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Social Choice with Changing Preferences: Representation Theorems and Long-Run Policies
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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.
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Cited by 1 Pith paper
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Can an AI Agent Safely Run a Government? Existence of Probably Approximately Aligned Policies
Given a sufficiently accurate model of social dynamics, there provably exist policies that are near-optimal for a chosen social welfare function with high probability, plus a safety filter for arbitrary black-box policies.
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