Priceless: The Myth of Fair Value (and How to Take Advantage of It) (30 page)

Behavioral decision theorists are showing us something we could not learn any other way. There is no replay button to life. Never is there a chance to hit rewind, to see how we might have decided differently, or what price we might have agreed to, had the situation been just a little different. That takes an experiment.

The results of those experiments often challenge the notion of free will. By their nature, executives are strong-willed people. When you tell them that something like who names a number first can exert an unconscious influence on them, an influence affecting their company’s bottom line, they can get indignant (as in “I’m too strong-willed to be hypnotized!”). They are sure they would have learned from experience what works and doesn’t work in negotiation.

Colin Camerer calls this the
Groundhog Day
argument. In the 1993 movie, Bill Murray plays a man who wakes up each morning to find it’s February 2 (again) and he has the day to live over. Murray is able to conduct a succession of “decision experiments”: romancing women, driving drunk, even committing suicide with impunity. After many disastrous
attempts, he finally he gets his life in order. The difference between that and real life, says Camerer, is that people never learn. Life rarely grants them the opportunity to match a complex cause to a complex effect.

Ilana Ritov of Ben-Gurion University in Be’er Sheva, Israel, did an experiment in which 148 managerial and engineering students engaged in a simulation of bargaining. Half the participants were given badges identifying themselves as buyers, the other half as sellers. The goal of the game was to make as much profit as possible by negotiating deals for the sale of an imaginary commodity. Each deal had to specify a price, delivery terms, and a discount level. The players consulted a profit schedule to determine how much they would earn under any particular agreement. In an attempt at realism, the two sides were not simply splitting an $8,000 pie. Depending on how the deal was structured, buyer and seller could gain as much as $5,200 apiece. It generally took a bit of back-and-forth to arrive at that win-win solution.

There are no rules to real-world bargaining, so Ritov didn’t impose any. Anyone could partner with anyone they liked, as long the pair included a buyer and a seller. Either could make the first offer. They could offer any justification they liked for their offers, and use any negotiating strategies. Anyone who felt they were spinning their wheels could walk away and find another bargaining partner. Once a deal was made, both players could approach other partners and keep making deals, until the assigned time ran out. Players could try to maximize profit on every deal or “make it up in volume.”

Ritov found that her sellers were usually the ones to approach a buyer. In a way, this was surprising because the game was so abstract. There was no physical merchandise to lug around and hand over; no fundamental difference between the two groups aside from the name tags. They might just as well have been called “skins” and “shirts.”

Words can frame behavior, though, and the players fell readily into the familiar roles of buyer and seller. Normally a seller sets an asking price, and the buyer responds with a counteroffer. For the most part, that’s what happened.

We often miss the forest for the trees. Ritov’s experiment was able to reveal something that was not apparent to the bargainers themselves: the
power of getting your number in first. On average, those who made the first offer made more money, and the higher the initial offer, the more money was made.

This is readily visible in a chart from Ritov’s 1996 paper in
Organizational Behavior and Human Decision Processes
. This chart (above) plots initial offers on the horizontal axis versus final agreements on the vertical. In both cases, the offers are expressed as the profit to the initiator. Each point is a finalized deal. The important thing is not the individual “trees” but the shape of the “forest.” The swarm of points roughly follows an upward-sloping line. In other words,
the more you ask for, the more you get
.

The maximum profit, to any one player in any one deal, was $8,000. The minimum was zero. Quite a few initiators started by asking for the
kitchen sink—their maximum $8,000. The dense cluster of points sitting on the chart’s right border represents this. That “greedy” opening left little or no profit for the partner.

Yet there was no evident downside to asking for the kitchen sink. While nobody ended up with an $8,000 profit, the people who made that their first offer did as well as or better than those who asked for less.

Another finding was more surprising yet. A chart of profit to non-initiators looked much the same. The better the initial offer was for the other guy, the more the other guy ended up with. This underscored how the initiator determined the fates of
both
parties.

In real estate, the seller sets an asking price. There’s not much a buyer can do about that. In many other situations, the first-mover advantage is up for grabs. This is often true of salary negotiations.

Most employees negotiating a salary rightly feel at a disadvantage. A big company interviews thousands of applicants a year. It makes hundreds of salary offers and sees how many of them are accepted. This gives the employer a good feel for current market conditions. The average job seeker interviews sporadically and is left guessing at his or her current market value. Ask for a too-low number and you cheat yourself; ask for a too-high amount and you look foolish (and may miss out on a job you’d like). It’s no wonder that many job seekers fall back on this strategy:

(a) Let the employer make the first offer

(b) Whatever it is, say it’s not enough

(c) Demand 20 percent more

(d) Settle for 10 percent more [or fill in your own percentages]

Should you follow this to the letter, you’d end up settling for the employer’s initial offer plus 10 percent,
no matter what the initial offer was
. That would mean you’d be even more a slave to the anchor than typical experimental subjects are.

