Endogenous Epistemic Weighting under Heterogeneous Information
This addresses the challenge of improving decision accuracy in groups with varying expertise, though it is incremental as it builds on existing epistemic weighting frameworks.
The paper tackles the problem of aggregating noisy information in collective decision-making under heterogeneous individual competences by introducing the Epistemic Shared-Choice Mechanism (ESCM), which endogenously infers and applies bounded, issue-specific voting weights, resulting in increased mean signal quality and higher signal-to-noise ratios compared to unweighted majority rule.
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.