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Default Tech Firms Turn to Experimental Economists

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For the last 15 years, economist Kay-Yut Chen has been quietly toiling away in Hewlett-Packard's labs, running experiments mostly on nearby Stanford students to help determine such things as the best incentive schemes to use with the hardware maker's suppliers and distributors, and how to set the right pricing and policies for H.P.'s online store.
Though his work has generated millions of dollars for H.P., Chen's experimental-economics lab remained the only one of its kind within a major corporation for many years. But recently that's started to change, as high-tech firms in particular have come to realize the value of testing their programs internally in labs, and experimental economics as a field has gained increasing respect.
"The picture has completely changed," Chen says, pointing to the 2002 Nobel Prize given to pioneering experimental economist Vernon Smith as an event that helped legitimize the field. Before, the field had been viewed as something of a crutch for economic theories that lacked good formal models.
Within the last three years, both Google and Yahoo have built in-house economics facilities of their own to work on such tasks as optimizing their keyword auctions, and dozens of other companies have turned to outside consultants for help on specific projects. EBay used experimental economists to develop a new seller-feedback system that wound up boosting the total value of goods sold on the site by 25 percent, according to the researcher who worked on the project.
"As things get more complex, it gets more difficult to manage your business using only buzzwords," says Özalp Özer, a Stanford professor of management science and engineering who recently ran experiments for Hitachi Global Storage Technologies to determine the optimal time to introduce a new disk drive, given the trade-offs among engineering, marketing, and manufacturing. Instead, says Özer, corporations are looking for hard data to support their business moves.
Experimental economists like Özer and Chen begin with a number of theories about which programs will maximize corporate profits or other goals and then test them—typically on paid subjects. Some ideas fail when tested on real people, who sometimes react unpredictably and aren't always the rational maximizers of utility assumed by classical economics. (For a demonstration of this, see our interactive quiz.)
So when testing policies in the lab, Chen and his fellow researchers look for unintended consequences, which alert them to loopholes that need to be closed or ideas that need to be scrapped altogether.
"The experiments are a cheap way of removing your mistakes," says CalTech economist Charlie Plott, who helped develop the field and was a teacher of Chen's. "All the big expensive mistakes are confined to the laboratory."
Plott recently used his expertise in auctions and markets to consult for Ford Motor, finding a clever way for Ford to profitably comply with federal fleetwide gas-mileage regulations despite the automaker's decentralized structure. To address the problem, he designed and tested a market for fuel-efficiency credits that would be used just within the company.
Governments have used the experimental approach for a long time to guide the development of regulatory policies and allocate resources. But few corporations have historically utilized it because of the cost and uncertain payoff. Hiring the staff and developing the software to run an in-house lab can easily cost hundreds of thousands of dollars or more a year, according to Sam Dinkin, a former I.B.M. research economist who now works for Power Auctions.
But the cost-saving work of researchers like Chen has caused more companies to take notice.
One of Chen's recent projects involved finding a way for H.P. to more accurately predict demand from its nine distributors, who collectively sell as much as $3 billion worth of H.P.'s products. The problem? Its distributors' forecasts for demand were frequently off by as much as 100 percent, wreaking havoc on H.P.'s production planning.
Chen's solution to the planning problem, which H.P. intends to test soon with one distributor, was to develop an incentive system that rewarded distributors for sticking to their forecasts by turning those forecasts into purchase commitments. In the lab, the overlap between distributors' forecasts and their actual orders using this system increased to as high as 80 percent. "That's pretty astonishing given that the underlying demand is completely random," Chen says.
"You model the environment and try different strategies, and the experiments tell you which way to go," says Chen of his basic approach. "If you can understand how people make decisions, you'll be able to zero in on useful ideas a lot faster."


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