  Computation plays a major role in decision making. Even if an agent is
willing to ascribe a probability to all states and a utility to all outcomes,
and maximize expected utility, doing so might present serious computational
problems. Moreover, computing the outcome of a given act might be difficult. In
a companion paper we develop a framework for game theory with costly
computation, where the objects of choice are Turing machines. Here we apply
that framework to decision theory. We show how well-known phenomena like
first-impression-matters biases (i.e., people tend to put more weight on
evidence they hear early on), belief polarization (two people with different
prior beliefs, hearing the same evidence, can end up with diametrically opposed
conclusions), and the status quo bias (people are much more likely to stick
with what they already have) can be easily captured in that framework. Finally,
we use the framework to define some new notions: value of computational
information (a computational variant of value of information) and and
computational value of conversation.
