Category Archives: Uncategorized

Not Intervening in Syria Might Help Us with Iran

So Syria’s chemical weapons have made life a little more interesting lately. I know enough to know that I don’t know enough to say whether we should intervene in Syria. I do know something about Iran, though. Here, I’m briefly going to argue that not intervening (or a very limited intervention) in Syria could help our ongoing negotiations with Iran regarding its nuclear program.

Before going further, I want to emphasize that this departs from the conventional wisdom. The standard argument is that failure to intervene in Syria shows weakness–if we aren’t resolved enough to attack Syria, we are less likely to attack Iran, so we should attack Syria to maintain the plausibility of the bluff.

The problem with this argument is that it overlooks the scope of these missions. We could plausibly commit to an air campaign in Syria a la Libya from a few years ago. Iran’s nuclear program, however, is complicated enough that air strikes might do more harm than good. And if you look at public opinion polls on Syria, a majority(ish) supports strikes. Due to Iraq’s shadow, the support for a ground war is in the single digits. Thus, the overall American war narrative here is that air strikes are okay but ground wars are not.

Back to Iran. One reason we cannot reach an agreement with Iran is our inability to credibly commit to a bargain. Given our negotiation history, Iran is worried that if we ever find ourselves in a position of strength again (like right after the fall of Baghdad but before the start of the insurgency), we will immediately cut all concessions to Tehran. Consequently, Iran views nuclear weapons as a costly insurance policy–a wasteful but necessary evil. Yet, if we could simply keep our commitment to not intervene credible, Iran would have no need to proliferate, and we would all be better off.

So if we state a full-scale assault on Syria, we officially signal that the lessons from Iraq are irrelevant, and we as Americans just don’t mind seeking total victory against weaker states. If we don’t do anything to Syria, or just limit the intervention to light aerial bombardments, we signal that Iraq ushered in a new era in which we just don’t do that type of thing anymore. In the former case, Iran will scramble to finish a nuclear bomb as quickly as possible. In the latter case, we reassure Tehran that our commitments to nonproliferation inducements are credible.

The whole thing is a strategic mess. But if you want to learn more about credible commitment in nonproliferation agreements (and incidentally preview my upcoming Peace Science presentation), check out this chapter from my dissertation.

APSA Presentation: The Theory of Butter-for-Bombs Agreements

Want to learn about negotiating over nuclear weapons? Come to the Stevens Meeting Center Salon D at 2 pm on Friday.

Download the paper here.

Abstract
This paper develops a model of negotiating over costly weapons programs. Surprisingly, in equilibrium, rising states rarely invest in arms. First, if the extent of the power shift is large, the declining state leverages the threat of preventive war to induce the rising state not to build. Second, if the power shift is too small to be worth the investment, the declining state offers no concessions and still induces non-armament. In between, if the cost are sizable, the declining state offers concessions-for-weapons, or butter-for-bombs deals. Even though the rising state could take those concessions and build anyway, it nevertheless accepts the payments and maintains the status quo. Armament only occurs in the least important of cases–that is, when the power shift is minimal and not costly. The results indicate that major power shifts–such as those caused by nuclear proliferation–are not non-negotiable and are instead the result of other bargaining problems.

Why Do Prisoners Cooperate in the Prisoner’s Dilemma?

According to a new study by Menusch Khadjavi and Andreas Lange, prisoners cooperate more frequently in prisoner’s dilemmas than college students. Here’s the abstract from their article “Prisoners and Their Dilemma”:

We report insights into the behavior of prisoners in dilemma situations that so famously carry their name. We compare female inmates and students in a simultaneous and a sequential Prisoner’s Dilemma. In the simultaneous Prisoner’s Dilemma, the cooperation rate among inmates exceeds the rate of cooperating students. Relative to the simultaneous dilemma, cooperation among first-movers in the sequential Prisoner’s Dilemma increases for students, but not for inmates. Students and inmates behave identically as second movers. Hence, we find a similar and significant fraction of inmates and students to hold social preferences.

