Social Choice Foundations for Simulation-Augmented Generation

Users increasingly turn to AI systems for normative assistance—guidance on what one ought to do or think—yet models are often opaque about whose viewpoints they represent. A promising approach is *simulation-augmented generation* (SAGE), which involves querying generative simulations of individuals in a target population at inference time, soliciting their open-ended judgments, and synthesizing them into a response while transparently reporting whose viewpoints are reflected. However, inference-time simulation raises acute *scalability constraints*. Since the key benefit of simulation is improved *representativeness*, the core challenge is scaling simulation without sacrificing representation. We introduce the first formalization of this problem, grounded in proportional clustering concepts from social choice theory. We prove that to represent a population of $m$ humans, we need only create $n \ll m$ simulations of them, and need only dynamically query $k \ll n$ of those simulations at inference time, while still maintaining approximate proportional representation guarantees for the full population. We empirically validate that our inference-time algorithm yields better representation--efficiency trade-offs than baseline approaches.