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Who’s the Smartest of Them All ? Social software uncovers the true experts

Future Watch by Julia King , COMPUTERWORLD

lundi 21 février 2005, par Collecte CND R.L

Before building a walkway through a garden, college campus or office park, an experienced landscaper carefully studies where people walk naturally to discover the best routes between popular destinations.

Hewlett-Packard Co. researcher Bernardo Huberman and his team at the Systems Research Center at HP Labs are using a similar strategy to study how e-mail flows through organizations. The idea is to uncover natural "communities of interest" that can be tapped to make smarter decisions and more accurate business predictions.

Using an algorithm that measures "betweeness centrality" — a measure of the prominence of individuals in a social network — Huberman and his team classified hundreds of thousands of e-mail messages by how they traveled within certain HP divisions.

They discovered that day-to-day work was often accomplished by self-selected teams of people who don’t show up as a group on a formal organization chart.

They theorized that members of the groups actually made up de facto teams of experts whose business decisions would outperform those of the formal experts. -----

To prove the theory, Huberman and his collaborators had 15 HP managers distributed around the globe place bets on projected monthly revenue and profit figures for an HP division.

The research team developed an algorithm to account for variations in the managers’ attitudes toward risk. As an incentive, Huberman also provided the managers with a small amount of cash that would increase or decrease, depending on the accuracy of their predictions.

Accurate Predictions

In the end, the group of managers consistently predicted the financial outcomes more accurately than an expert financial software tool the division had been using to forecast the figures.

Huberman says the test could also be conducted by pitting the informal group against a formal group of decision-makers, and the results would be the same.

The reason is that the information used to predict a business outcome is aggregated from the best possible sources, even though their high level of knowledge may not be reflected in their job titles.

He also notes that only nominal incentives are needed to persuade undeclared experts to do their best. "Just putting up a little bit of money — less than $100 — makes people behave differently," Huberman says. Moreover, money isn’t necessarily a requirement.

"People in companies are concerned about their status. If they predict well, call them ’dukes’ or ’barons.’ There are ways to enhance people’s status other than giving them financial compensation," he says.

Brian Whitworth, a researcher and assistant professor of information systems at the New Jersey Institute of Technology in Newark, has written several academic papers on social computing. He says Huberman’s ideas and work leverage the social environment of the Internet.

People have traditionally viewed the Internet as a physical system only, when it’s really a physical and social system because the physical network connects people, Whitworth says.

Huberman is mediating the interactions between people linked over the Internet, Whitworth says. He believes this is destined to be the decade of social computing, where all software is at least "group aware," as it is under Huberman’s model, if not groupware.

Indeed, Huberman says he has a notion of ultimately "building an enterprise knowledge navigator" that would allow organizations to harvest all of the knowledge in people’s heads, "and not just what’s in documents that are stored on a server somewhere."

Communities of interest and expertise could be established not just by studying e-mail flows, but also by studying the kinds of documents people access and the Web sites they visit.

To that end, Huberman’s team has developed a peer-to-peer system that automatically creates profiles of users based on those activities and stores them on their PCs. This way, users can reach so-called undeclared experts.

Undeclared Experts

For example, if someone in an organization wants to know of a good restaurant in Beijing, the system will automatically send that user’s query to only those employees whose profiles fit the request. Likely candidates could include people whose travel vouchers show trips to China or whose human resources records show Chinese language skills.

"This way, knowledge gets declared automatically," Huberman explains. "Some people call it social software. What we’re trying to do is harness the power of the implicit.

The idea that guides our work is to go and uncover all that implicit knowledge in order to gain an understanding and then to use it in interesting ways."

For starters, he says, corporations could use the technique to understand how work really gets done as opposed to how a company is formally organized.

Information from various communities of interest could also be used to supplement known experts’ knowledge.

For example, medical researchers might be able to predict disease outbreaks sooner by studying the purchase patterns of certain medicines at pharmacies.

Huberman acknowledges that there are issues of privacy that must be resolved in order for his techniques to be applied.

However, within a single organization, all e-mail generally belongs to the organization, which is why his techniques will most likely be used within companies, at least initially.

Huberman says HP has applied for patents on all of the algorithms developed as part of his social software research. How they will surface in the marketplace remains to be seen, however.

Besides helping HP manage its internal business, they could also end up as services for sale commercially in the next two years or so.

The one big remaining question, says Huberman, is, "Would you pay to use this ?"

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