Forecasting Round-Up No. 7

1. I got excited when I heard on Twitter yesterday about a machine-learning process that turns out to be very good at predicting U.S. Supreme Court decisions (blog post here, paper here). I got even more excited when I saw that the guys who built that process have also been running a play-money prediction market on the same problem for the past several years, and that the most accurate forecasters in that market have done even better than that model (here). It sounds like they are now thinking about more rigorous ways to compare and cross-pollinate the two. That’s part of what we’re trying to do with the Early Warning Project, so I hope that they do and we can learn from their findings.

2. A paper in the current issue of the Journal of Personality and Social Psychology (here, but paywalled; hat-tip to James Igoe Walsh) adds to the growing pile of evidence on the forecasting power of crowds, with an interesting additional finding on the willingness of others to trust and use those forecasts:

We introduce the select-crowd strategy, which ranks judges based on a cue to ability (e.g., the accuracy of several recent judgments) and averages the opinions of the top judges, such as the top 5. Through both simulation and an analysis of 90 archival data sets, we show that select crowds of 5 knowledgeable judges yield very accurate judgments across a wide range of possible settings—the strategy is both accurate and robust. Following this, we examine how people prefer to use information from a crowd. Previous research suggests that people are distrustful of crowds and of mechanical processes such as averaging. We show in 3 experiments that, as expected, people are drawn to experts and dislike crowd averages—but, critically, they view the select-crowd strategy favorably and are willing to use it. The select-crowd strategy is thus accurate, robust, and appealing as a mechanism for helping individuals tap collective wisdom.

3. Adam Elkus recently spotlighted two interesting papers involving agent-based modeling (ABM) and forecasting.

  • The first (here) “presents a set of guidelines, imported from the field of forecasting, that can help social simulation and, more specifically, agent-based modelling practitioners to improve the predictive performance and the robustness of their models.”
  • The second (here), from 2009 but new to me, describes an experiment in deriving an agent-based model of political conflict from event data. The results were pretty good; a model built from event data and then tweaked by a subject-matter expert was as accurate as one built entirely by hand, and the hybrid model took much less time to construct.

4. Nautilus ran a great piece on Lewis Fry Richardson, a pioneer in weather forecasting who also applied his considerable intellect to predicting violent conflict. As the story notes,

At the turn of the last century, the notion that the laws of physics could be used to predict weather was a tantalizing new idea. The general idea—model the current state of the weather, then apply the laws of physics to calculate its future state—had been described by the pioneering Norwegian meteorologist Vilhelm Bjerknes. In principle, Bjerkens held, good data could be plugged into equations that described changes in air pressure, temperature, density, humidity, and wind velocity. In practice, however, the turbulence of the atmosphere made the relationships among these variables so shifty and complicated that the relevant equations could not be solved. The mathematics required to produce even an initial description of the atmosphere over a region (what Bjerknes called the “diagnostic” step) were massively difficult.

Richardson helped solve that problem in weather forecasting by breaking the task into many more manageable parts—atmospheric cells, in this case—and thinking carefully about how those parts fit together. I wonder if we will see similar advances in forecasts of social behavior in the next 100 years. I doubt it, but the trajectory of weather prediction over the past century should remind us to remain open to the possibility.

5. Last, a bit of fun: Please help Trey Causey and me forecast the relative strength of this year’s NFL teams by voting in this pairwise wiki survey! I did this exercise last year, and the results weren’t bad, even though the crowd was pretty small and probably not especially expert. Let’s see what happens if more people participate, shall we?

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