# kaal:claim:6192998-027

**Claim.** Constraining the citation weight parameter below one and enforcing row stochasticity through normalization guarantees that the citation weighted value vector exists, is unique, and converges, which resolves the instability of the original recursive valuation formula.

**Type.** condition  **Support.** evidenced

**Holds when.**

- citation weight parameter strictly less than one
- contribution matrix normalized to be row stochastic

**Source quote.**

> This theorem addresses the instability concern with the recursive formula in Calcaterra, Kaal, and Andrei (2018, 41). By constraining and ensuring row-stochasticity through normalization, we guarantee convergence, a crucial property for autonomous operation.

**From.** Wulf A. Kaal, *Evolution of Domain-Specific Reputation Systems From Binary Validation to Citation-Weighted Knowledge Attribution* (2026), IV.C. Citation-Based Reward Allocation via PageRank, page 33

**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.** citation-and-knowledge

**Keywords.** pagerank, convergence, uniqueness, perron-frobenius, citation-weighting

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