Playing Chicken with Your Calendar

The ever-interesting Brendan Nelson on meeting chicken:

You have a regular meeting in your calendar. It’s with just one other person. Sometimes you have things to talk to them about and sometimes you don’t. But as long as your calendar says you both have to go, you will both go.

The day of the meeting comes round. There are lots of things that need to be done that day. You look at that meeting sitting obstinately in your calendar and think how useful it would be to get that time back.

Inspiration strikes: why not cancel the meeting? A couple of mouse clicks, an automatic notification sent out, a joyously blank calendar. It seems so easy.

But you can’t bring yourself to do it, to cancel a meeting at such short notice. It would make you look disorganised, unprepared. And what about the other person?

More at the link.

More on Food Truck Regulation

Popular Durham-area food truck Chirba Chirba serves dumplings. Photo via livewell.

Popular Durham-area food truck Chirba Chirba serves dumplings. Photo via livewell.

More on the plight of food truck operators in NYC, from the Times:

There are numerous (and sometimes conflicting) regulations required by the departments of Health, Sanitation, Transportation and Consumer Affairs. These rules are enforced, with varying consistency, by the New York Police Department. As a result, according to City Councilman Dan Garodnick, it’s nearly impossible (even if you fill out the right paperwork) to operate a truck without breaking some law. Trucks can’t sell food if they’re parked in a metered space . . . or if they’re within 200 feet of a school . . . or within 500 feet of a public market . . . and so on.

Enforcement is erratic. Trucks in Chelsea are rarely bothered, Nafziger said. In Midtown South, where I work and can attest to the desperate need for more lunch options, the N.Y.P.D. has a dedicated team of vendor-busting cops. “One month, we get no tickets,” Thomas DeGeest, the founder of Wafels & Dinges, a popular mobile-food businesses that sells waffles and things, told me. “The next month, we get tickets every day.” DeGeest had two trucks and five carts when he decided he couldn’t keep investing in a business that was so vulnerable to overzealous cops or city bureaucracy. Instead, DeGeest reluctantly decided to open a regular old stationary restaurant.

We’ve discussed food truck regulations and the competition between vendors before. There is certainly a place for regulation, but inconsistent and seemingly arbitrary enforcement undermines the goal of clarifying expectations between all parties.

Python for Political Scientists, Spring 2013 Recap

pythonThis spring Josh Cutler‘s Python course was back by popular demand. (This time it was known as “Computational Political Economy” but I like the less formal title.) I participated this time around as a teaching assistant rather than student, and it was a thoroughly enjoyable experience. The course syllabus and schedule is on Github.

Class participants were expected to have a basic familiarity with Python from going through Zed Shaw’s book over Christmas break outside of class. Each Tuesday Josh would walk them through a new computer science concept and explain how it could be used for social science research. These topics included databases, networks, web scraping, and linear programming. On Thursdays they would come to a lab session and work together in small groups to solve problems or answer questions based on some starter code that I supplied. I generally tried to make the examples relevant and fun but you would have to ask them whether I succeeded.

The class ended this past Saturday with final presentations, which were all great. The first project scraped data from the UN Millenium Development Goal reports and World Bank statistics to compare measures of maternal mortality in five African countries and show how they differed–within the same country! This reminded me of Morten Jerven’s book Poor Numbers on the inaccuracy of African development statistics (interview here).

In the second presentation, simulated students were treated with one of several education interventions to see how their abilities changed over time. These interventions could be applied uniformly to everyone or targeted at those in the bottom half of the distribution. Each child in the model had three abilities that interacted in different ways, and interventions could target just one of these abilities or several in combination. Combining these models with empirical data on programs like Head Start is an interesting research program.

The third presentation also used a computational model. Finding equilibrium networks of interstate alliances is incredibly difficult (if not impossible) to do analytically when the number of states is large. The model starts with pre-specified utility functions and runs until the network hits an equilibria. Changing starting values allows for the discovery of multiple equilibria. This model will also be combined with empirical data in the future.

For the fourth and final presentation, one participant collected data on campaign events in Germany for each of the political parties during the current election cycle. This reminded me of a Washington Post website (now taken down) detailing campaign visits in 2008 that I scraped last year and used in lab once this semester.

These examples show the wide variety of uses for programming in social science. From saving time in data collection to generating models that could not be done without the help of algorithms, a little bit of programming knowledge goes a long way. Hopefully courses like this will become more prominent in social science graduate (and undergraduate) programs over the coming years. Thanks to Josh and all the class participants for making it a great semester!

