Date of Original Version



Conference Proceeding

Rights Management

Copyright 2013, Association for the Advancement of Artificial Intelligence (

Abstract or Description

Social choice theory provides insights into a variety of collective decision making settings, but nowadays some of its tenets are challenged by internet environments, which call for dynamic decision making under constantly changing preferences. In this paper we model the problem via Markov decision processes (MDP), where the states of the MDP coincide with preference profiles and a (deterministic, stationary) policy corresponds to a social choice function. We can therefore employ the axioms studied in the social choice literature as guidelines in the design of socially desirable policies. We present tractable algorithms that compute optimal policies under different prominent social choice constraints. Our machinery relies on techniques for exploiting symmetries and isomorphisms between MDPs.



Published In

Proceedings of the AAAI Conference on Artificial Intelligence, 2013, 767-773.