Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems. Issue 2 (13th October 2022)

• Record Type: Journal Article
• Authors: Böttcher, Lucas; Kernell, Georgia
• Is Part Of: Collective intelligence. Volume 1:Issue 2(2022)
• Publication Date: 2022-10-13
• Subjects: Condorcet's Jury Theorem -- information aggregation -- decision making -- voting -- abstention; Computational intelligence -- Periodicals; Collective behavior -- Periodicals; Swarm intelligence -- Periodicals; 006.3
• DOI: 10.1177/26339137221133401
• Languages: English
• ISSNs: 2633-9137

Abstract

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 equalsN d, where N d is the total number of voters in the direct system). In multitier systems, we prove that this difference is maximized when group size equalsN 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.