  Decision analysis deals with modeling and enhancing decision processes. A
principal challenge in improving behavior is in obtaining a transparent
description of existing behavior in the first place. In this paper, we develop
an expressive, unifying perspective on inverse decision modeling: a framework
for learning parameterized representations of sequential decision behavior.
First, we formalize the forward problem (as a normative standard), subsuming
common classes of control behavior. Second, we use this to formalize the
inverse problem (as a descriptive model), generalizing existing work on
imitation/reward learning -- while opening up a much broader class of research
problems in behavior representation. Finally, we instantiate this approach with
an example (inverse bounded rational control), illustrating how this structure
enables learning (interpretable) representations of (bounded) rationality --
while naturally capturing intuitive notions of suboptimal actions, biased
beliefs, and imperfect knowledge of environments.
