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 o…
Read moreI 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.
