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<p>I propose a ranking-based aggregation model utilizing quantiles, such as the median or first quartile. This approach is broader than most in existing literature, as it does not require competent individuals. It recovers the asymptotic convergence property of finite estimations in the Condorcet Jury Theorem as a special case. This procedure is radical in occasionally granting greater respect to minority opinions. An optimistic conclusion is that individual errors can be mitigated and the wisdom of crowds manifested through intelligent aggregation.</p>

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A skewed jury theorem: more theorems in search of the truth

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月18日

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  • Berna Kilinc

摘要

I propose a ranking-based aggregation model utilizing quantiles, such as the median or first quartile. This approach is broader than most in existing literature, as it does not require competent individuals. It recovers the asymptotic convergence property of finite estimations in the Condorcet Jury Theorem as a special case. This procedure is radical in occasionally granting greater respect to minority opinions. An optimistic conclusion is that individual errors can be mitigated and the wisdom of crowds manifested through intelligent aggregation.