21 , 2002
traces root of computers' logic
Like nervous students who know the right answer but aren't sure
why, computers sometimes know more than they "think."
For example, a computer using a "neural network" can often
reliably pick high performing stocks. But, like the student asked
to explain his answer, the computer often can't articulate how it
came up with the answer. It just "knows."
"But if you want people to make a decision based on the output,
they need to know 'why,'" said management information science
professor Monica Lam.
In search of "why," Lam developed an innovative way to
coax the answer from the computer.
"Now we can explain to customers why we are making a recommendation,"
she said. And, she added, that explanation often leads to even more
While neural networks aren't new-the first mathematical models were
developed in the 1950s-they have become more sophisticated and are
being used in a wider range of applications. The networks are modeled
after the processes that allow mammals (like that tongue-tied student)
to think and learn.
Like people, neural networks learn through training and can apply
learning to new situations. The networks are first given data with
a known outcome.
In the stock-picking example, company financial data would be input
along with the company's stock performance. The network then processes
the data to identify the "rules" that connect the financial
data to stock performance. In this example, the network is taught
to recognize high, average and low performing stocks. Once it has
established the rules, the neural network can analyze any stock
and predict stock performance.
Stock selection is a common use of neural networks. Yet investors
are often reluctant to invest based on the abstract calculations
in a black box, especially since neural networks have not been able
to explain the rules.
"The traditional neural networks couldn't do that for you,"
Lam said. Now they can. Lam created an algorithm that queries the
neural networks and reveals the rules the system developed to evaluate
the data and make predictions.
"It will figure out what relationships are established during
the learning process," she said. "Then I can judge whether
I want to use the results."
Even better, because her algorithm looks for the most significant
relationships, it filters out extraneous or insignificant information.
"The rules then have a higher prediction ability than the neural
network itself," Lam said.
While Lam looked specifically at stock picking, neural networks
and the algorithm could be applied to a variety of problems in business
and other fields-including medicine.
Her work was published in the IEEE Transactions on Knowledge and
Data Engineering and the proceedings of the first International
Conference on Electronic Business last year. She holds a copyright
on the computer code that implements her algorithm, though the algorithm
itself is in the public domain.