____________

Note: I am happy to give each of the presenters credit for their work, but did not want to reveal their names here due to privacy concerns. If you have questions about any of the presentations I can put you in touch with their authors via email.

Five Lessons on Strategic Thinking from Jane Austen

austen-game-theoristOn Monday I mentioned Michael Suk-Young Chwe‘s new book, Jane Austen, Game Theorist. In this post we take a deeper look at Chwe’s argument: that Jane Austen was teaching lessons about strategic thinking through her novels in what he calls “folk game theory.” We will do that by going through chapters nine and ten in which Chwe examines five lessons on strategic thinking found in Austen’s six novels. I will focus here on examples from Pride and Prejudice as a way of narrowing the field and because it is probably the most popular of the six; page numbers refer to Chwe’s book.

1. Strategic thinking can lead to strong partnerships

One of Chwe’s goals in his book is to help dispel the notion that game theory is strictly atomistic. Austen does a good job of this because some of the strongest couples in her novels result from two characters jointly strategizing. Elizabeth Bennet and Mr. Darcy are first in conflict because they are strategizing differently (Mr. Darcy cannot imagine Elizabeth turning down his proposal of marriage; p. 146). Austen is shows the importance of choice and in particular the choice of a woman to accept to reject a proposal. As they encounter other strategic situations throughout the novel, though, Elizabeth and Mr. Darcy gradually establish a pattern of working together. By learning how the other thinks, they engage in what for Austen is the height of intimacy. This type of joint strategizing can also strengthen female friendships (for Austen females are the more strategic of the two genders; p. 151).

2. You can strategically manipulate yourself

Another matter of choice–again, a primary theme in Austen’s work–is the decision to engage in “self-management” (156). An individual can have multiple “selves,” some of which are more in line with her long-term goals than others. Temperament alone is not sufficient to maintain commitment to your long-term interests, so you must allow your more rational self to override your short-term interests. This strategy can also be used to work against your own biases if you are aware of them (157-8). Mr. Darcy argues in a letter to Elizabeth that he was aware of his bias and was able to avoid letting it influence him: “That I was desirous of believing her indifferent is certain,–but I will venture to say that my investigations and decisions are not usually influence by my hopes or fears.–I did not believe her indifferent because I wished it.”

3. Preferences can be changed

Most social science models take preferences as given, but Austen is interested in how they can be shaped. One mechanism for changing preferences is gratitude (158-9). When Elizabeth learns that Mr. Darcy helped support the marriage between her sister Lydia and Wickham she becomes much more open to the idea of a relationship with him  (telling him that “her sentiments had undergone so material a change… as to make her receive with gratitude and pleasure, his present assurances”). Love in Austen’s novels is a coordination problem, and being in love can also affect individuals’ preferences (160). A third factor that influences preferences is reference dependence: to what baseline are you comparing your current options (161-2).

4. Commitment requires strategic thinking

As discussed above, understanding how someone makes decisions–their preferences and strategies–is for Austen the basis of intimacy. By understanding another, you can view subsequent choices that might otherwise seem inconsistent as flowing from the same strategic point of view. This allows you to understand their goals and recognize their commitments (169). It also helps you to predict how they will react in changing circumstances, allowing you to assess whether and how committed they are to you.

5. Strategic thinking has its disadvantages

This final lesson is truly an innovation on Austen’s part, since contemporary game theory does not often consider downsides to rational thinking. Several complications may arise if you are known to be a strategic thinker. First, others might rely on you too heavily to make decisions for them (172). It may also lead to moral complications if others ask you to engage in strategic actions on their behalf, such as deception. Others might be less willing to help you if they know you are thinking strategically (173). If they view you as always looking for your own most preferred outcome, they may also become less trusting (175-6).

Through these lessons we can see that the manner in which an individual engages in strategic thinking can either strengthen or weaken her social interactions. Austen’s “folk game theory” helped to teach a disadvantaged social class how to outthink their counterparts and end up in more desirable circumstances. She also showed that game theory need not be individualistic, and how strategic thinking can be used to help others. If you enjoyed this post, there is much more to learn from Austen and Chwe does a great job of drawing out those lessons from all six of her novels. One of the biggest lessons in Austen’s novels–that others think differently from you–is still valuable today.

What Can Novels Teach Us?

