  We study group decision making with changing preferences as a Markov Decision
Process. We are motivated by the increasing prevalence of automated
decision-making systems when making choices for groups of people over time. Our
main contribution is to show how classic representation theorems from social
choice theory can be adapted to characterize optimal policies in this dynamic
setting. We provide an axiomatic characterization of MDP reward functions that
agree with the Utilitarianism social welfare functionals of social choice
theory. We also provide discussion of cases when the implementation of social
choice-theoretic axioms may fail to lead to long-run optimal outcomes.
