Predicting the Future Is Easier Than It Looks - By Michael D. Ward and Nils Metternich ... - 0 views
www.foreignpolicy.com/...future_is_easier_than_it_looks
prediction world politics social science model
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The same statistical revolution that changed baseball has now entered American politics, and no one has been more successful in popularizing a statistical approach to political analysis than New York Times blogger Nate Silver, who of course cut his teeth as a young sabermetrician. And on Nov. 6, after having faced a torrent of criticism from old-school political pundits -- Washington's rough equivalent of statistically illiterate tobacco chewing baseball scouts -- the results of the presidential election vindicated Silver's approach, which correctly predicted the electoral outcome in all 50 states.
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Today, there are several dozen ongoing, public projects that aim to in one way or another forecast the kinds of things foreign policymakers desperately want to be able to predict: various forms of state failure, famines, mass atrocities, coups d'état, interstate and civil war, and ethnic and religious conflict. So while U.S. elections might occupy the front page of the New York Times, the ability to predict instances of extreme violence and upheaval represent the holy grail of statistical forecasting -- and researchers are now getting close to doing just that.
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In 2010 scholars from the Political Instability Task Force published a report that demonstrated the ability to correctly predict onsets of instability two years in advance in 18 of 21 instances (about 85%)
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Let's consider a case in which Ulfelder argues there is insufficient data to render a prediction -- North Korea. There is no official data on North Korean GDP, so what can we do? It turns out that the same data science approaches that were used to aggregate polls have other uses as well. One is the imputation of missing data. Yes, even when it is all missing. The basic idea is to use the general correlations among data that you do have to provide an aggregate way of estimating information that we don't have.
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As it turned out, in this month's election public opinion polls were considerably more precise than the fundamentals. The fundamentals were not always providing bad predictions, but better is better.
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In 2012 there were two types of models: one type based on fundamentals such as economic growth and unemployment and another based on public opinion surveys
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There is a tradition in world politics to go either back until the Congress of Vienna (when there were fewer than two dozen independent countries) or to the early 1950s after the end of the Second World War. But in reality, there is no need to do this for most studies.
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Ulfelder tells us that "when it comes to predicting major political crises like wars, coups, and popular uprisings, there are many plausible predictors for which we don't have any data at all, and much of what we do have is too sparse or too noisy to incorporate into carefully designed forecasting models." But this is true only for the old style of models based on annual data for countries. If we are willing to face data that are collected in rhythm with the phenomena we are studying, this is not the case