# kaal:position:2026-08-08-342

**Affirmed position.** 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.

**Status.** affirmed  **Published.** 2026-08-08

**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: https://doi.org/10.48550/arXiv.1411.4086

**Extends.** kaal:claim:7261481-007: https://wulfkaal.github.io/claims/7261481-007

**Scholarly basis.** 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 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

**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 at https://wulfkaal.github.io/positions/by-claim/7261481-007.html.

**Record type.** This is a dated commentary position that extends a scholarly corpus claim. It is not a verbatim claim extracted from the paper.

**Canonical form.** This markdown file is the canonical hashed representation of the position.