Policy Aggregation

TL;DR

This paper addresses the challenge of combining multiple individuals' reward functions into a collective policy in AI, using social choice theory and geometric interpretations to adapt voting methods for policy aggregation.

Contribution

It introduces a novel framework that applies social choice methods to policy aggregation by interpreting preferences through occupancy polytope volumes.

Findings

Approval voting, Borda count, and other methods can be adapted for policy aggregation.

Geometric interpretation enables practical application of social choice methods.

Framework supports AI alignment with multiple stakeholders.

Abstract

We consider the challenge of AI value alignment with multiple individuals that have different reward functions and optimal policies in an underlying Markov decision process. We formalize this problem as one of policy aggregation, where the goal is to identify a desirable collective policy. We argue that an approach informed by social choice theory is especially suitable. Our key insight is that social choice methods can be reinterpreted by identifying ordinal preferences with volumes of subsets of the state-action occupancy polytope. Building on this insight, we demonstrate that a variety of methods--including approval voting, Borda count, the proportional veto core, and quantile fairness--can be practically applied to policy aggregation.