Is it worthwhile for a social scientist to read fiction? What can novels teach us about human behavior? This post summarizes the work of several authors who would answer the first question with a resounding “yes,” and describes their arguments about how novels help us understand social behavior.

Most recently I had the pleasure of reading Michal Suk-Young Chwe‘s new book, Jane Austen, Game Theorist. Austen herself likely would have preferred the term “imaginist,” which is how the title character in Emma describes herself, referring to her strategic thinking abilities. Chwe’s argument in the book is that Austen is systematically analyzing strategic thinking through her novels. Austen certainly understood that novels could help teach social behavior: she writes in Northanger Abbey that novels contain “the most thorough knowledge of human nature [and] the happiest delineation of its varieties.” On Wednesday we will take a more detailed look at Chwe’s argument. In the meantime you can find a presentation summarizing the book here.

Austen would be in good company with Ariel Rubinstein. The central thesis of his recent book, Economic Fables, is straightforward: “Economic models are not more, but also not less, than stories–fables.” (You can read the book for free here, or see Ariel explain the motivation behind the book in this video.) Rubinstein’s view is actually the converse of Austen’s: he is not arguing that works of fiction are illustrative of human behavior, but that many social science models are themselves useful fictions. (Ed Leamer has advanced a similar view with a more practical twist in his book, Macroeconomic Patterns and Stories.)

Tyler Cowen helps to identify the key differences and similarities between models and novels in his paper, “Is a Novel a Model?” Here is the abstract:

I defend the relevance of fiction for social science investigation. Novels can be useful for making some economic approaches — such as behavioral economics or signaling theory — more plausible. Novels are more like models than is commonly believed. Some novels present verbal models of reality. I interpret other novels as a kind of simulation, akin to how simulations are used in economics. Economics can, and has, profited from the insights contained in novels. Nonetheless, while novels and models lie along a common spectrum, they differ in many particulars. I attempt a partial account of why we
sometimes look to models for understanding, and other times look to novels.

This interview with Tyler contains a summary of his perspective on novels and much more.

Cowen’s former GMU Economics colleague Russ Roberts also agrees that novels are useful for understanding social behavior–so much so that he has written three of them. Each of the novels illustrates one main economic lesson, and all of them support the idea of free markets for solving problems. Roberts interviewed Rubinstein the Econtalk podcast, in which they discuss some of the ideas that led to Rubinstein’s new book.

Overall this attention to useful fictions is a positive development for social science. Novels can help reach a much wider audience than journal articles and many nonfiction books. One danger–which we are far from now but still exists–is that we value the elegance of the novel itself (the language it uses) rather than the lessons it teaches. Another downside is that it is difficult to convey the policy relevance of a novel. Nevertheless, teaching lessons about human behavior in an enjoyable and memorable form is a huge step forward from most contemporary social science.

Interviews with Over 50 IR Scholars

Readers of this blog may enjoy Theory Talks, which I recently discovered thanks to a link on Twitter that I cannot remember now. Here’s how the site describes itself:

Theory Talks is an interactive forum for discussion of debates in International Relations with an emphasis of the underlying theoretical issues. By frequently inviting cutting-edge specialists in the field to elucidate their work and to explain current developments both in IR theory and real-world politics, Theory Talks aims to offer both scholars and students a comprehensive view of the field and its most important protagonists.

The interviews tend to follow a pattern of questions, which I like because you can compare views between scholars in different interviews. The three questions they ask in every interview I have read so far are:

  1. What is, according to you, the biggest challenge / principal debate in current IR? What is your position or answer to this challenge / in this debate?
  2. How did you arrive at where you currently are in IR?
  3. What would a student need to become a specialist in IR like yourself?

Here are some interviews with big names to get you started:

Enjoy!

The Political Economy of Scrabble: Currency, Innovation, and Norms

Scrabble ornaments made by Jennifer Bormann, 2011

Scrabble Christmas ornaments made by Jennifer Bormann, 2011

In Scrabble, there is a finite amount of resources (letter tiles) that players use to create value (points) for themselves. Similarly, in the real world matter cannot be created so much of human effort is rearranging the particles that exist into more optimal combinations. The way that we keep track of how desirable those new combinations are in the economy is with money. Fiat currency has no intrinsic value–it is just said to be worth a certain amount. Sometimes this value changes in response to other currencies. Other times, governments try to hold it fixed. The “law of Scrabble” has remained unchanged since 1938 when it was introduced–but that may be about to change.

