Sequential Extensions of Causal and Evidential Decision Theory
  Moving beyond the dualistic view in AI where agent and environment are
separated incurs new challenges for decision making, as calculation of expected
utility is no longer straightforward. The non-dualistic decision theory
literature is split between causal decision theory and evidential decision
theory. We extend these decision algorithms to the sequential setting where the
agent alternates between taking actions and observing their consequences. We
find that evidential decision theory has two natural extensions while causal
decision theory only has one.
