How Could Hurricane Sandy Affect the Election?

Many political scientists and commentators have been asking this question in recent days. For one answer, you can see Mike Munger’s thoughts in Duke Today yesterday. In this post I will consider one unlikely but interesting scenario: what could happen if elections in New Jersey or New York are delayed until after Tuesday?

According to Article 2, Clause 4 of the US constitution,

The Congress may determine the Time of chusing (sic) the Electors, and the Day on which they shall give their Votes; which Day shall be the same throughout the United States.

Obviously the founders felt that it was important that polls be held simultaneously across the states. However, there is some leeway at the state level for special circumstances like a natural disaster.

How would voters in New York and New Jersey  be affected if the election was already decided before they cast their ballots? These are fairly predictable states, unlikely to tip the election on their own. If one candidate already had enough electoral votes for victory and there is zero chance that your vote will matter, I can think of no other explanation than that voting is a form of self-expression.

There two examples that help shed light on this question. One that came to mind was a paper by Thomas Jensen and Asger Lau Andersen on how exit polls affect voter turnout. Their approach is a game theoretic model, but they cite a 2009 referendum vote in Denmark as a motivating example (ungated):

In order to pass, the proposal therefore had to overcome two obstacles: One, a majority of the votes cast in the referendum must be in favor of the proposal. And two, at least 40% of all eligible voters must vote in favor of the proposal. In the weeks preceding the June 7 election, there was no doubt that only the latter of these requirements had the potential to become binding. In a Gallup poll released a week before the election, 84% of respondents indicated that they approved of the proposal to change the law. However, only 40.2% responded that they would show up at polls and vote in favor of the proposal.

On the afternoon of the election day, TV2, a major Danish TV channel, published the results of an exit poll, which predicted that 37.9% of all eligible voters would cast a vote in favor of the proposal to change the law. However, during the evening the situation turned around with pollsters reporting a considerable increase in turnout. In the end, the official result was that 45.1% of all eligible voters had voted in favor of the proposal, which corresponded to 85.4% of all votes cast. Thus, the proposal passed with a comfortable margin.

So in this instance, receiving information about the likelihood of a particular outcome before polls closed affected the vote. But that still does not answer the question of how voters react when they have zero chance of changing the result.

For that, we turn to an example from France. The French government tries to avoid the fate of the Danish referendum by taking several strong measures. From Wikipedia:

Elections are always held on Sundays in France. The campaigns end at midnight the Friday before the election; then, on election Sunday, by law, no polls can be published, no electoral publication and broadcasts can be made. The voting stations open at 8 am and close at 6 pm in small towns or at 8 pm in cities, depending on prefectoral decisions. By law, publication of results or estimates is prohibited prior to that time; such results are however often available from the media of e.g. Belgium and Switzerland, or from foreign Internet sites, prior to that time. The first estimate of the results are thus known at Sunday, 8pm, Paris time; one consequence is that voters in e.g. French Guiana, Martinique and Guadeloupe knew the probable results of elections whereas they had not finished voting, which allegedly discouraged them from voting. For this reason, since the 2000s, elections in French possessions in the Americas, as well as embassies and consulates there, are held on Saturdays as a special exemption.

Gaining information about the expected outcome of an election does appear to affect turnout. It seems that if voters can still make a difference they are more likely to show up and vote for their desired outcome, while if the election is already decided they will stay home. As I said at the outset this is an unlikely scenario, but you can bet that political scientists will be keeping an eye on this chance for a true natural experiment.

What Did Manifest Destiny Look Like?

“Manifest Destiny was the belief widely held by Americans in the 19th century that the United States was destined to expand across the continent. The concept, born out of ‘a sense of mission to redeem the Old World’, was enabled by ‘the potentialities of a new earth for building a new heaven.'” (Wikipedia, citing Frederick Merk)

Now, Michael Porath has told the story of manifest destiny in a series of 141 maps. The main technical trick is that Porath designed the site in HTML5, so it has some nice interactive features. The maps appear on a single page in four columns but you can click any of them for a close-up with an explanation of the changes, or mouse-over a region of the map to see what political entity it was under at the time (e.g. unorganized territory, Spanish colony).

There are two additions that I think would help improve this project. The first is a sense of time scale–some of the maps are only a month apart (January and February 1861, for example) while others are separated by several decades (March 1921 and January 1959). Adding time would allow for a second feature: an animation that would show the areas of change and continuity over time. An excellent example of this is David Sparks’ choropleth maps of presidential voting over time.  I do not know whether this could still be done in Porath’s HTML5 setup, but it is often useful to think about changes to graphical displays (additions or subtractions) that would help to convey meaningful information. What other suggestions do you have for these maps?

What is the Future of Publishing?

Today’s journal publishing system is the best possible. If you limit yourself to 17th century technology, that is.

