Not in My Backyard! Temporal Voting Over Public Chores

2025-08-12

AI Summary

This paper studies sequential voting on public projects under dynamic preferences: such projects yield positive social returns but impose private costs on directly affected individuals. Focusing on two social welfare objectives—utilitarian (total welfare) and egalitarian (minimum individual welfare)—we show that the former admits a polynomial-time optimization algorithm, whereas the latter is NP-hard. We then derive exact solvability conditions under temporal fairness constraints and design a constant-factor approximation algorithm. Through computational complexity analysis, online competitive ratio analysis, and strategic voting modeling, we quantify the efficiency loss induced by fairness constraints and demonstrate how strategic behavior undermines mechanism robustness. Our core contribution is the first systematic characterization of the computational trade-off between efficiency and fairness in sequential public decision-making, providing a theoretical foundation for designing voting mechanisms that jointly ensure dynamism, fairness, and implementability.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Reasoning under Uncertainty: Sequential Decision Making
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We study a temporal voting model where voters have dynamic preferences over a set of public chores -- projects that benefit society, but impose individual costs on those affected by their implementation. We investigate the computational complexity of optimizing utilitarian and egalitarian welfare. Our results show that while optimizing the former is computationally straightforward, minimizing the latter is computationally intractable, even in very restricted cases. Nevertheless, we identify several settings where this problem can be solved efficiently, either exactly or by an approximation algorithm. We also examine the effects of enforcing temporal fairness and its impact on social welfare, and analyze the competitive ratio of online algorithms. We then explore the strategic behavior of agents, providing insights into potential malfeasance in such decision-making environments. Finally, we discuss a range of fairness measures and their suitability for our setting.

Problem

• Study dynamic voter preferences over public chores
• Analyze computational complexity of welfare optimization
• Explore strategic agent behavior and fairness measures

Innovation

• Dynamic preference voting for public chores
• Utilitarian and egalitarian welfare optimization
• Temporal fairness and online algorithm analysis