Tag: prediction

Prediction: For whom the bell tolls?

The idea of prediction in the study of international relations has been a persistent thought in my head for some time. Ostensibly, in our (mostly) non-experimental discipline, prediction represents the preeminent demonstration of a theory’s veracity. Of course, this perspective derives from simplistic conceptions of science as practiced in the natural sciences and as a consequence fit poorly with IR. Regressions struggle to develop models that ‘explain’ more than a small percentage of the variance in the dependent variable(s)—making prediction of outcomes nearly impossible. Our discipline defining structural theories also struggle to make more than vague predictions about systemic patterns—Waltz after all rejected the idea that structural realism is a theory of foreign policy, which would commit the theory to a much more exacting level of prediction. Nonetheless, despite the problems with prediction, my sense is that remains with us as an ideal. Continue reading

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Monti’s Exit: Predictable?

Some of the big news out of Europe this week surrounded Mario Monti’s upcoming resignation from his current post as Prime Minister of Italy. Recall that Monti became prime minister on November 11, 2011 as the leader of a technocratic government. Monti’s government was charged with beginning to dig Italy out from significant economic turmoil, including record bond spreads and fear that Italy could be the next Greece.

Well, as we learned on Monday morning, Monti is resigning as soon as he passes his budget through the Italian parliament. Why did this occur? Due to the withdrawal of support from Silvio Berlusconi’s People of Freedom Party. Financial markets were clearly surprised – and that’s rarely a good thing. The key question from the perspective of my ongoing work is whether or not this was predictable.
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Quote of the Day

Krugman:

In short, there’s no reason at all to consider microeconomics the “real” economics and macroeconomics some kind of flaky impostor. Yes, micro is a lot more rigorous — but if it’s rigorously wrong, who cares?

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What Exactly is the Point of Foreign Policy Analysts?: Lessons from Libya (and any other crisis in history)

I don’t follow the news as closely as I should. I am not up on everyone’s blogs. I don’t check the Brookings Institution for every new report it issues. I am a lazy blogger. But I do seem to recall when this whole Libya thing started and didn’t end successfully immediately that all kinds of “foreign policy analysts” came out of the woodwork to say it wouldn’t work, that we were merely facilitating and prolonging a protracted civil war stalemate. We couldn’t will the means that would be necessary.

Dumbasses.

I say that not because they got the prediction wrong, but rather that they tried to make a prediction at all. I wish we could just admit that we generally have no earthly idea how a civil war, humanitarian intervention, tsunami response, military coup, financial crisis, etc. will work out. I


Eat humble pie, boys. I said, “Eat it!”

am an international relations academic not because I don’t want to be on TV, but because I have a sense of shame and dignity. I simply could not get up in front of millions, or even dozens, of people and claim that I had any notion of how any of those things was going to work out. We can only work out explanations well after the fact when we know what was going on on the ground, what people were thinking, etc. Every social phenomena of interest is simply too complicated. I appreciate it when folks like Steve Saideman tell us the mistakes of the past. But I wouldn’t like it if he predicted the future. And he doesn’t.

I am not going to name names, because I’d have to look them up. Like I said, I’m lazy. But the next time that one of these things happens again, like tomorrow, and someone offers you a prediction of how it will end, respond like this: “Ew, get that away from me. That’s been up your butt.”

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Political Models vs. Current Events

Noah Schachtmann at Wired:

In the last three years, America’s military and intelligence agencies have spent more than $125 million on computer models that are supposed to forecast political unrest. It’s the latest episode in Washington’s four-decade dalliance with future-spotting programs. But if any of these algorithms saw the upheaval in Egypt coming, the spooks and the generals are keeping the predictions very quiet.

Instead, the head of the CIA is getting hauled in front of Congress, making calls about Egypt’s future based on what he read in the press, and getting proven wrong hours later. Meanwhile, an array of Pentagon-backed social scientists, software engineers and computer modelers are working to assemble forecasting tools that are able to reliably pick up on geopolitical trends worldwide. It remains a distant goal.


The benefits and the costs:

“All of our models are bad, some are less bad than others,” says Mark Abdollahian, a political scientist and executive at Sentia Group, which has built dozens of predictive models for government agencies.

