Full Information Equivalence in Large Elections
Full Information Equivalence in Large Elections
复制标题
大型选举中的完全信息对等
DOI：
10.2139/ssrn.3183959
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Lucas Siga
中科院分区：
文献类型：
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作者：
Paulo Barelli;Sourav Bhattacharya;Lucas Siga
我们研究的问题，聚集在选举中的私人信息有两个或两个以上的替代品的一个大家庭的评分规则。我们引入一个可行性条件，
线性细化条件，描述了当选民人数增加时，信息可以渐进地聚合：必须存在一个效用函数，在信号上呈线性分布，与原始效用函数共享相同的顶部选择。我们的研究结果补充了现有的工作，其中强有力的假设强加于环境，并警告潜在的误报时，施加太多的结构。
We study the problem of aggregating private information in elections with two or more alternatives for a large family of scoring rules. We introduce a feasibility condition, the
linear refinement condition, that characterizes when information can be aggregated asymptotically as the electorate grows large: there must exist a utility function, linear in distributions over signals, sharing the same top alternative as the primitive utility function. Our results complement the existing work where strong assumptions are imposed on the environment, and caution against potential false positives when too much structure is imposed.