Digital Dealmakers Meet in the Middle

The New York Times

September 11, 2003

Digital Dealmakers Meet in the Middle By KEN BELSON

PICTURE two people haggling over a debt - say, $1,000 - that is due in a
month. The lender wants the money, but the debtor wants to cut a deal.

"I'll get you the money in two weeks," the debtor says, "but only if I can
pay $800."

The lender doesn't give in so easily. "Pay me now," he counters, "and I
want $950." On it goes until an agreement is reached or both sides give up.

The two sides could be facing off at an office conference table or in a
movie about small-time criminals. But in fact just such a scenario plays
out regularly online, in the form of negotiations over claims between
doctors and insurers, who have enlisted computers to represent them.

Software developed by a company called SplitTheDifference makes proposals
on behalf of insurers, relying on algorithms to gauge what discount a
doctor might accept in exchange for a faster payment.

The software, which has been used in 20,000 negotiations over the past 18
months, is just one of the ways in which computers are playing a larger
role in a growing variety of everyday transactions. The innovations are
rooted in advances in mathematics that enable computers to mimic human
behavior and in some cases replace them altogether.

Most uses of automated negotiations have so far been simple: settling
insurance claims, selling excess telecommunications capacity and so on. But
the scope for such negotiations is vast. Negotiation software may one day
be used to help settle credit card bills, car loans and other financial
exchanges, for example.

The electronics company Fujitsu is in the early stages of commercializing
software that could enable consumers to negotiate deals for air travel,
cellphone service or other products.

Human beings, of course, continue to set the parameters. But computers,
which have the capacity to consider millions of offers and counteroffers in
seconds, hold out the possibility of crossing the subtlety of the human
intellect with the raw power of the processor.

"The big difference between humans and machines is that humans are really
smart but slow, while machines are stupid but fast," said Steve White, a
senior manager in the I.B.M. autonomic computing group. "Machines don't
make mistakes, either."

There is an emotional component to negotiation that goes beyond numbers,
and computers cannot fully account for it. "What happens in negotiations
is, both sides have their own interests so we never both get what we want,"
Mr. White said. "So what we are concentrating on is getting computers to
talk to each other."

Software advances have extended the automated negotiators' potential scope.
Instead of considering price alone, computers can weigh the relative merits
of variables including quantity, delivery time and technical
specifications. Computers have moved beyond merely matching bids with
offers or arbitrating between adversaries; they now can incorporate into
the negotiations factors that people may not have considered.

The trick to making automated negotiation software work is finding a niche
where the humans involved will not feel either that the computer is
fleecing them or giving too much away to the other side. To be effective,
the software must give both sides some control over the process.

In practice, that has meant finding industries or business practices that
are already digitized and have a high volume of data traffic, like
financial markets and telecommunications providers. But while I.B.M. and
other developers have had some success creating negotiation software for
such areas, most companies are still a long way from being ready for
digital dealing because the bulk of their data is on paper or in human
memory, not on hard disks. Aside from the money and time needed to digitize
such information, managers tend to be wary about ceding job functions or
decision-making authority to a machine, a hurdle that must be overcome
before negotiation software can achieve broader acceptance.

"There was a huge amount of belief in automated negotiations, but it has
turned into a long slog," said Robert E. Hall, a professor of economics at
Stanford University and the author of "Digital Dealing: How E-Markets Are
Transforming the Economy" (Norton, 2002). "We're still living in a fax world."

That said, companies are having some success selling the software,
particularly to manufacturers. Ariba, a software company that is a leader
in the field, has created a negotiating tool that gathers information about
a company's inventory, production schedules and prices, among other things,
and uses the information to field inquiries from potential buyers of its
products. Bidders who use the software can specify their tolerance for such
variables as return rates, reliability and other factors, which are
assigned numerical rankings. By blending quantitative and qualitative
information, the software enables users to determine the total cost of a
purchase before negotiations begin.

"Defining the parameters of the negotiations are as important as the
negotiations," said Steve Markle, a product manager for Ariba's Spend
Management Solution group.

But to use such a full-service approach, companies must purchase an array
of software that may exceed their needs if they are seeking only to
automate one or two functions rather than an entire supply chain.

SplitTheDifference, focusing more narrowly, found an opportunity to help
insurers and doctors haggle over payments that are too small to be handled
economically by conventional methods. Ordinarily, insurance companies have
45 days after processing a claim to send the payment to the doctor. The
doctors want to get the money sooner, while insurers have an incentive to
pay out less money.

SplitTheDifference's software adjusts itself as the negotiations proceed.
The company has found that most doctors reject the first offer and come
back with a counteroffer. The software then formulates a new offer that
takes into account shifts in the doctor's position reflected by the
counteroffer. The process continues until a deal is sealed or the doctor
has rejected three proposals, in which case the insurer pays the full claim
under the original time frame. The average negotiated settlement reached
using the software has been $2,000, and the settlements saved the insurers
an average of $400, or 20 percent. SplitTheDifference gets 20 percent of
any money the insurer saves as a result of the negotiations.

