Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers, Part I
Machine Learning, Optimization, and Data Science - 9th International Conference, LOD 2023, Grasmere, UK, September 22-26, 2023, Revised Selected Papers, Part I
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机器学习、优化和数据科学 - 第九届国际会议，LOD 2023，英国格拉斯米尔，2023 年 9 月 22-26 日，修订后的选定论文，第一部分
DOI：
10.1007/978-3-031-53969-5_1
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Majumdar S
中科院分区：
文献类型：
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作者：
Majumdar S
决策者自下而上分配公共资金的民主程序的合法性具有挑战性和复杂性。参与式预算就是这样一个过程，投票结果可能并不总是公平或包容的。对哪些项目方案进行投票和选择实施的审议缺乏系统性和规模性。本文通过引入一种新颖合法的基于共识的参与式预算流程来解决这些重大挑战。共识被设计为通过创新的多智能体强化学习方法进行决策支持的结果。帮助选民相互交流，做出可行的妥协。对波兰现实世界参与式预算数据进行的广泛实验评估揭示了惊人的发现：共识是可以达成的、高效的和稳健的。妥协是必要的，这与现有的没有达成共识却促进公平和包容的投票聚合方法类似。
The legitimacy of bottom-up democratic processes for the distribution of public funds by policy-makers is challenging and complex. Participatory budgeting is such a process, where voting outcomes may not always be fair or inclusive. Deliberation for which project ideas to put for voting and choose for implementation lack systematization and do not scale. This paper addresses these grand challenges by introducing a novel and legitimate iterative consensus-based participatory budgeting process. Consensus is designed to be a result of decision support via an innovative multi-agent reinforcement learning approach. Voters are assisted to interact with each other to make viable compromises. Extensive experimental evaluation with real-world participatory budgeting data from Poland reveal striking findings: Consensus is reachable, efficient and robust. Compromise is required, which is though comparable to the one of existing voting aggregation methods that promote fairness and inclusion without though attaining consensus.