Collective decision-making requires aggregating multiple noisy information channels about an unknown state of the world. Classical epistemic justifications of majority rule rely on homogeneity assumptions often violated when individual competences are heterogeneous. This paper studies endogenous epistemic weighting in binary collective decisions. It introduces the Epistemic Shared-Choice Mechanism (ESCM), a lightweight and auditable procedure that generates bounded, issue-specific voting weights from short informational assessments. Unlike likelihood-optimal rules, ESCM does not require ex ante knowledge of individual competences, but infers them endogenously while bounding individual influence. Using a central limit approximation under general regularity conditions, the paper establishes analytically that bounded competence-sensitive monotone weighting strictly increases the mean quality of the aggregate signal whenever competence is heterogeneous. Numerical comparisons under Beta-distributed and segmented mixture competence environments show that these mean gains are associated with higher signal-to-noise ratios and large-sample accuracy relative to unweighted majority rule.

翻译：集体决策需要整合关于未知世界状态的多个嘈杂信息渠道。多数决的经典认识论辩护依赖于同质性假设，但个体能力存在异质性时这一假设常被违背。本文研究二元集体决策中的内生成知权重，提出"认识共享选择机制"（ESCM）——一种轻量级、可审计的程序，通过简短的信息评估生成有界、议题特定的投票权重。与似然最优规则不同，ESCM不要求事先知晓个体能力，而是内生推断能力的同时约束个体影响力。利用中心极限近似及一般正则条件，本文从理论上证明：当能力存在异质性时，有界且对能力敏感的非递减权重会严格提升聚合信号的平均质量。在Beta分布与分段混合能力环境下的数值比较表明，相对于未加权多数决，这些平均增益与更高的信噪比及大样本精度相关联。