[OPE-L] All power to the computer?

[Jurriaan Bendien <adsl675281@xxxxxxxxxx>]

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