Collective decision-making can be viewed as the problem of aggregating multiple noisy information channels about an unknown state of the world. Classical epistemic justifications of majority rule rely on restrictive assumptions about the homogeneity and symmetry of these channels, which are often violated in realistic environments. This paper introduces the Epistemic Shared-Choice Mechanism (ESCM), a lightweight and auditable procedure that endogenously estimates issue-specific signal reliability and assigns bounded, decision-specific voting weights. Using central limit approximations, the paper provides an analytical comparison between ESCM and unweighted majority rule, showing how their relative epistemic performance depends on the distributional structure of information in the population, including unimodal competence distributions and segmented environments with informed minorities. The results indicate that endogenous and bounded epistemic weighting can improve collective accuracy by merging procedural and epistemic requirements.

翻译：集体决策可被视为对关于未知世界状态的多个噪声信息通道进行聚合的问题。多数决原则的经典认知论证依赖于这些通道同质性与对称性的严格假设，而这些假设在现实环境中常被违背。本文提出认知共享选择机制（ESCM），这是一种轻量级且可审计的程序，能够内生估计议题特定的信号可靠性，并分配有界的、决策特定的投票权重。通过中心极限近似，本文对ESCM与非加权多数决进行了分析比较，揭示了两者的相对认知表现如何取决于群体中信息的分布结构，包括单峰能力分布和存在知情少数群体的分割环境。结果表明，内生且有界的认知加权能够通过融合程序性与认知性要求，提升集体决策的准确性。