# kaal:claim:7456999-012

**Claim.** The 225-configuration sweep covers 4,500 trials, treats party accuracy as an input, and is reported as simulation rather than validation.

**Type.** methodological  **Support.** asserted

**Holds when.**

- the parameter sweep reported in Part V.B

**Source quote.**

> A 225-configuration parameter sweep across 4,500 trials supplies a two-mode comparison the cohort cannot observe. It does not establish an equilibrium-trajectory separation. Finding 7’s coefficients of variation do not define or test that separation. In that sweep the parties are distributions whose accuracy is an input, and it is reported as a simulation rather than as validation.

**From.** Wulf A. Kaal, *Reinforcement Learning from Reputation Feedback* (2026), Abstract, page 2

**Cite as.** Wulf A. Kaal, Reinforcement Learning from Reputation Feedback (2026). SSRN: https://ssrn.com/abstract=7456999

**Verify.** sha256 of source PDF `01016fbbcc25cad205cebd59ea686d8dfee8ae9b632095d86ba6c9a900456a4b` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202026%20-%20Reinforcement%20Learning%20from%20Reputation%20Feedback.pdf

**Topics.** research-methods, empirical-evidence, ai-and-agents

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.