Online Approval Committee Elections

TL;DR

This paper studies online approval-based committee selection where candidates appear sequentially and decisions are made immediately, analyzing proportional representation and optimizing expected scores with probabilistic information.

Contribution

It introduces methods for online approval committee elections, focusing on proportional representation and maximizing expected committee scores with prior approval probabilities.

Findings

Proposes algorithms for proportional representation in online settings.

Develops methods to maximize expected committee scores using prior probabilities.

Analyzes the effectiveness of online approval voting in dynamic candidate scenarios.

Abstract

Assume $k$ candidates need to be selected. The candidates appear over time. Each time one appears, it must be immediately selected or rejected -- a decision that is made by a group of individuals through voting. Assume the voters use approval ballots, i.e., for each candidate they only specify whether they consider it acceptable or not. This setting can be seen as a voting variant of choosing $k$ secretaries. Our contribution is twofold. (1) We assess to what extent the committees that are computed online can proportionally represent the voters. (2) If a prior probability over candidate approvals is available, we show how to compute committees with maximal expected score.