Endogenous Epistemic Weighting under Heterogeneous Information

TL;DR

This paper introduces the ESCM, a lightweight method for endogenous epistemic weighting in collective decisions, which improves decision quality by inferring individual competences without prior knowledge.

Contribution

It proposes the ESCM mechanism that adaptively assigns issue-specific weights based on short assessments, enhancing collective decision accuracy under heterogeneity.

Findings

Bounded, competence-sensitive weights improve aggregate decision quality.

Numerical results show higher signal-to-noise ratios with ESCM compared to unweighted majority.

Analytical results confirm increased mean quality with heterogeneity using ESCM.

Abstract

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.