An Interview with Thomas J. Sargent

Sargent: Yes. And an agent’s response to fear of model misspecification
contributes behavioral responses that have interesting quantitative implications.
For example, fear of model misspecification contributes components of indirect
utility functions that in some types of data can look like heightened risk aversion,
but that are actually responses to very different types of hypothetical mental
experiments than are Pratt measures of risk aversion. For this reason, fear
of model misspecification is a tool for understanding a variety of asset price
spreads. Looked at from another viewpoint, models of robust decision making
contribute a disciplined theory of what appears to be an endogenous preference
shock.

Another reason is that decision making in the face of fear of model misspec-
ification can be a useful normative tool for solving Ramsey problems. That is
why people at central banks are interested in the topic. They distrust their
models.

Evans and Honkapohja: What are some of the connections to learning
theory?

Sargent:    There are extensive mathematical connections through the

theory of large deviations. Hansen and I exploit these. Some misspecifications
are easy to learn about, others are difficult to learn about. By ‘difficult’ I mean
‘learn at a slow rate’. Large deviation theory tells us which misspecifications
can be learned about quickly and which can’t. Hansen and I restricted the
amount of misspecification that our agent wants to guard against by requiring
that it be a misspecification that is hard to distinguish from his approximating
model. This is how we use learning theory to make precise what we mean by the
phrase ‘the decision maker thinks his model is a good approximation’. There
is a race between a discount factor and a learning rate. With discounting, it
makes sense to try to be robust against plausible alternatives that are difficult
to learn about.

Evans and Honkapohja: Can this model of decision making be recast
in Bayesian terms?

Sargent:   It depends on your perspective. We have shown that ex post,

it can, in the sense that you can come up with a prior, a distorted model, that
rationalizes the decision maker’s choices. But ex ante you can’t — the set of
misspecifications that the agent fears is too big and he will not or cannot tell
you a prior over that set.

By the way, Lars and I have constructed equilibria with heterogeneous agents
in which the ex post Bayesian analysis implies that agents with different inter-
ests will have different ‘twisted models’. From the point of view of a rational
expectations econometrician, these agents look as if they have different beliefs.
This is a disciplined way of modelling belief heterogeneity.

Evans and Honkapohja:    Is this a type of behavioral economics or

bounded rationality?

Sargent: Any decision theory is a type of behavioral economics. It is not
a type of bounded rationality. The decision maker is actually smarter than a

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