Like any well-intentioned dictator, Scrabble inventor Alfred Butts tried to base the value of his fiat money–er, tiles–on a reasonable system:  the frequency of their appearance on the front page of the New York Times. As the English language and the paper of record have evolved over the years, though, the tiles’ stated value has remained static. This has opened the door for arbitrage opportunities, although some players try to enforce norms to discourage this type of play:

What has changed in the intervening years is the set of acceptable words, the corpus, for competitive play. As an enthusiastic amateur player I’ve annoyed several relatives with words like QI and ZA, and I think the annoyance is justified: the values for Scrabble tiles were set when such words weren’t acceptable, and they make challenging letters much easier to play.

That is a quote from Joshua Lewis, who has proposed updating Scrabble scoring using his open source software package Valett. He goes on to say:

For Scrabble, Valett provides three advantages over Butts’ original methodology. First, it bases letter frequency on the exact frequency in the corpus, rather than on an estimate. Second, it allows one to selectively weight frequency based on word length. This is desirable because in a game like Scrabble, the presence of a letter in two- or three-letter words is valuable for playability (one can more easily play alongside tiles on the board), and the presence of a letter in seven- or eight-letter words is valuable for bingos. Finally, by calculating the transition probabilities into and out of letters it quantifies the likelihood of a letter fitting well with other tiles in a rack. So, for example, the probability distribution out of Q is steeply peaked at U, and thus the entropy of Q’s outgoing distribution is quite low.

Lewis’s idea seems to fit with a recent finding by Peter Norvig of Google. Norvig was contacted last month by Mark Mayzner, who studied the same kind of information as the Valett package but did it back in the early 1960s. Mayzner asked Norvig whether his group at Google would be interested in updating those results from five decades ago using the Google Corpus Data. Here’s what Norvig has to say about the process:

The answer is: yes indeed, I (Norvig) am interested! And it will be a lot easier for me than it was for Mayzner. Working 60s-style, Mayzner had to gather his collection of text sources, then go through them and select individual words, punch them on Hollerith cards, and use a card-sorting machine.

Here’s what we can do with today’s computing power (using publicly available data and the processing power of my own personal computer; I’m not not relying on access to corporate computing power):

1. I consulted the Google books Ngrams raw data set, which gives word counts of the number of times each word is mentioned (broken down by year of publication) in the books that have been scanned by Google.

2. I downloaded the English Version 20120701 “1-grams” (that is, word counts) from that data set given as the files “a” to “z” (that is, http://storage.googleapis.com/books/ngrams/books/googlebooks-eng-all-1gram-20120701-a.gz to http://storage.googleapis.com/books/ngrams/books/googlebooks-eng-all-1gram-20120701-z.gz). I unzipped each file; the result is 23 GB of text (so don’t try to download them on your phone).

3. I then condensed these entries, combining the counts for all years, and for different capitalizations: “word”, “Word” and “WORD” were all recorded under “WORD”. I discarded any entry that used a character other than the 26 letters A-Z. I also discarded any word with fewer than 100,000 mentions. (If you want you can download the word count file; note that it is 1.5 MB.)

4. I generated tables of counts, first for words, then for letters and letter sequences, keyed off of the positions and word lengths.

Here is the breakdown of word lengths that resulted (average=4.79):

norvig-word-lengths

Sam Eifling then took Norvig’s results and translated them into updated Scrabble values:

While ETAOINSR are all, appropriately, 1-point letters, the rest of Norvig’s list doesn’t align with Scrabble’s point values….

This potentially opens a whole new system of weighing the value of your letters….  H, which appeared as 5.1 percent of the letters used in Norvig’s survey, is worth 4 points in Scrabble, quadruple what the game assigns to the R (6.3 percent) and the L (4.1 percent) even though they’re all used with similar frequency. And U, which is worth a single point, was 2.7 percent of the uses—about one-fifth of E, at 12.5 percent, but worth the same score. This confirms what every Scrabble player intuitively knows: unless you need it to unload a Q, your U is a bore and a dullard and should be shunned.

However, Norving included repeats like “THE”–not much fun to play in Scrabble, and certainly not with the same frequency it appears in the text corpus (1 out of 14 turns). With the help of his friend Kyle Rimkus, Eifling conducted a letter-frequency survey of words from the Scrabble dictionary and came up with these revisions to the scoring system:

Image from Slate

Image from Slate

Eifling points out that Q and J seem quite undervalued in the present scoring system. So what is an entrepreneurial player to do? “Get rid of your J and your Q as quickly as possible, because they’re just damn hard to play and will clog your rack. The Q, in fact, is the worst offender,” he says.

