# kaal:position:2026-08-08-339

**Affirmed position.** Kokkodis and Ipeirotis show that reputation design affects prediction. Their model replaces an undifferentiated average of prior feedback with category-specific histories and cross-category weights. On held-out oDesk transactions, this representation reduces prediction error relative to aggregate-history baselines. The result supports testing whether a structured reputation signal contains information for future task performance that a simpler outcome-history summary omits.

The comparison also narrows Kaal's H2. The external model constructs reputation from prior employer ratings. It does not compare an independently generated reputation measure with the full observed outcome history. It therefore does not establish that reputation adds information beyond outcomes as such. The source studies human online labor markets, not multi-model agents or Kaal's registered Stage 4 design. Its contribution is methodological. H2 must distinguish incremental information in reputation from predictive gains created by transforming the same outcome history.

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

**Holds when.**

- The response is limited to the exact WSDM 2013 passages and the one mapped Kaal claim.
- External evidence level: complete public refereed WSDM 2013 conference paper with concordant Crossref, DBLP, author page, DOI, authorship, venue, pages, and exact proposition-bearing passages.
- Mapping review tier: independent substantive scholarly-growth qualification.
- The source studies human workers and employer ratings on oDesk, not multi-model agents or Kaal's registered Stage 4 design.
- The reputation representation is constructed from prior task feedback and therefore does not establish information independent of observed task outcomes.
- The worker-level split tests prediction on workers absent from training, but the source does not report Kaal's exact estimand, outcome measure, or cohort controls.
- The authors classify the model as predictive and not necessarily causal.
- Semantic Scholar returned HTTP 429. No rate-limited or metadata-only response was promoted.

**Current debate.** Have you done anything like that? Predicting Performance Using Inter-category Reputation: https://doi.org/10.1145/2433396.2433450

**Extends.** kaal:claim:7261481-004: https://wulfkaal.github.io/claims/7261481-004

**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 public refereed WSDM 2013 conference paper with concordant Crossref, DBLP, author page, DOI, authorship, venue, pages, and exact proposition-bearing passages

**Mapping review tier.** independent substantive scholarly-growth qualification

**Mapping confidence.** 0.98  **Mapping ambiguous.** false

**Topics.** research-methods, reputation, scholarly-growth-coverage, scholarly-literature, prediction, online-labor-markets, evidence-provenance

**Provenance.** Affirmed in kaal-review:2026-08-13:scholarly-growth-7261481-004-reviewed-v1 at https://wulfkaal.github.io/positions/by-claim/7261481-004.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.