I have always thought that the prisoner’s dilemma was a terrible example of strict dominance for introductory classes in game theory. Students tend to shoot back with “snitches get stitches” or something similar, so prisoners would cooperate in such a situation. This leads to an awkward conversation about expected utilities when all you really want to do is explain the logic of strict dominance. The study further suggests we drop the prisoner story from the prisoner’s dilemma.[1]

Nevertheless, after having read the article, I have serious problems with the results. The article is extremely short on theoretical mechanisms, so I am going to step in and provide some speculation. Here are three explanations for the result:

  1. The mechanism the authors would prefer is that prisoners are incredibly strategic. To quote Red from Shawshank Redemption, prison time is slow time. Prisoners have nothing better to do than plot, strategize, and scheme against one another.[2] While this might initially appear detrimental to cooperation, the truth is just the opposite. Everyone knows you can’t get away with doing stupid things, so people don’t bother. As such, prisoners cooperate–even though the game is anonymous, cooperation is less likely to lead to a witch hunt later and less likely to cause problems greater than a few Euros worth of phone credit.
  2. Prisoners didn’t believe that the game really is anonymous. The experimenters stressed to participants that they would be the only one to see the results. However, this is utterly ridiculous. No one, NO ONE, NO ONE took that statement at face value. Marek Kaminski, a political scientist at UC Irvine, spent some time in a Polish prison for publishing anti-communist materials way back in the day. He wrote Games Prisoners Play based off of his experiences.[3] It is a compelling read. Kaminski might be the only serious social scientist to spend time in a prison. He makes a big point that we really shouldn’t trust any studies of prisoners simply because prisoners do not trust supposedly confidential experimenters. At all. Prisoners might have cooperated in the study because they believed the prison staff would see the results, or other prisoners would see the results, or whatever. College students, meanwhile, know the results will be confidential and are therefore free to defect all they wish.
  3. The results have nothing to do with the prison/free dichotomy but rather education levels. Non-prisoners in the study were all college educated. The prisoners averaged below 10 years of schooling. The authors only obtain statistically significant results without any controls. Once they popped in education level, the only thing that was statistically significant was coffee consumption. (Good luck explaining that result theoretically!) But education and the prisoner dummy are about as multicollinear as multicollinearity gets. We probably shouldn’t trust the coefficients on either of those variables. But this also would mean that education would be statistically significant if you ran a regression without any controls. Perhaps the prisoners just don’t see that defection strictly dominates cooperation. The data tell us nothing here.[4]

On the surface, the paper is neat. However, authors of any quantitative model need to think hard about their data generating process and then construct their research design model accordingly. The authors don’t do that here, especially when it comes to point 3. As such, this study is…lacking.

[1]But this is a coordination problem, and we are well past that tipping point.
[2]This is why Orange Is the New Black is an interesting series.
[3]The authors do not cite Kaminski. It should be required reading (and a required citation) for all studies of prisoners.
[4]I would imagine someone has previously studied the effect of education level on prisoner’s dilemma cooperation, but I am unaware of any such study and the authors do not cite any.

How to Calculate Your Total Time Listening to Your iTunes/iPhone/iPod

A couple of days ago, I wondered what percentage of my life I listen to music. Fortunately, I figured out exactly how to calculate it. I have been using iTunes since I got my first iPod for my 18th birthday in 2005. Three computers and four iPods later, I have managed to preserve my playcounts for the last eight years. A little bit of Excel handiwork revealed that I have spent about 450 days listening to that music, or about an eighth of all my time. That’s about a quarter of all my waking hours!

Want to calculate your time? Here is the process in ten steps. If you have even the slightest experience with formulas in Excel, this will be painless:

1) Open your iTunes and go to your songs tab. Make sure iTunes is showing a column for Time and Plays. (If it isn’t, you can fix this by right clicking on the columns and checking the appropriate categories.)

2) Press CTRL+A to highlight all the songs and then press CTRL+C to copy them.

step 2

3) Open Excel and press CTRL+V to paste all the information. Surprisingly, the information nicely conforms to the cells.

4) iTunes gives a lot of information superfluous to our purposes. Ignore everything but the column for Time and Plays (columns B and I for me).

step 4

5) Create a new column by multiplying the time column by 24.

6) Highlight your new column (column K for me) and use the drop down formatting menu to convert it to a number.

step 6

You might be thinking “why on earth are we doing this?” for the last two steps. Good question. This is part is fancy, so bear with me. Excel treats the time column as a time of day rather than a number of minutes and seconds. For example, it thinks cell B1 refers to 5:09 AM, not 5 minutes, 9 seconds. Excel stores this information as a proportion of a 24 hour day. As such, it thinks of 5:09 AM as approximately 0.21458. Multiplying by 24 and converting it to a number gives us the number of minutes each song is.