“We do better than human estimates, but not by much,” Abdollahian adds. “But think of this like Las Vegas. In blackjack, if you can do four percent better than the average, you’re making real money.”

Over the past three years, the Office of the Secretary of Defense has handed out $90 million to more than 50 research labs to assemble some basic tools, theories and processes than might one day produce a more predictable prediction system. None are expected to result in the digital equivalent of crystal balls any time soon.

In the near term, Pentagon insiders say, the most promising forecasting effort comes out of Lockheed Martin’s Advanced Technology Laboratories in Cherry Hill, New Jersey. And even the results from this Darpa-funded Integrated Crisis Early Warning System (ICEWS) have been imperfect, at best. ICEWS modelers were able to forecast four of 16 rebellions, political upheavals and incidents of ethnic violence to the quarter in which they occurred. Nine of the 16 events were predicted within the year, according to a 2010 journal article [.pdf] from Sean O’Brien, ICEWS’ program manager at Darpa.

Darpa spent $38 million on the program, and is now working with Lockheed and the United States Pacific Command to make the model a more permanent component of the military’s planning process. There are no plans, at the moment, to use ICEWS for forecasting in the Middle East.

All of this, I must say, is pretty predictable.

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Predicting flu outbreaks, fashion trends, and political unrest with social networks

[Cross-posted at Signal/Noise]

Nicholas Christakis and James Fowler have released a new paper that looks at the potential predictive power of social networks.  They claim that current methods of contagion detection are, at best, contemporaneous with the actual epidemic.  What is needed is a true early detection method, one that would actually provide an accurate prediction of a coming epidemic.

Christakis and Fowler claim that social networks can be used as sensors for various types of contagions (whether biological, psychological, informational, etc).  In an inventive twist, they leverage what is known as the Friendship Paradox–the idea that, for almost everyone, a person’s friends tend to have more friends than they do.   Contagions tend to appear sooner in those individuals that are closer to the center of a social network.  The logic goes that if you ask a group of people to name one of their friends, those friends will be closer to the center of the network than the people you asked.  Rather than map and monitor an entire social network, simply monitoring these friends should allow researchers to detect the outbreak of, say, H1N1 much earlier.

They tested their theory using Harvard College undergrads, attempting to detect the outbreak of the flu.  (You can watch Christakis discuss the paper and research during a recent TED talk in the video embed below).  What did they find?

Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 13.9 days (95% C.I. 9.9–16.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (p,0.05) on day 16 of the epidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. The amount of lead time will depend on features of the outbreak and the network at hand. The method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.

That is a pretty impressive result.  By simply tracking those individuals located closer to the center of the network, Christakis and Fowler were about to detect the progression of the flu a full 2 weeks before the general population.  They were also able to derive an early warning signal over a month before the peak of the outbreak in the general population.

If this result can be replicated and validated there are various ways it can be utilized.

Here are a few off the top of my head:

  1. Product Launches: Particularly in the tech industry–where so often we now see product launches as proto-typing–we could use this method to very quickly gauge the awareness and adoption of a new product and predict the extent to which it will spread throughout the general population.  Companies would have better early-warning systems, which would allow for killing dud products or boosting marketing for those products that are poised to explode.  I would assume this would be particularly applicable to products that benefit/rely on network effects.
  2. Political Indicators: One can think of political unrest as a contagion–discontent starting earlier with a core group within a social network and then, over time, spreading to those on the outskirts of the network.  Tracking the population as a whole may not give you an early warning of unrest, but rather a snapshot of a problem at a time when it is too late to do much about it.  Focusing on those closer to the core of a social network could provide enough lead time to diffuse tensions or intervene in other ways to avoid a full-scale upheaval.  Moreover, businesses and investors could also use the early warning as a signal to make adjustments in supply chain and their portfolios to take into account the potential unrest.  Finally, citizens within those countries could benefit by having more lead time to evacuate conflict zones, etc.
  3. Economic Indicators: Investors, businesses, and politicians are always looking for better economic indicators–those signals that are leading indicators of larger economic trends.  I wonder if adjusting the sampling frames of various polls to incorporate the Friendship Paradox might give us an even earlier warning for mortgage defaults, consumer confidence and spending, manufacturing activity, etc.  Not as sure about this one, but certainly much of economic activity takes place in a networked structure.

