kaal:position:2026-07-31-7700

Frequency adjusted multi-agent Q-learning presents the following source proposition: In today's interconnected world, such systems are ubiquitous in many domains, including auctions in economics, swarm robotics in computer science, and politics in social sciences. This proposition is pertinent to Kaal's source-bound claim that Domains using multi agent competition will exhibit fifteen to thirty percent higher quality scores than single agent selection, controlling for agent capability. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.

• The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.
• External evidence level: abstract indexed.
• Mapping review tier: moderate-confidence claim review.
• Primary mapping confidence: 0.3528.
• The source-to-claim mapping remains explicitly ambiguous and is published with that limitation.

Frequency adjusted multi-agent Q-learning

kaal:claim:6192998-036
Wulf A. Kaal, Evolution of Domain-Specific Reputation Systems From Binary Validation to Citation-Weighted Knowledge Attribution (2026). SSRN: https://ssrn.com/abstract=6192998
Source PDF sha256: b04292561ee041e0c9eaa7eca28a410ed440e76a95743a539361a3f76c97f2b3

Evidence: abstract indexed
Review tier: moderate-confidence claim review
Mapping confidence: 0.3528
Mapping ambiguous: true

ai-and-agentseconomicshistorical-responsescholarly-literaturecrossref

Affirmed in kaal-review:2026-07-31:streaming-etl-0010 on 2026-07-31. Review record.

Canonical markdown sha256: d00c38265d1cdf4243f66e71e5c9b15ba1fc84d4e2b1f80d8021489ef4173785
curl -s https://wulfkaal.github.io/positions/2026-07-31-7700.md | sha256sum