Query-Based Committee Selection

TL;DR

This paper explores how to efficiently select representative committees in large-scale elections by using limited, structured preference queries, balancing elicitation cost and outcome accuracy.

Contribution

It introduces a query-based framework for multiwinner elections, analyzes cost functions, and demonstrates effective recursive splitting strategies through experiments.

Findings

Recursive splitting strategies outperform other query types in cost-effectiveness.

Query-based elicitation can approximate k-Borda election outcomes with fewer preferences.

Well-designed queries significantly reduce preference elicitation while maintaining high-quality results.

Abstract

Purpose: Multiwinner voting rules typically require full knowledge of voter preferences, which becomes impractical in large-scale or attention-limited settings. This paper investigates how accurately a winning committee can be approximated when voter preferences are elicited using a limited budget of structured queries. Methods: We introduce a query-based framework for multiwinner elections in which voter preferences are elicited through refinement queries over subsets of candidates under a limited budget. We analyse several cost functions that model the cognitive effort needed to answer such queries, propose axiomatic properties for evaluating them, and experimentally evaluate simple query-based committee selection rules across multiple election models. Results: Experimental results show that strategies based on recursively splitting candidate sets provide the best trade-off between elicitation cost and committee accuracy. Across several statistical models, these strategies approximate the outcome of k-Borda elections significantly more efficiently than alternative query types. Conclusion: The results demonstrate that well-designed query strategies can substantially reduce the amount of preference information required while still producing high-quality committee outcomes, suggesting that query-based elicitation is a promising approach for scalable multiwinner decision-making.