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For that reason, it may yield rather fragile predictions about the future. Experimental evidence bears this out. Take sports prediction. In principle, this should draw on a complex array of historical data, optimally weighted. That is why complex, data-hungry algorithms are used to generate rankings for sports events, such as the FIFA world rankings for football teams or the ATP world rankings for tennis players. These complex algorithms are designed to fit performance data from the past. Yet, when it comes to out-of-sample prediction, these complex rules perform miserably. In fact, they are often inferior to simple alternatives. One such alternative would be the “recognition heuristic” – picking a winning team or player purely on the basis of name-recognition. This simple rule out-performs the ATP or FIFA rankings (Serwe and Frings (2006), Scheibehenne and Broder (2007)). One good reason beats many. It is not just sports prediction. Experimental evidence has found the same to be true across a range of other activities. Among physicians diagnosing heart attacks, simple decision trees beat a complex model. 3 Among detectives locating serial criminals, simple locational rules trump complex psychological profiling. 4 Among investors picking stocks, simple passive strategies outperform complex active ones. 5 And among shopkeepers understanding spending patterns, repeat purchase data out-predict complex models. 6 Applying complex decision rules in a complex environment may be a recipe not just for a cock-up but catastrophe. In “Natural Accidents”, sociologist Charles Perrow demonstrated how catastrophe was more likely to strike in complex, interconnected – “tightly coupled” – systems (Perrow (1984)).