From: Jurriaan Bendien (adsl675281@TISCALI.NL)
Date: Sun Mar 25 2007 - 11:05:33 EDT
Maybe We Should Leave That Up to the Computer By DOUGLAS HEINGARTNER Published: July 18, 2006 AMSTERDAM - Do you think your high-paid managers really know best? A Dutch sociology professor has doubts. The professor, Chris Snijders of the Eindhoven University of Technology, has been studying the routine decisions that managers make, and is convinced that computer models, by and large, can do a better job of it. He even issued a challenge late last year to any company willing to pit its humans against his algorithms. "As long as you have some history and some quantifiable data from past experiences," Mr. Snijders claims, a simple formula will soon outperform a professional's decision-making skills. "It's not just pie in the sky," he said. "I have the data to support this." Some of Mr. Snijders's experiments from the last two years have looked at the results that purchasing managers at more than 300 organizations got when they placed orders for computer equipment and software. Computer models given the same tasks achieved better results in categories like timeliness of delivery, adherence to the budget and accuracy of specifications. No company has directly taken Mr. Snijders up on his challenge. But a Dutch insurer, Interpolis, whose legal aid department has been expanding rapidly in recent years, called in Mr. Snijders to evaluate a computer model it had designed to automate the routing of new cases - a job previously handled manually by the department's in-house legal staff. The manager in charge of the project, Ludo Smulders, said the model was much faster and more accurate than the old system. "We're very satisfied about the results it's given our organization," he said. "That doesn't mean there are no daily problems, but the problems are much smaller than when the humans did it by hand. And it lets them concentrate more on giving legal advice, which is what their job is." Mr. Snijders's work builds on something researchers have known for decades: that mathematical models generally make more accurate predictions than humans do. Studies have shown that models can better predict, for example, the success or failure of a business start-up, the likelihood of recidivism and parole violation, and future performance in graduate school. They also trump humans at making various medical diagnoses, picking the winning dogs at the racetrack and competing in online auctions. Computer-based decision-making has also grown increasingly popular in credit scoring, the insurance industry and some corners of Wall Street. The main reason for computers' edge is their consistency - or rather humans' inconsistency - in applying their knowledge. "People have a misplaced faith in the power of judgment and expertise," said Greg Forsythe, a senior vice president at Schwab Equity Ratings, which uses computer models to evaluate stocks. The algorithms behind so-called quant funds, he said, act with "much greater depth of data than the human mind can. They can encapsulate experience that managers may not have." And critically, models don't get emotional. "Unemotional is very important in the financial world," he said. "When money is involved, people get emotional." Many putative managerial qualities, like experience and intuition, may in fact be largely illusory. In Mr. Snijders's experiments, for example, not only do the machines generally do better than the managers, but some managers perform worse over time, as they develop bad habits that go uncorrected from lack of feedback. Other cherished decision aids, like meeting in person and poring over dossiers, are of equally dubious value when it comes to making more accurate choices, some studies have found, with face-to-face interviews actually degrading the quality of an eventual decision. "People's overconfidence in their ability to read someone in a half-an-hour interview is quite astounding," said Michael A. Bishop, an associate professor of philosophy at Northern Illinois University who studies the social implications of these models. Complete text: http://www.nytimes.com/2006/07/18/technology/18model.html?ex=1174968000&en=f1f701d3d4b93a78&ei=5070
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