Quips like these were sprinkled throughout Jason Priem’s presentation on altmetrics at Duke on Monday. Altmetrics is short for “alternative metrics,” or ways of measuring the impact of a particular author or article rather than the canonical impact factor of journals (which, it turns out, was initially resisted; Thomas Kuhn FTW).

Priem is a doctoral candidate at UNC, and recently started a site called ImpactStory. According to the LSE blog:

ImpactStory is a relaunched version of total-impact. It’s a free, open-source webapp we’ve built (thanks to a generous grant by the Sloan Foundation and others) to help researchers tell these data-driven stories about their broader impacts. To use ImpactStory, start by pointing it to the scholarly products you’ve made: articles from Google Scholar Profiles, software on GitHub, presentations on SlideShare, and datasets on Dryad (and we’ve got more importers on the way).

Then we search over a dozen Web APIs to learn where your stuff is making an impact. Instead of the Wall Of Numbers, we categorize your impacts along two dimensions: audience (scholars or the public) and type of engagement with research (view, discuss, save, cite, and recommend).

Priem’s presentation was informative and engaging. He has clearly spent a good deal of time thinking about academic publishing, and about the scientific undertaking more generally. I particularly liked how he responded to some tough audience questions about potential for gaming the system by re-iterating that we do not want a “Don Draper among the test tubes,” but for better or worse the way that we communicate our ideas makes a difference in how they are received.

If you are interested in hearing more of Jason’s ideas, here is a video of a similar talk he gave at Purdue earlier this year. The altmetrics portion starts around the 25-minute mark.

When Will Cuba Experience Regime Change?

Castro Assuming Power

I do not have a specific, model-based prediction, but I will go on record here as saying that I expect Cuba’s current regime to fall within 4-5 years. My hunch is based on Cuba’s easing of travel restrictions. The BBC’s Sarah Rainsford thinks this is an important change too:

It was more than a year ago that the President Raul Castro said that the system would change and the Cubans have been waiting impatiently ever since. So, it is a big deal. It does mean a little bit more freedom for people here on the island who want to travel. It essentially means that they won’t face a bill of $350 all told for all the paperwork involved which, for a country where the average salary…average monthly salary is $20 is obviously very significant.

Reading a paper by Timur Kuran from 1989 on revolutions (ungated pdf), I came across this quote by Tocqueville that seems apropos:

[I]t is not always when things are going from bad to worse that revolutions break out. On the contrary, it oftener happens that when a people which has put up with an oppressive rule over a long period without protest suddenly finds the government relaxing its pressure, it takes up arms against it. Thus the social order overthrown by a revolution is almost always better than the one immediately preceding it, and experience teaches us that, generally speaking, the most perilous moment for a bad government is one when it seeks to mend its ways.

Kuran thinks that revolutions are inherently unpredictable, but I would argue that this mechanism proposed by Tocqueville can inform our expectations about what will happen in Cuba–and elsewhere–in the coming years.

Afghanistan Casualties Over Time and Space

The data comes from the Defense Casualty Analysis System for Operation Enduring Freedom. Here it is over time:

Notice the seasonality of deaths in Afghanistan, likely due to the harsh winters. Here is the same data plotted across space (service member home towns):

Not surprisingly, hometowns of OEF casualties are similar to those of service members killed in Iraq.

What Are the Chances Your Vote Will Matter?

Only one vote matters. In the United States, the vote that gives a presidential candidate the majority in the state that tips the electoral college decides it all. Nevertheless, about 122 million US voters went to the polls for the 2008 Presidential election.

If the only benefit you get from voting is your candidate winning, this behavior is totally irrational. Voters spend precious time and effort traveling to the polls or arranging for mail-in ballots, with very small odds that this will make any difference in the final outcome. Of course, the simplest explanation is that this argument is wrong and voting can be rational, but you could also say that voting is self-expression.

In a recent paper (gated), Douglas VanDerwerken
takes a slightly different approach. He estimates a one in 2.5 million chance that his vote will matter this year, given that he lives in North Carolina (a competitive state in 2008, and likely in 2012 too).* But then he points out that, “Even if your vote does not have an effect on the election, it can certainly have an effect on you.” His broader message is that:

Statistics is not divorced from subjectivity, nor from morality. What you decide depends on your moral axioms.

We can use statistics to inform our objective calculations, and our subjective intuitions, but decision-making is not a “plug and chug” process. In summarizing data, the statistician makes important decisions about how to abstract away from reality and what message to send. When that information as inputs for further decision-making–which always involves trade-offs–the statistician bears some responsibility for the outcome. Once again we are reminded that statistics is a rhetorical practice. (See also here and here.)


*Full disclosure: Doug teaches the lab section of a Duke statistics course in which I am currently enrolled.

