The Economics of Artificial Intelligence
Page 102
the most habitual or who do not shop cleverly, but will help savvy consum-
ers who can hijack the personalization algorithms to look like low WTP
consumers and save money. See Gabaix and Laibson (2006) for a carefully
worked- out model about hidden (“shrouded”) product attributes.
24.5 Conclusion
This chapter discussed three ways in which AI, particularly machine learn-
ing, connect with behavioral economics. One way is that ML can be used
to mine the large set of features that behavioral economists think could
improve prediction of choice. I gave examples of simple kinds of ML (with
much smaller data sets than often used) in predicting bargaining outcomes,
risky choice, and behavior in games.
The second way is by construing typical patterns in human judgment as
the output of implicit machine- learning methods that are inappropriately
applied. For example, if there is no correction for overfi tting, then the gap
21. I put the word “hurts” in quotes here as a way to conjecture, through punctuation, that in many industries the AI- driven capacity to personalize pricing will harm consumer welfare overall.
22. A feature of their fairness framework is that people do not mind price increases or sur-charges if they are even partially justifi ed by cost diff erentials. I have a recollection of Kahneman and Thaler joking that a restaurant could successfully charge higher prices on Saturday nights if there is some enhancement, such as a mariachi band—even if most people don’t like mariachi.
Artifi cial Intelligence and Behavioral Economics 605
between training set accuracy and test- set accuracy will grow and grow if
more features are used. This could be a model of human overconfi dence.
The third way is that AI methods can help people “assemble” preference
predictions about unfamiliar products (e.g., through recommender systems)
and can also harm consumers by extracting more surplus than ever before
(through better types of price discrimination).
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Comment Daniel Kahneman
Below is a slightly edited version of Professor Kahneman’s spoken remarks.
During the talks yesterday, I couldn’t understand most of what was going
on, and yet I had the feeling that I was learning a lot. I will have some
remarks about Colin (Camerer) and then some remarks about the few things
that I noticed yesterday that I could understand.
Colin had a lovely idea that I agree with. It is that if you have a mass of
data and you use deep learning, you will fi nd out much more than your
theory is designed to explain. And I would hope that machine learning can
be a source of hypotheses. That is, that some of these variables that you
identify are genuinely interesting.
At least in my fi eld, the bar for successful publishable science is very low.
We consider theories confi rmed even when they explain very little of the
variance so long as they yield statistically signifi cant predictions. We treat
the residual variance as noise, so a deeper look into the residual variance,
which machine learning is good at, is an advantage. So as an outsider, actu-
Daniel Kahneman is professor emeritus of psychology and public aff airs at the Woodrow Wilson School and the Eugene Higgins Professor of Psychology emeritus, Princeton University, and a fellow of the Center for Rationality at the Hebrew University in Jerusalem.
For acknowledgments, sources of research support, and disclosure of the author’s material fi nancial relationships, if any, please see http:// www .nber .org/ chapters/ c14016.ack.
Comment 609
ally, I was surprised not to hear more about that aspect of the superiority
of artifi cial intelligence (AI) compared to what people can do. Perhaps, as
a psychologist, this is what interests me most. I’m not sure that new signals
will always be interesting, but I suppose that some may lead to new theory
and that would be useful.
I do not fully agree with Colin’s second idea: that it is useful to view human
intelligence as a weak version of artifi cial intelligence. There certainly are
similarities, and certainly you can model some of human overconfi dence in
that way. But I do think that the processes that occur in human judgment are
quite diff erent than the processes that produce overconfi dence in software.
Now I turn to some general remarks of my own based on what I learned
yesterday. One of the recurrent issues, both in talks and in conversations,
was whether AI could eventually do whatever people can do. Will there be
anything that is reserved for human beings?
Frankly, I don’t see any reason to set limits on what AI can do. We have in
our heads a wonderful computer. It is made of meat, but it’s a computer. It’s