7) Create a new column by multiplying the previous new column by the Plays column. (For me, this is multiplying column I by column K.) The result is the total number of minutes you have listened to each song.

This in itself is interesting, and you can use Excel to sort the songs by the most time you have spent listening to them. This list can be quite different from your most played songs, as a 5 minute song counts for twice as much as a 2 minute 30 second song.

8) Use the SUM function to sum the column you created in step 7. This is the total number of minutes you have spent listening to iTunes.

step 8

9) Divide by 60 to get the number of hours you have spent listening to iTunes.

10) Divide step 9 by 24 to get the number of days you have spent listening to iTunes.

Can anyone top 450 days? Post your times in the comments below…

How to Use American Airlines’ “Hold Tickets” Option for Cheap Airfare

I was booking plan tickets for an upcoming trip when I discovered this hack. If you book on American Airlines’ website, it gives you the option to hold your tickets at the current price for 24 hours. Always use this option. Wait until the next day, and input your travel itinerary again. Check the price. If it is cheaper, cancel your current hold and book again for the cheaper price. Then repeat this process the next day. If the fare goes up, buy at the previous day’s price.

There is virtually no cost to doing this–the hold option is free, so the only cost is time it takes to reenter a flight itinerary–and has the potential to save you a lot of money. For example, my fare was $641 two days ago. Yesterday, it was $515. Today, it is up to $680. Consequently, I bought my tickets for yesterday’s price and saved $126. Not bad for five minutes’ effort.

(The extreme price fluctuations make no sense to me either, but that is a different discussion.)

Note that this only works if you book through aa.com. Travel aggregation websites like Kayak and Orbitz do not allow holds. Same goes for other airlines as far to my knowledge. However, if you are not committed to flying with American but American is still the cheapest option, you should still hold the tickets for 24 hours. Then, on the next day, you can rerun your search on Kayak or Orbitz and see if any of the other fares beat the one you have on hold. If so, buy from the competitor–but note that you won’t be able to hold the competitor’s price and repeat the process on the next day.

Which Day Was the Midterm?

stats

Hmm…

Theory, Assumptions, and a God-Awful Final Jeopardy

In case you missed it, last night’s Final Jeopardy was flat terrible. This was the semifinal game in the teen tournament; only the top (strictly positive) scorer advanced to the next round, and no one keeps any money. The scores were $16,400, $12,000, and $1,200. The Final Jeopardy category was capital cities. Pretend you are the leader and place your wager.

Ready? Cue music:

It’s criss-crossed by dozens of “peace walls” that separate its Catholic & Protestant neighborhoods

Was your response Dublin? Mine was, as was all of the contestants’. Dublin is also wrong. The correct response was Belfast.

Nothing wrong with a triple stumper, though. The wagering strategies, on the other hand, were horrible. Every contestant wagered everything. With no one coming up with the correct response, no one had any money and thus no one qualified for the finals.

This made me go insane. The leader had no reason to wager more than $7,601; such a wager ensures that the leader wins with certainty if he receives the correct response and also gives him a win against a wider variety of opposing bids, including the set of bids from the game. In game theory terms, wagering $7,601 weakly dominates wagering everything.

I then vented in YouTube form:

Here’s a comment from the YouTube view page:

This is why the idea that people are intelligent self interested agents makes me laugh. People do this kind of thing ALL THE TIME, and it’s why economic theories that don’t account for this can’t predict [stuff].

Only he didn’t say stuff.

There are two big problems with this logic. First, rational self-interest is an assumption. We use assumptions to build theories not for their accuracy but for their usefulness. The better metric for modeling is a simple question: is this model more useful than the alternative? If yes, the model is satisfactory. If not, then use the alternative. We could discard certain reality and instead use some probability distribution over rational agents and automaton agents. While this would certainly be a more realistic model, it would come at the expense of being substantially more computationally intensive without much obvious reward. We should find no inherent shame in simplicity.

Second, a good theory explains and predicts behavior. Theories are not laws–we should not require a theory to hold 100% of the time for us to find a theory useful. Contrary to what the commentator wrote, we can use “intelligent, self-interested agents” as an assumption and predict quite a lot. In fact, the reason Final Jeopardy last night caused such a stir is because it egregiously violated what intelligent individuals should do. Intelligent individuals make up about 99.9% of the Jeopardy players, which is what made last night so extraordinary.

If models are useless because of the .1%, then all of academia–hard and soft science alike–needs to close up shop immediately.