  In most contemporary approaches to decision making, a decision problem is
described by a sets of states and set of outcomes, and a rich set of acts,
which are functions from states to outcomes over which the decision maker (DM)
has preferences. Most interesting decision problems, however, do not come with
a state space and an outcome space. Indeed, in complex problems it is often far
from clear what the state and outcome spaces would be. We present an
alternative foundation for decision making, in which the primitive objects of
choice are syntactic programs. A representation theorem is proved in the spirit
of standard representation theorems, showing that if the DM's preference
relation on objects of choice satisfies appropriate axioms, then there exist a
set S of states, a set O of outcomes, a way of interpreting the objects of
choice as functions from S to O, a probability on S, and a utility function on
O, such that the DM prefers choice a to choice b if and only if the expected
utility of a is higher than that of b. Thus, the state space and outcome space
are subjective, just like the probability and utility; they are not part of the
description of the problem. In principle, a modeler can test for SEU behavior
without having access to states or outcomes. We illustrate the power of our
approach by showing that it can capture decision makers who are subject to
framing effects.
