Full Information Equivalence in Large Elections
研究在多种计分规则下，大规模选举如何有效聚合私人信息，提出线性细化条件作为信息可渐近聚合的充要条件，对现有强假设研究提出警示。
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.