Seven Guidelines for Better Forecasting

Nice summary by longtime colleague and arch argument mapper Tim van Gelder. “The pivotal element here obviously is Track, i.e. measure predictive accuracy using a proper scoring rule.” If “ACERA” sounds familiar, it’s because they were part of our team when we were DAGGRE: they ran several experiments on and in parallel to the site.

Tim van Gelder

“I come not to praise forecasters but to bury them.”  With these unsubtle words, Barry Ritholz opens an entertaining piece in the Washington Post, expressing a widely held view about forecasting in difficult domains such as geopolitics or financial markets.  The view is that nobody is any good at it, or if anyone is, they can’t be reliably identified.  This hard-line skepticism has seemed warranted by the persistent failure of active fund managers to statistically outperform dart-throwing monkeys, or the research by Philip Tetlock showing that geopolitical experts do scarcely better than random, and worse than the simplest statistical methods.

More recent research on a range of fronts – notably, by the Good Judgement Project, but also by less well-known groups such as Scicast and ACERA/CEBRA here at Melbourne University – has suggested that a better view is what might be termed “tempered optimism” about expert judgement forecasting. This new attitude acknowledges that forecasting challenges will always fall on…

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About ctwardy

2015: Senior Data Scientist, Defense Suicide Prevention Office (via NTVI Federal), and Affiliate Professor, George Mason University. 2008-2015: Research Assistant Professor at George Mason University. Areas: Judgment & Decision-making; Machine learning; Statistical inference; Philosophy of Science; Collective Intellligence; Computational Philosophy.

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