power, willpower, and self-interest. These assumptions can be considered “proce-
durally rational” (Herbert Simon’s term) because they posit functional heuristics
for solving problems that are often so complex that they cannot be solved exactly
by even modern computer algorithms.
Evaluating Behavioral Economics
Stigler (1965) says economic theories should be judged by three criteria: congru-
ence with reality, generality, and tractability. Theories in behavioral economics
should be judged this way too. We share the modernist view that the ultimate test
of a theory is the accuracy with which it identifies the actual causes of behavior;
making accurate predictions is a big clue that a theory has pinned down the right
causes, but more
realistic
assumptions are surely helpful too.
3
Theories in behavioral economics also strive for
generality—
e.g., by adding
only one or two parameters to standard models. Particular parameter values then
often reduce the behavioral model to the standard one, and the behavioral model
can be pitted against the standard model by estimating parameter values. Once
parameter values are pinned down, the behavioral model can be applied just as
widely as the standard one.
Adding behavioral assumptions often
does
make the models less tractable. How-
ever, many of the papers represented in this volume show that it can be done. More-
over, despite the fact that they often add parameters to standard models, behavioral
models, in some cases, can be even more
precise
than traditional ones that assume
more rationality, when there is dynamics and strategic interaction. Thus, Lucas
(1986) noted that rational expectations allow for multiple inflationary and asset
price paths in dynamic models, while adaptive expectations pin down one path. The
same is true in game theory: Models based on cognitive algorithms (Camerer, Ho,
and Chong 2003) often generate precise predictions in those games where the mu-
tual consistency requirement of Nash permits multiple equilibria.
The realism, generality, and tractability of behavioral economics can be illus-
trated with the example of loss-aversion. Loss-aversion is the disparity between
the strong aversion to losses relative to a reference point and the weaker desire for
gains of equivalent magnitude. Loss aversion is more
realistic
than the standard
continuous, concave, utility function over wealth, as demonstrated by hundreds of
experiments. Loss aversion has proved useful in identifying where predictions of
standard theories will go wrong: Loss-aversion can help account for the equity
premium puzzle in finance and asymmetry in price elasticities. (We provide more
examples further on.) Loss aversion can also be parameterized in a general way,
as the ratio of the marginal disutility of a loss relative to the marginal utility of a
Do'stlaringiz bilan baham: