# kaal:claim:6192998-019

**Claim.** Multi agent competitive collaboration captures a diversity dividend worth more than half a standard deviation of quality improvement while enabling attribution through citation graphs.

**Type.** design  **Support.** argued

**Holds when.**

- quality approximately normally distributed across competing agents
- competition size of roughly three to seven agents

**Source quote.**

> Our multi-agent competitive collaboration protocol captures diversity dividends worth 0.5+ standard deviations of quality improvement while enabling attribution through citation graphs.

**From.** Wulf A. Kaal, *Evolution of Domain-Specific Reputation Systems From Binary Validation to Citation-Weighted Knowledge Attribution* (2026), VII.A. Summary of Contributions, page 56

**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

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

**Keywords.** multi-agent-competition, diversity-dividend, quality-improvement, order-statistics, citation-graphs

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