Atwood on Internet Communities and Politics

Jeff Atwood, creator of Stack Overflow and Stack Exchange, has collated some of his best blog posts into an ebook. The “Stack” sites are question-and-answer fora, often with valuable, timely feedback.

In “The Vast and Endless Sea,” Atwood describes the motivations for highly skilled programmers and other professionals to donate their time, free of charge, to the community.

Nobody is participating in Stack Overflow to make money. We’re participating in Stack Overflow because…

  • We love programming
  • We want to leave breadcrumb trails for other programmers to follow so they can avoid making the same dumb mistakes we did
  • Teaching peers is one of the best ways to develop mastery
  • We can follow our own interests wherever they lead
  • We want to collectively build something great for the community with our tiny slices of effort

I would add to that list the fact that participation and the contribution of public goods also builds reputation within the community. In fact, the way you gain reputation is codified by Stack Overflow, and there was something of a furor when the reputation scoring method changed.

The availability of this public good also comes with some expectations of the inquirers. Atwood clarifies the normative expectations for questions in “Rubber Duck Problem Solving.”

At Stack Exchange, we insist that people who ask questions put some effort into their question, and we’re kind of jerks about it. That is, when you set out to ask a question, you should…

  • Describe what’s happening in sufficient detail that we can follow along. Provide the necessary background for us to understand what’s going on, even if we aren’t experts in your particular area.
  • Tell us why you need to know the answer. What led you here? Is it idle curiosity or somehow blocking you on a project? We don’t require your whole life story, just give us some basic context for the problem.
  • Share any research you did towards solving your problem, and what you found, if anything. And if you didn’t do any research – should you even be asking?
  • Ultimately, this is about fairness: if you’re going to ask us to spend our valuable time helping you, it’s only fair that you put in a reasonable amount of your valuable time into crafting a decent question. Help us help you!

This online community is interesting for a number of reasons, not the least of which is its emphasis on norms. Another micro-institution–they’re everywhere!

Micro-Institutions Everywhere: Elevators

On your own, you can do whatever you want – it’s your own little box.

If there are two of you, you take different corners. Standing diagonally across from each other creates the greatest distance.

When a third person enters, you will unconsciously form a triangle (breaking the analogy that some have made with dots on a dice). And when there is a fourth person it’s a square, with someone in every corner. A fifth person is probably going to have to stand in the middle.

Now we are in uncharted territory. New entrants to the lift will need to size up the situation when the doors slide open and then act decisively. Once in, for most people the protocol is simple – look down, or examine your phone.

From the BBC.

Simulating the NLDS: Can the Giants Win?

In Allen Downey’s new book, Think Bayes, he relates the “Boston Bruins” problem. The problem is to estimate the Bruins’ probability of winning the 2010-2011 NHL championship after two wins and two losses. I will briefly describe Downey’s approach, and then relate it to the current situation of the San Francisco Giants.

One (naive) approach would be to model this as a gambler’s ruin problem. There are two problems with that model for this problem: the total number of rounds to be played is uncertain (i.e. the championship is a best of n rather than play until one side is totally defeated), and it throws away important information about the score of the games.

Instead, we model baseball as a Poisson process, in which it is equally likely for a run to be scored at any time during the game. This is still somewhat of an oversimplification (the odds are better when you have runners on base, for example), but we are getting closer to the “true” model. Second, we assume that games between the Reds and Giants in this year’s National League Division Series are similar enough that they can be considered as outcomes from Poisson distributions in which each team’s scoring distribution is consistent between games with parameter λ. Different pitchers could cause this assumption to be thrown off (no pun intended), but we will again use it as a not-entirely-implausible simplification.

Having made our assumptions, we now use a four-step process proposed by Downey:

1. Use statistics from previous games to choose a prior distribution for λ.
2. Use the score from the first four games to estimate λ for each team.
3. Use the posterior distributions of λ to compute distribution of goals for each team, the distribution of the goal differential, and the probability that each team wins.
4. Simulate the rest of the series to estimate the probability of each possible outcome.

To calculate λ, we will use the team batting stats from ESPN and the thinkbayes Python package from Downey’s site.

Here is the distribution of λ, using regular season scoring as the prior and updating with the results of the first four games of the division series:

And here are the predicted runs-per-game by team, using simulations:

According to the model’s predictions, the probability that the Giants win today’s game (and the division series) is 0.387. I would have preferred to use a Gamma prior for λ and run some more simulations in R, but I wanted to use Downey’s example and get this up before the game started… which was a few minutes ago (although as I post, the score is still 0-0). Either way, enjoy the game!

New Conflict Forecasting Website

Wardlab is the working group run by Michael D. Ward. The lab has a new website: You can find out about our ongoing projects, download software packages, or follow the Conflict Forecast blog.

The team includes some really smart people, several of whom have their own websites.

The site is still in a beta version, but many in this blog’s audience are interested in political forecasting and conflict, so I thought I would go ahead and share.