Toward Idealized Decision Theory
  This paper motivates the study of decision theory as necessary for aligning
smarter-than-human artificial systems with human interests. We discuss the
shortcomings of two standard formulations of decision theory, and demonstrate
that they cannot be used to describe an idealized decision procedure suitable
for approximation by artificial systems. We then explore the notions of policy
selection and logical counterfactuals, two recent insights into decision theory
that point the way toward promising paths for future research.
