  State variables are easily the most subtle dimension of sequential decision
problems. This is especially true in the context of active learning problems
(bandit problems") where decisions affect what we observe and learn. We
describe our canonical framework that models {\it any} sequential decision
problem, and present our definition of state variables that allows us to claim:
Any properly modeled sequential decision problem is Markovian. We then present
a novel two-agent perspective of partially observable Markov decision problems
(POMDPs) that allows us to then claim: Any model of a real decision problem is
(possibly) non-Markovian. We illustrate these perspectives using the context of
observing and treating flu in a population, and provide examples of all four
classes of policies in this setting. We close with an indication of how to
extend this thinking to multiagent problems.
