考察孔多塞陪审团定理的局限性:层次信息聚合系统中的权衡

L. Böttcher, G. Kernell
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引用次数: 2

摘要

孔多塞的陪审团定理指出,只要每个选民对该结果的独立投票概率大于1/2,在有足够多选民的直接多数投票系统中就会得出正确的结果。先前的研究发现,转换到等级制度总是导致较差的结果。然而,在许多情况下,直接投票是不可行的(例如,由于高实施或基础设施成本),分层投票可能提供一个合理的替代方案。本文研究了不同群体规模、弃权率和选民能力的分层和直接投票系统的准确率差异。我们得出了三个主要结果。首先,我们证明了当群体规模和数量相等时(即当每个群体都等于Nd时,其中Nd是直接系统中选民的总数),间接双层系统与直接双层系统的差异最大。在多层系统中,我们证明了当群体大小等于N d N时,这种差异是最大的,其中N是分层层的数量。其次,我们表明,虽然直接多数决原则在同质选民中总是优于间接投票,但当群体数量或每个群体中的个人数量增加时,等级投票的准确性就会提高。第三,我们证明了当选民弃权和能力在群体内相关时,等级制度可以优于直接投票。研究结果的影响不仅限于投票,还包括大脑中的信息处理、动物群体的集体认知以及机器学习中的信息聚合。
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Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems
Condorcet’s Jury Theorem states that the correct outcome is reached in direct majority voting systems with sufficiently large electorates as long as each voter’s independent probability of voting for that outcome is greater than 1/2. Previous research has found that switching to a hierarchical system always leads to an inferior result. Yet, in many situations direct voting is infeasible (e.g., due to high implementation or infrastructure costs), and hierarchical voting may provide a reasonable alternative. This paper examines differences in accuracy rates of hierarchical and direct voting systems for varying group sizes, abstention rates, and voter competences. We derive three main results. First, we prove that indirect two-tier systems differ most from their direct counterparts when group size and number are equal (i.e., when each equals N d , where Nd is the total number of voters in the direct system). In multitier systems, we prove that this difference is maximized when group size equals N d n , where n is the number of hierarchical levels. Second, we show that while direct majority rule always outperforms indirect voting for homogeneous electorates, hierarchical voting gains in accuracy when either the number of groups or the number of individuals within each group increases. Third, we prove that when voter abstention and competency are correlated within groups, hierarchical systems can outperform direct voting. The results have implications beyond voting, including information processing in the brain, collective cognition in animal groups, and information aggregation in machine learning.
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