异质信息下的内生认知权重
Endogenous Epistemic Weighting under Heterogeneous Information
AI总结：
本文研究二元集体决策中的内生认知权重问题，提出Epistemic Shared-Choice Mechanism机制，通过短信息评估生成有界、问题特定的投票权重，无需提前了解个体能力，且在能力异质时提升整体信号质量。
摘要：
集体决策需要聚合多个关于未知世界的噪声信息渠道。经典认知正当性主要规则依赖于同质性假设，这在个体能力异质时常被违反。本文研究二元集体决策中的内生认知权重，引入Epistemic Shared-Choice Mechanism（ESCM）机制，一种轻量且可审计的程序，从短信息评估中生成有界、问题特定的投票权重。与似然最优规则不同，ESCM无需事前了解个体能力，但通过内生推断能力并限制个体影响。在一般正则条件下，使用中心极限近似，本文证明有界能力敏感单调权重严格增加聚合信号的均值质量，当能力异质时。在Beta分布和分段混合能力环境下进行的数值比较显示，这些均值增益与更高的信噪比和大样本准确性相关，相对于无权多数规则。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