Now as with any proposed policy update that challenges long-standing norms, there has been some pushback against these recent developments.  at Slate quotes the old guard of Scrabble saying that the new values “take the fun out” of the game. Fatsis seems to hope that the imbalance between stated and practical values will persist:

Quackle co-writer John O’Laughlin, a software engineer at Google, said the existing inequities also confer advantages on better players, who understand the “equity value” of each tile—that is, its “worth” in points compared with the average tile. That gives them an edge in balancing scoring versus saving letters for future turns, and in knowing which letters play well with others. “If we tried to equalize the letters, this part of the game wouldn’t be eliminated, but it would definitely be muted,” O’Laughlin said. “Simply playing the highest score available every turn would be a much more fruitful strategy than it currently is.”

In political economy this is known as rent-seeking behavior. John Chew, doctoral student in mathematics at the University of Toronto and co-president of the North American Scrabble Players Association, went so far as to call Valett a “catastrophic outrage.”

Who knew that the much beloved board game could provoke such strong feelings? With a fifth edition of the Scrabble dictionary due in 2014 it seems possible but highly unlikely that there could be a response to these new findings. A more probable outcome is that we begin to see “black market” Scrabble valuations that incorporate the new data, much like underground economies emerge in states with strict official control over the value of their money. Yet again, evidence for politics in everyday life.

For more fun with letter games, data, and coding, check out Jeff Knups’ guide to “Creating and Optimizing a Letterpress Cheating Program in Python.”

Micro-Institutions Everywhere: Parking and Snow

snow cartoonJeff Ely reports the problem:

You dig your car out of the snow, run an errand or two and come back home to discover…someone else has parked in “your” spot! This free rider problem reduces your incentive to dig your car out in the first place. If only property rights could be enforced, your incentives would be good.

According to the Washington Post, some cities use the legal system, others employ norms:

Boston has codified its citizens’ right to benefit from their backbreaking snow-clearing labor; a city law says that if you dig out your car in a snow emergency, a lawn chair or trash can renders the spot yours for at least two days while you’re away at work. In Chicago, blocking a parking spot is illegal, but city officials acknowledge an informal rule of dibs if you’ve done the digging.

During the DC Snowpocalypse of 2010, residents were unsure which method was best. The desire for some level of temporary property rights was there, but enforcement methods varied:

“I know this is public property, but if you spent hours laboring, I mean, come on, I think you have the right to say that is my spot,” said Tanya Barbour, who spent two hours Sunday shoveling free her silver Ford Expedition in the 1500 block of T Street NW. “If someone had clearly taken the time to shovel it out, I would not take that spot because I would not want that done to me.”

Across the District and in the Maryland suburbs Monday, many were not relying on Barbour’s honor system. Some used Boston-style markers — lawn chairs, recycling bins, orange cones, a mattress, even two bar stools with a Swiffer on top — to try to save spots along residential streets.

With Durham facing a forecast of “icy mix” for this afternoon, you can bet we here at YSPR will be on the lookout for emergent norms.

Statistical Thinking and the Birth of Modern Computing

John von Neumann and the IAS computer, 1945

John von Neumann and the IAS computer, 1945

What do fighter pilots, casinos, and streetlights all have in common? These three disparate topics are all the subject of statistical thinking that led to (and benefitted from) the development of modern computing. This process is described in Turing’s Cathedral by George Dyson, from which most of the quotes below are drawn. Dyson’s book focuses on Alan Turing far less than the title would suggest, in favor of John von Neumann’s work at the Institute for Advanced Studies (IAS). Von Neumann and the IAS computing team are well-known for building the foundation of the digital world, but before Turing’s Cathedral I was unaware of the deep connection with statistics.

Statistical thinking first pops up in the book with Julian Bigelow’s list of fourteen “Maxims for Ideal Prognosticators” for predicting aircraft flight paths on December 2, 1941. Here is a subset (p. 112):

7. Never estimate what may be accurately computed.

8. Never guess what may be estimated.

9. Never guess blindly.

This early focus on estimation will reappear in a moment, but for now let’s focus on the aircraft prediction problem. With the advent of radar it became possible for sorties at night or in weather with poor visibility. In a dark French sky or over a foggy Belgian city it could be tough to tell who was who until,

otherwise adversarial forces agreed on a system of coded signals identifying their aircraft as friend or foe. In contrast to the work of wartime cryptographers, whose job was to design codes that were as difficult to understand as possible, the goal of IFF [Identification Friend or Foe] was to develop codes that were as difficult to misunderstand as possible…. We owe the existence of high-speed digital computer to pilots who preferred to be shot down intentionally by their enemies rather than accidentally by their friends. (p. 116)