Roy Ophir, the company's president, said that insurers and doctors have
agreed to automate their negotiations because it is cheaper than the
existing arrangement, in which outside negotiators working for insurance
companies employ telephone operators to call doctors, who are often too
busy to talk. Because the fees earned by the outside negotiators are
related to the ultimate payments, most of the negotiation companies focus
only on claims for substantially more than $2,000.

The software can be used effectively only if the doctors see an advantage
in cooperating. "Financial instruments like insurance payments are where
the value proposition is high enough to make negotiation software
worthwhile," Mr. Ophir said. "The key is that both sides have to agree."

Other behavioral patterns have emerged from SplitTheDifference's business.
Just 2 percent of the insurers' offers are accepted immediately because
doctors routinely think they can get a better result without giving
anything away in return. Three-quarters of the negotiations, however, end
after the second offer, suggesting that most doctors would rather reach an
agreement quickly than haggle endlessly.

The doctors' behavior does expose a fundamental weakness in the concept
underlying algorithm-based negotiation software. Most negotiators have
little incentive to divulge initially the outcome they are willing to
accept. Participants regularly plead poverty or inflate the value of their
goods to get what they want. Such strategic posturing makes it impossible
for a computer to determine an "ideal" price and at times can scuttle the
negotiations altogether.

"The right solution depends on the right information, but it's
mathematically impossible to come up with an equation to make me truthfully
disclose my incentives," said Peter Cramton, an economics professor at the
University of Maryland who teaches game theory. "Life would be pretty
simple and maybe boring if we just ran into each other and exchanged
information honestly. But life is not that simple."

Dave Marvit and a team of researchers at Fujitsu Laboratories of America,
however, have not let that stand in their way. His team, called Project A
within Fujitsu, has worked for two years on a software program that can run
through millions of potential scenarios in seconds. Unlike "matching"
software, which aims to bring together parties with similar preferences,
Fujitsu's program tries to narrow differences between groups that initially
disagree.

"We can run millions of prices and have 1,000 or more negotiations that ask
how about this, how about that," said Mr. Marvit, whose background is in
neuroscience. "Humans look for all these clues to psych out our opponents.
Now, you can figure that out by making enough offers back and forth."

As it moves to make the software commercially available, Fujitsu is hoping
it will appeal to companies that swap foodstuffs, computer chips and other
commodities. But it could in theory be used to negotiate to get a better
rate on a hotel room or cheaper cellphone service as well. In a
hypothetical example offered by Mr. Marvit, a traveler might bargain with
an airline, offering a mixture of money, frequent-flier mileage and
flexibility over route and destinations in exchange for a lower fare on the
ticket and other benefits.

In such a case, the traveler would presumably negotiate with the computer
by entering preferences in an online form. The would-be traveler could
choose to use software to negotiate on his behalf as well, raising the
possibility of a computer-to-computer confrontation. The theory suggests
that a stalemate is unlikely even in a computer face-off, however, because
the automated negotiators' power to search for potential solutions is so
much greater than humans'.

The Fujitsu software is relatively simple to use. Negotiators select
preferences for categories like quantity, delivery time, shipping and
handling costs, unit prices and reliability. Buyers can specify one
priority, like the lowest price, or they can negotiate over several terms
at once. They might continue asking for 1 percent discounts and tell the
computer to stop when the price stops falling.

Although the software may run thousands of iterations during a negotiation,
the last round does not always yield the best price. In fact, in tests of
the software, the optimum outcome is often found about halfway through the
process. During the remaining repetitions, the price changed little but
both sides gave ground on nonprice considerations like earlier or later
delivery dates.

The writing of the program, which Mr. Marvit compares to a computerized
chess game, got tricky when colleagues from Japan joined in. Because
practices vary by country, there were time-consuming debates about the most
effective ways to run a business. As a result, it became clear that
negotiating software must be capable of accounting for differences between,
say, one business culture's tendency to tolerate defects if the price is
right and another's assumption that prompt delivery is a given.

Despite those differences, Mr. Marvit predicts that the relative ease and
low cost of running the software will prompt more participants to trade
online. In seconds, they will be able to calculate the cost of choosing
blue over red or any number of other variables. Potential business partners
could even haggle with each other to decide whether they want to cooperate.
This could relieve anxiety that one side was bound to get cheated.

"Good negotiators know you can do better if you add more business terms to
the discussions," Mr. Marvit said. "Not just price, but time and others.
The computers can handle more parameters so more value can be squeezed out
of the transaction. It's the opposite of a zero-sum game."

Copyright 2003 The New York Times Company


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