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Endogenous Epistemic Weighting under Heterogeneous Information: Beyond Majority Rule

Enrico Manfredi

Abstract

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.

Endogenous Epistemic Weighting under Heterogeneous Information: Beyond Majority Rule

Abstract

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.
Paper Structure (36 sections, 37 equations, 6 figures)

This paper contains 36 sections, 37 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Model of the Decision
  3. Epistemic Collective Choice as an Inference Problem
  4. Individual Signals as Information Channels
  5. Votes as Signal Transmission
  6. Aggregation Rules and Collective Choice
  7. Likelihood-Based Aggregation and Information Summation
  8. Endogeneity and Informational Constraints
  9. An Epistemic Shared-‑Choice Mechanism for Collective Decisions
  10. Step 1: Item authoring (topic coverage)
  11. Step 2: Peer review (quality control)
  12. Step 3: Pool construction
  13. Step 4: Individual assessment
  14. Step 5: Weight construction
  15. Step 6: Weighted aggregation and outcome selection
  16. ...and 21 more sections

Figures (6)

  • Figure 1: CJT success probability under Beta-distributed competence.
  • Figure 2: ESCM with linear weights ($l_a=10$, $k=1$).
  • Figure 3: ESCM with log-odds weights ($l_a=10$).
  • Figure 4: CJT success probability under CMM-3 wide competence.
  • Figure 5: ESCM with linear weights under CMM-3 wide competence ($l_a=10$, $k=1$).
  • ...and 1 more figures

Theorems & Definitions (7)

  • Remark 1: Noise as non-discriminating information
  • Remark 2: Parameter Flexibility
  • Remark 3: Coupling assessment length and curvature in the power map
  • Remark 4: Asymptotic Convergence to Nitzan--Paroush Optimality
  • Remark 5: Log-Odds Generalization
  • Remark 6: Computational Tractability and Scalability
  • Remark 7: Epistemic Accuracy and Inequality of Influence