Artificial Delegates Resolve Fairness Issues in Perpetual Voting with Partial Turnout
This addresses fairness problems in sequential collective decision-making for scenarios with incomplete participation, representing an incremental improvement.
The paper tackled fairness issues in perpetual voting systems under partial turnout by integrating Artificial Delegates, preference-learning agents representing absent voters, and found that they reliably mitigate the negative effects of absenteeism and enhance robustness.
Perpetual voting addresses fairness in sequential collective decision-making by evaluating representational equity over time. However, existing perpetual voting rules rely on full participation and complete approval information, assumptions that rarely hold in practice, where partial turnout is the norm. In this work, we study the integration of Artificial Delegates, preference-learning agents trained to represent absent voters, into perpetual voting systems. We examine how absenteeism affects fairness and representativeness under various voting methods and evaluate the extent to which Artificial Delegates can compensate for missing participation. Our findings indicate that while absenteeism significantly affects fairness, Artificial Delegates reliably mitigate these effects and enhance robustness across diverse scenarios.