kaal:claim:6192998-027

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.

Source quote, verbatim

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, p. 33
https://ssrn.com/abstract=6192998 · source PDF

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

Holds when

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

Classification

conditionsupport: evidencedcitation-and-knowledge

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