Prediction and Good Judgment: Can ICEWS Inform Forecasts?
The prediction community owes a great deal to Phil Tetlock, who has been involved in some of the largest and longest evaluations of expert forecasts to date. Tetlock is perhaps most widely known for his two-decade long study of political forecasters, which found that “foxes” (who know a little about a lot of different topics) typically outperform “hedgehogs” (who know a lot about one specific domain) in near-term forecasting. Over the last three years, Tetlock, Barbara Mellers, and Don Moore have led the Good Judgment Project, a large-scale forecasting tournament.
The Good Judgment Project began in mid-2011 as a forecasting tournament between five teams, sponsored by the US Government. (Read early coverage of the project from The Economist here.) Each of these teams had its own methods for leveraging the knowledge of its members to generate accurate forecasts about political and economic developments around the world. For example, the Good Judgment Team now assigns its forecasters to smaller teams of about a dozen members. This allows for collaboration in sharing information, discussing questions, and keeping each member motivated. Example questions include “What will the highest price of one ounce of gold be between January 1, 2014 and May 1, 2014?” or “Who will be the King of Saudi Arabia on March 15, 2014?” Predictions are scored both individually and as a team using Brier scores.
Season 3 of the tournament began this summer, and for the first time forecasters now have access to information from ICEWS, provided directly by the ICEWS project. ICEWS covers five events of interest (insurgency, rebellion, ethnic or religious violence, domestic political crisis, and international crisis) around the world on a monthly basis, and makes forecasts six months into the future. Two current Good Judgment questions related to ICEWS are:
- Will Chad experience an onset of insurgency between October 2013 and March 2014?
- Will Mozambique experience an onset of insurgency between October 2013 and March 2014?
One piece of advice that Good Judgment forecasters are often reminded of is to use the baseline rate of an event as a starting point for their forecast. For example, insurgencies are a very rare event on the whole. For the period January, 2001 to August, 2013, insurgencies occurred in less than 10 percent of country-months in the ICEWS data set. The map below shows the proportion of months in which a country experienced insurgency, with darker red representing more months of insurgency:
From this baseline, we can then incorporate information about the specific countries at hand and their recent history. Neither country in the questions above was coded as experiencing an insurgency for the twelve months ending in August 2011. In fact, Mozambique has not experienced an insurgency for the entire period of the ICEWS dataset. On the other hand, Chad had an insurgency that ended in December, 2003, and another that extended from November, 2005, to April, 2010. For the duration of the ICEWS data set, Chad has experienced an insurgency 59 percent of the time. This suggests that our predicted probability of insurgency in Chad should be higher than for Mozambique.
Of course, this information about the past is not a perfect indicator of what is to come. Forecasters have their own background knowledge about the two countries and regional politics, and keeping up with current events is an important part of participation in the tournament. This example shows that a few basic heuristics, combined with the information provided by ICEWS, can potentially help inform forecasters and improve their accuracy. At least, that is the proposition we are examining.
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As a Good Judgment forecaster, I read your post as suggesting that we have access to ICEWS forecasts. We have access to ICEWS’s past data, but not its forecasts.
Yes, thanks for that clarification, Bob. I haven’t seen any direct forecasts from ICEWS during my participation in the Good Judgment Project either–apologies if the post implied otherwise.
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