# kaal:claim:6192998-018

**Claim.** Selecting one agent per job by weighted random selection appears efficient but is mathematically suboptimal for quality assurance, since expected quality equals only the reputation weighted average rather than the best available output.

**Type.** failure  **Support.** argued

**Holds when.**

- reputation correlates with true quality only imperfectly

**Source quote.**

> The Calcaterra-Kaal-Andrei framework selects a single agent per job through weighted random selection.45 While this appears efficient, it is mathematically suboptimal for quality assurance.

**From.** Wulf A. Kaal, *Evolution of Domain-Specific Reputation Systems From Binary Validation to Citation-Weighted Knowledge Attribution* (2026), III.B. The Quality-Efficiency Paradox, page 19

**Cite as.** Wulf A. Kaal, Evolution of Domain-Specific Reputation Systems From Binary Validation to Citation-Weighted Knowledge Attribution (2026). SSRN: https://ssrn.com/abstract=6192998

**Verify.** sha256 of source PDF `b04292561ee041e0c9eaa7eca28a410ed440e76a95743a539361a3f76c97f2b3` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202026%20-%20Evolution%20of%20Domain-Specific%20Reputation%20Systems%20From%20Binary%20Validation%20to%20Citation-Weighted%20Knowledge%20Attribution.pdf

**Failure mode.** quality-efficiency-paradox  (family: governance-participation-collapse)

**Topics.** ai-and-agents, economics, empirical-evidence

**Keywords.** single-agent-selection, quality-efficiency-tradeoff, order-statistics, selection-mechanisms

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