Shortlisting: a Principled Approach
Edith Elkind, Qishen Han, Lirong Xia
TL;DR
Defines shortlisting as a two-stage decision process with a candidate space $\mathcal{S} \subseteq \mathcal{A}$ and two roles (shortlisters and agents); proposes a unified framework with axioms (recovery and shortlist-focused), aggregation rules, and incentive analysis. Formalizes the model via $r_L:(\mathcal{E}_{\mathcal{A}})^L \rightarrow 2^{\mathcal{A}}$ and $r_N: \mathcal{E}_{\mathcal{S}}^N \rightarrow \mathcal{D}_{\mathcal{S}}$, and discusses extensions with probabilistic preferences and information beyond preferences. Explores cognitive efficiency and the use of predictions to improve performance while maintaining robustness, and analyzes strategic behavior of participants through a game-theoretic lens. Aims to establish a principled, transparent foundation for shortlisting applicable to participatory budgeting, hiring, school choice, and AI-assisted governance.
Abstract
Shortlisting is the process of selecting a subset of alternatives from a larger pool for further consideration or final decision-making. It is widely applied in social choice and multi-agent system scenarios. The growing demand for participatory decision-making and the continuously expanding space of candidates create an urgent need for efficient and fair shortlisting procedures. However, little principled study has been done on this problem. This blue-sky paper aims to highlight the overlooked significance of shortlisting, distinguish it from related problems, provide initial thoughts, and, more importantly, serve as a call to arms. We envision that principled shortlisting can reduce cognitive burden, enable fair collective decisions, encourage broader participation, and ultimately build trust in democratic systems.