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A: My wife Esther died six years ago, of cancer, after being ill for about a year
and a half. She was an extraordinary person. After elementary school she entered
the Bezalel School of Art—she had a great talent for art. At Bezalel she learned
silversmithing, and she also drew well. She was wonderful with her hands and also
with people. When about fifty, she went to work for the Frankforter Center, an old-
age day activities center; she ran the crafts workshop, where the elderly worked
with their hands: appliqu´e, knitting, embroidery, carpets, and so on. This enabled
Esther to combine her two favorite activities: her artistic ability, and dealing with
people and helping them, each one with his individual troubles.
When she went to school, Bezalel was a rather Bohemian place. It probably
still is, but at that time it was less fashionable to be Bohemian, more special. Her
parents were very much opposed to this. In an orthodox Jewish family, a young
girl going to this place was really unheard of. But Esther had her own will. She
was a mild-mannered person, but when she wanted something, you bet your life
she got it, both with her parents and with me. She definitely did want to go to that
school, and she went.
H: There is a nice story about your decision to come to Israel in ’56.
A: In ’56 I had just finished two years of a postdoc at Princeton, and was
wondering how to continue my life. As mentioned, I had made up my mind to
come to Israel eventually. One of the places where I applied was the Hebrew
University in Jerusalem. I also applied to other places, because one doesn’t put
F
IGURE
5. Bob Aumann with his immediate family, Jerusalem, October 10, 2005.
INTERVIEW WITH ROBERT AUMANN
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all one’s eggs in one basket, and got several offers. One was from Bell Telephone
Laboratories in Murray Hill; one from Jerusalem; and there were others. Thinking
things over very hard and agonizing over this decision, I finally decided to accept
the position at Bell Labs, and told them that. We started looking around for a place
to live on that very same day.
When we came home in the evening, I knew I had made the wrong decision. I
had agonized over it for three weeks or more, but once it had been made, it was
clear to me that it was wrong. Before it had been made, nothing was clear. Now, I
realized that I wanted to go to Israel immediately, that there is no point in putting
it off, no point in trying to earn some money to finance the trip to Israel; we’ll
just get stuck in the United States. If we are going to go at all we should go right
away. I called up the Bell Labs people and said, “I changed my mind. I said I’ll
come, so I’ll come, but you should know that I’m leaving in one year.” They said,
“Aumann, you’re off the hook. You don’t have to come if you don’t want to.” I
said, “Okay, but now it’s June. I am not leaving until October, when the academic
year in Israel starts. Could I work until October at Bell Labs?” They said, “Sure,
we’ll be glad to have you.” That was very nice of them.
That was a really good four months there. John McCarthy, a computer scientist,
was one of the people I got to know during that period. John Addison, a math-
ematician, logician, Turing machine person, was also there. One anecdote about
Addison that summer is that he had written a paper about Turing machines, and
wanted to issue it as a Bell Labs discussion paper. The patent office at Bell Labs
gave him trouble. They wanted to know whether this so-called improvement on
Turing machines could be patented. It took him a while to convince them that a
Turing machine is not really a machine.
I am telling this long story to illustrate the difficulties with practical decision
making. The process of practical decision making is much more complex than our
models. In practical decision making, you don’t know the right decision until after
you’ve made it.
H: This, at least to my mind, is a good example of some of your views on
experiments and empirics. Do you want to expand on that?
A: Yes. I have grave doubts about what’s called “behavioral economics,” but
isn’t really behavioral. The term implies that that is how people actually behave, in
contradistinction to what the theory says. But that’s not what behavioral economics
is concerned with. On the contrary, most of behavioral economics deals with
artificial laboratory setups, at best. At worst, it deals with polls, questionnaires.
One type of so-called behavioral economics is when people are asked, what would
you do if you were faced with such and such a situation. Then they have to imagine
that they are in this situation and they have to give an answer.
H: Your example of Bell Labs versus the Hebrew University shows that you
really can give the wrong answer when you are asked such a question.
A: Polls and questionnaires are worse than that; they are at a double remove
from reality. In the Bell Labs case, I actually was faced with the problem of which
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job to take. Even then I took a decision that was not the final one, in spite of
the setup being real. In “behavioral economics,” people ask, “What would you do
if . . . ”; it is not even a real setup.
Behavioral economists also do experiments with “real” decisions rewarded by
monetary payoffs. But even then the monetary payoff is usually very small. More
importantly, the decisions that people face are not ones that they usually take, with
which they are familiar. The whole setup is artificial. It is not a decision that really
affects them and to which they are used.
Let me give one example of this—the famous “probability matching” experi-
ment. A light periodically flashes, three quarters of the time green, one quarter
red, at random. The subject has to guess the color beforehand, and gets rewarded
if he guesses correctly. This experiment has been repeated hundreds of times; by
far the largest number of subjects guess green three quarters of the time and red
one quarter of the time.
That is not optimal; you should always guess green. If you get a dollar each time
you guess correctly, and you probability-match—three quarters, one quarter—then
your expected payoff is five eighths of a dollar. If you guess green all the time
you get an average of three quarters of a dollar. Nevertheless, people probability-
match. The point is that the setting is artificial: people don’t usually sit in front
of flashing lights. They don’t know how to react, so they do what they think is
expected of them, which becomes probability-matching.
In real situations, people don’t act that way. An example is driving to work in
the morning. Many people have a choice of routes, and each route has a certain
probability of taking less time. It is random, because one can’t know where there
will be an accident, a traffic jam. Let’s say that there are two routes; one is quicker
three quarters of the time and the other, one quarter of the time. Most people will
settle down and take the same route every day, although some days it will be the
longer one; and that is the correct solution.
In short, I have serious doubts about behavioral economics as it is practiced.
Now, true behavioral economics does in fact exist; it is called empirical economics.
This really is behavioral economics. In empirical economics, you go and see how
people behave in real life, in situations to which they are used. Things they do
every day.
There is a wonderful publication called the NBER Reporter. NBER is the
National Bureau of Economic Research, an American organization. They put out
a monthly newsletter of four to six pages, in which they give brief summaries
of research memoranda published during that month. It is all empirical. There
is nothing theoretical there. Sometimes they give theoretical background, but all
these works are empirical works that say how people actually behave. It is amazing
to see, in these reports, how well the actual behavior of people fits economic theory.
H: Can you give an example of that?
A: One example I remember is where there was a very strong effect of raising
the tax on alcohol by a very small amount, let’s say ten percent. Now we are talking
about raising the price of a glass of beer by two to two and a half percent. It had a
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