Convergence of Multi-Issue Iterative Voting under Uncertainty
Convergence of Multi-Issue Iterative Voting under Uncertainty
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DOI：
10.48550/arxiv.2301.08873
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Joshua Kavner;R. Meir;Francesca Rossi;Lirong Xia
中科院分区：
文献类型：
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作者：
Joshua Kavner;R. Meir;Francesca Rossi;Lirong Xia
我们研究战略行为在不确定性下对多个问题的迭代投票中的影响。我们引入了一种模型合成，以同时进行多发投票，并与Meir，Lev和Rosenschein（2014）的局部优势理论确定其收敛性。在证明局部优势改善动态可能无法收敛之后，我们提出了两种足够的模型改进，可以保证从任何初始投票概况中融合二进制问题的收敛：约束代理具有O-法律偏好，并赋予对其修改的问题较少的不确定性，而不是其他问题。我们的经验研究表明，尽管循环在没有不确定性的情况下很常见，但引入不确定性几乎可以保证在实践中。
We study the effect of strategic behavior in iterative voting for multiple issues under uncertainty. We introduce a model synthesizing simultaneous multi-issue voting with Meir, Lev, and Rosenschein (2014)'s local dominance theory and determine its convergence properties. After demonstrating that local dominance improvement dynamics may fail to converge, we present two sufficient model refinements that guarantee convergence from any initial vote profile for binary issues: constraining agents to have O-legal preferences and endowing agents with less uncertainty about issues they are modifying than others. Our empirical studies demonstrate that although cycles are common when agents have no uncertainty, introducing uncertainty makes convergence almost guaranteed in practice.