In statistics this is known as the distinction between Type I and Type II errors, which we have discussed before. Pilots flying near their own lines likely figured there was a greater probability that their own forces would make a mistake than that the enemy would detect them–and going down as a result of friendly fire is no one’s idea of fun. This emergence of a cooperative norm in the midst of combat is consistent with stories from other conflicts in which the idea of fairness is used to compensate for the rapid progress of weapons technology.

casino-monte-carlo-roulette-monaco-1Chapter 10 of the book (one of my two favorites along with Chapter 9, Cyclogenesis) is entitled Monte Carlo. Statistical practitioners today use this method to simulate statistical distributions that are analytically intractable. Dyson weaves the development of Monte Carlo in with a recounting how von Neumann and his second wife Klari fell in love in the city of the same name. A full description of this method is beyond the scope of this post, but here is a useful bit:

Monte Carlo originated as a form of emergency first aid, in answer to the question: What to do until the mathematician arrives? “The idea was to try out thousand of such possibilities and, at each stage, to select by chance, by means of a ‘random number’ with suitable probability, the fate or kind of event, to follow it in a line, so to speak, instead of considering all branches,” [Stan] Ulam explained. “After examining the possible histories of only a few thousand, one will have a good sample and an approximate answer to the problem.”

For a more comprehensive overview of this development in the context of Bayesian statistics, check out The Theory That Would Not Die.

The third and final piece of the puzzle for our post today is the well-known but not sufficiently appreciated distinction between correlation and causation. Philip Thompson, a meteorologist who joined the IAS group in 1946, learned this lesson at the age of 4 and counted it as the beginning of his “scientific education”:

[H]is father, a geneticist at the University of Illinois, sent him to post a letter in a mailbox down the street. “It was dark, and the streetlights were just turning on,” he remembers. “I tried to put the letter in the slot, and it wouldn’t go in. I noticed simultaneously that there was a streetlight that was flickering in a very peculiar, rather scary, way.” He ran home and announced that he had been unable to mail the letter “because the streetlight was making funny lights.”

Thompson’s father seized upon this teachable moment, walked his son back to the mailbox and “pointed out in no uncertain terms that because two unusual events occurred at the same time and at the same place it did not mean that there was any real connection between them.” Thus the four-year-old learned a lesson that many practicing scientists still have not. This is also the topic of Chapter 8 of How to Lie with Statistics and a recent graph shared by Cory Doctorow.

The fact that these three lessons on statistical thinking coincided with the advent of digital computing, along with a number of other anecdotes in the book, impressed upon me the deep connection between these two fields of thought. Most contemporary Bayesian work would be impossible without computers. It is also possible that digital computing would have come about much differently without an understanding of probability and the scientific method.

The Politics of Beards: Syrian Rebels Edition

Almost all the [Syrian] rebel fighters sport similar facial hair….

Some beards do indeed signify religiosity, especially the bushy Salafist type with only the shadow of a moustache, a style believed by followers to have been favoured by the Prophet Muhammad.

In addition to their religious significance, the beards also have practical and political implications:

Yet many fighters, like Abu Azzam, have beards for other reasons: to seem more devout so as to attract cash from rich conservative donors; to appear more authoritative; to satisfy a personal taste; or simply because their wives like it. “We have no time to shave!” laughs a skinny fighter, bringing up the topic spontaneously.

Despite such jokes, moderate fighters worry that beards may give Westerners a bad impression. Abu Adnan, who leads a small band of fighters in the hills above Latakia, the Assads’ homeland, refuses to be interviewed until he has shaved. Abu Samer, who runs a local revolutionary police station, agrees to meet only after checking that I work for a newspaper rather than a television station. “I know people will interpret my beard the wrong way,” he says. “It’s a bad image to give the revolution.”

From the Economist.

There are some things that just do not fit neatly into existing models of civil war. Nevertheless, bearded rebels are in good company–in their appearance if not their politics. Interestingly enough few American Revolutionaries wore beards and thankfully wigs have fallen out of favor since then.

Fidel Castro

Fidel Castro

Che Guevara

Che Guevara

Ruhollah Khomeini

Ruhollah Khomeini