Would love to hear other thoughts.

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All conventional wisom is not created equal

Jacob Weisberg reminds us that conventional wisdom is often wrong, and then isolates seven instances of “received wisdom” that might prove wrong.

A lot of our premises have turned out to be wrong lately. I’m talking not about evanescent bits of conventional wisdom that have shifted but about overarching assumptions that were widely shared across the political spectrum—big things that experts and nonexperts agreed about—until they were proved false.

For instance, before 1989, virtually all Sovietologists agreed that the USSR was highly stable. Before 2001, few Middle East scholars worried that the United States was vulnerable to a major terrorist attack. Before 2003, everyone from neocon hawks to French lefties agreed that Saddam Hussein had weapons of mass destruction. Before 2008, few economists wondered about the fundamental soundness of the American financial system. Popular opinion echoed the expert consensus on each of these points. Those who challenged the groupthink—such as Soviet dissident Andrei Amalrik, renegade counterterrorism expert John O’Neill, former weapons inspector Scott Ritter, and pessimistic economist Nouriel Roubini—tended to be dismissed as provocateurs, wackos, or (in Ritter’s case) worse.

Some of these examples are pretty darn poor, however.

1. To be blunt, a shitload of people were predicting a major terrorist attack on the United States long before 11 September 2001. I don’t know, of course, about the strange invocation of “Middle East” experts. Maybe Weisberg knows his point wouldn’t withstand scrutiny if it concerned the terrorism scholarly community?

2. Everyone most certainly did not agree that Hussein had “weapons of mass destruction.” Most observers thought Hussein had some kind of residual biological or chemical weapons program. But the ability to produce mustard gas is not synonymous with having WMD, and a lot of people knew it.

The Bush Administration’s great con was collapsing any kind of nuclear, biological, or chemical program into the dreaded “WMD threat,” and a great many people simply didn’t buy it. Weisberg should know better than to perpetuate this fraud six years after the fact.

The rest of the article is kind of enh in an I-need-to-write-an-article-and-I-don’t-have-any-good-ideas-right-now way. Examples include:

“Look, Freeman Dyson says that climate change might not be so bad. The models are uncertain and increased CO2 might lead to better growing conditions for plants” …. “Central authority in China might collapse!” …. “There’s this crazy theory that fossil fuels don’t come from fossils, and the chemical reaction even happens in laboratories!”

That sort of thing.

But, as an international-relations scholar, I feel compelled to comment on one: “Ken Waltz says nuclear proliferation might be stabilizing!”

Well, yeah.

Indeed, as my colleague, Matt Kroenig, has pointed out (PDF), many of the states that actively oppose nuclear proliferation do so precisely because they worry that Waltz is right about the deterrent effects of nuclear weapons.

Such states would, shockingly enough, rather not be deterred from engaging in force projection and various other forms of compellence.

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Next Nostradamus

What are you watching?

I’m tuned to the History Channel’s “Next Nostradamus“:

Two men sharing startling visions of the future possess distinctly different backgrounds: Michel de Nostradamus was a French apothecary and healer in the 16th century; he would become the most famous seer in history. His 21st century counterpart is Dr. Bruce Bueno de Mesquita, a renowned political scientist who teaches game theory at New York University and Stanford. While Nostradamus looked to the stars and mysticism to divine his apocalyptic revelations, Dr. Bueno de Mesquita relies on the most omnipotent tool ever designed by man to predict future events: the computer. This special explores not only the commonalities of these men’s visions about World War III, famine and the coming of the Anti-Christ, but it also traces the evolution from mysticism to hard math, and determines whether science has always existed in prophecy, manifesting itself in different forms through the ages.

I was on the phone and watched most of the first hour with the sound off.

However, in addition to Bueno de Mesquita, I know the program also features John Mearsheimer of the University of Chicago and at least one on-screen appearance each by Ethan Bueno de Mesquita and Alastair Smith. Historian Pamela Smith and a few other scholars are also interviewed.


If you missed the program, it is on again at 1 am ET and Saturday December 6 at 5 pm ET. Check it out. You can also buy it.

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