The person who names a number first creates the strongest anchor. No one should willingly cede that opportunity. Fortunately, it’s easier than ever for job seekers to research their current worth. Sites such as Salary.com ask a few questions (job title, education, experience, zip
code) and generate a bell curve of likely salaries. You can learn, for instance, that 90 percent of comparable workers make less than $73,415. Answer the site’s questions honestly, and that 90th percentile figure is a decent anchor/first offer. You won’t likely get that much, but neither will they laugh you out of the office.

One of the worst things that can happen in a negotiation is for the other side to open with a wholly unacceptable number. In such situations, Max Bazerman and Margaret Neale believe it’s necessary to “reanchor”—to demand a fresh start. In their
Negotiating Rationally
(1992), a popular text in MBA courses, they warn, “Responding to an initial offer with suggested adjustments gives the anchor some measure of credibility . . . Threatening to walk away from the table is better than agreeing to an unacceptable starting point.”

Possibly the commonest objection to the idea of price anchoring is that it must be for dummies.
I’m too smart to fall for it, and so are the people I deal with.

In 2008 Jörg Oechssler, Andreas Roider, and Patrick W. Schmitz of the German Institute for the Study of Labor tested this notion. They had a group of 1,250 volunteers answer the three-question Cognitive Reflection Test (CRT), a sort of mini-IQ test. The questions are classic brain-teasers. You’re welcome to try them. Answer all three before reading on.

(1) A bat and a ball together cost 110 cents. The bat costs 100 cents more than the ball. How much does the ball cost? _____

(2) If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? _____

(3) In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half the lake? _____

The CRT doesn’t purport to measure intelligence in any meaningful sense. It’s better described as a test of willingness to
think things through
and
check your answer
. All three items are “gotcha” questions to which the first answer that occurs to just about everyone is wrong.

Oechssler’s team split their subjects into two groups. Anyone who got two or three questions right was in the “reflective” group, and anyone who got zero or one right was in the “impulsive” group. (Just so you know where you’d fit in, the correct answers are (1) 5 cents; (2) 5 minutes; (3) 47 days.)

Both groups also answered questions involving anchoring. There was no difference between the impulsive and reflective thinkers in susceptibility to anchoring. In fact, they found slightly more of an anchoring effect with the reflective people, though it wasn’t statistically significant.

For bright, reflective people, a number or hypothetical question triggers a rich network of associations. The longer and harder someone thinks about an answer, the more extended the exposure to these primed thoughts. This appears to counteract whatever accuracy advantages might have come from additional thought.

“When I build something for somebody,” Donald Trump once confided, “I always add $50 million or $60 million onto the price. My guys come in, they say it’s going to cost $75 million. I say it’s going to cost $125 million, and I build it for $100 million. Basically I did a lousy job. But they think I did a great job.”

Trump is hardly the only deal maker to appreciate the power of
two
numbers. Consider a novel ultimatum game devised by Max Bazerman, Sally Blount White, and George Loewenstein. One group of responders was simply asked to indicate the minimum offer they would accept out of $10. The average answer was $4, and that’s typical.

A second group of responders was presented with two offers rather than the usual one (say, $3 and $2). These responders could accept either offer—or veto both.

This changed behavior greatly. Responders given a choice were more likely to accept the higher offer ($3) than to veto. Remember, the majority of people in the first group indicated that they would veto a $3 offer (or any offer under $4). The implication is that people who happily accepted $3,
when a $2 offer was also on the table
, would have vetoed that same $3, had it been the
only
offer.

Bazerman’s team tested various pairs of offers. They found that the minimum offer accepted, when it was the higher of two, averaged $2.33. In this context, that’s a big effect. Responders were willing to accept about 40 percent less just because it was presented as the better of two offers.

Why? It’s apparently a matter of contrast and misdirection. In the
standard ultimatum game, a responder offered $3 can compare it only to the $7 the proposer wants to keep for himself. The $7 makes the $3 look small and triggers feelings of unfairness, even anger. When there are two offers on the table, attention is diverted to the fact that one is better than the other. There is less mental machinery available to contemplate how the offers compare with what the proposer would be getting. At the moment of choice, deciders settle for the executive summary: which will it be, $3 or $2 or nothing?

“Automatic processes—whether cognitive or affective—are the default mode of brain operation,” Colin Camerer, George Loewenstein, and Drazen Prelec wrote recently. “They whir along all the time, even when we dream, constituting most of the electro-chemical activity in the brain . . . Attention, for example, is largely controlled by automatic processes, and attention in turn determines what information we absorb.” You can be doing your taxes when a baseball crashes through the window. You don’t “decide” to look up and see what made the noise. It’s automatic.

Neuroscience is starting to sketch in the details. There’s a nubbin of gray meat at the base of the brain called the amygdala. One of its roles is to act as watchdog, detecting possible threats even when the focus of attention is elsewhere. In lab studies, the amygdala “sees” objects in peripheral vision that are invisible to the more deliberative parts of the brain.