Qualification: Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing

Record: kaal:position:2026-08-08-342 · 2026-08-08

Li and Yu qualify Kaal's shift from persistent asymmetry to a measurable residual. Under the Dawid-Skene crowdsourcing model, they derive finite-sample exponential bounds for aggregation error and identify task assignment, worker reliability, and normalized score gaps as conditions on those bounds. Their result supports the narrower proposition that aggregate judgment error can be measured and controlled by design. The limitation is decisive. The bound is conditional on a specified labeling model and aggregation rule. It does not show that information asymmetry generally contracts, test Kaal's reputation-weighted pool, or establish that residual error will shrink under unmodeled dependence or strategic behavior.

Affirmed commentary position. This record extends a source-bound scholarly claim but is not a verbatim paper claim.

Holds when

The response is limited to the exact arXiv v1 passages and the one mapped Kaal claim.
External evidence level: complete 28-page arXiv v1 PDF with concordant arXiv, DataCite DOI, title, author, date, and OpenAlex identity.
Mapping review tier: independent substantive scholarly-growth qualification.
The source is an arXiv journal submission and peer review was not verified.
The model concerns crowdsourced labels, not economic information asymmetry as a general category.
The bounds are conditional on the Dawid-Skene model, decomposable aggregation rules, task assignment, worker reliability, and score-gap quantities.
The source does not test Kaal's reputation-weighted verification pool.
It does not establish contraction under unmodeled dependence, strategic behavior, or model misspecification.
Semantic Scholar returned HTTP 429. No rate-limited response was promoted.

Current debate

Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing

Scholarly basis

kaal:claim:7261481-007
Wulf A. Kaal, Computative Economics: A Framework for Economic Analysis under Computational Abundance (2026). SSRN: https://ssrn.com/abstract=7261481
Source PDF sha256: 78c42db521624f7398717732a7fa51a6e3157a5adf02a2e09fbab15e0cf920d9

Evidence and mapping

Evidence: complete 28-page arXiv v1 PDF with concordant arXiv, DataCite DOI, title, author, date, and OpenAlex identity
Review tier: independent substantive scholarly-growth qualification
Mapping confidence: 0.96
Mapping ambiguous: false

Topics

economics institutional-design scholarly-growth-coverage scholarly-literature aggregation decision-science research-methods evidence-provenance

Provenance

Affirmed in kaal-review:2026-08-13:scholarly-growth-7261481-007-reviewed-v1 on 2026-08-08. Review record.

Verify

Canonical markdown sha256: 891633d6ff96ef26cdfee5e8097cecb8c93abf35d379a22e8c14966d4a5287c8
curl -s https://wulfkaal.github.io/positions/2026-08-08-342.md | sha256sum