because, with lower forecasts, consumers still focus on the target event (and not its complement), lower confidence in the target event occurring is erroneously interpreted as the forecaster being less confident in her overall prediction (instead of more confident in the complementary event occurring—team A losing). The authors identify boundary conditions, generalize to other prediction formats, and demonstrate consequences.
Of course this also has relevance for the evolutionary processes governing pundits.
Here is a related press release (pdf). For the pointer I thank Charles Klingman.