From Validator Selection to Portfolio Collection Optimization in Proof-of-Stake Blockchains

• URL: http://arxiv.org/abs/2606.08282v1
• Date: Sat, 06 Jun 2026 18:06:53 GMT
• Title: From Validator Selection to Portfolio Collection Optimization in Proof-of-Stake Blockchains
• Abstract summary: We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators.<n>We propose a decision support framework to optimize this selection by simultaneously maximizing two objectives.<n>The resulting bi-objective optimization problem is solved with a multi-objective evolutionary algorithm.
• Score: 0.4893345190925178
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for maintaining the blockchain's physical infrastructure. The selection process is inherently subjective and multi-criterial and combines with the fact that nominators commonly operate through multiple accounts. This gives rise to a portfolio selection problem, where agents seek to distribute their nominations across accounts to diversify risk. We propose a decision support framework to optimize this selection by simultaneously maximizing two objectives: the expected utility of the validators likely to be allocated, representing portfolio quality and profitability, and the expected entropy of the allocation, representing diversification and risk mitigation across stashes. Validator utilities are derived using an original active preference learning procedure based on multi-attribute value theory, with emphasis on top-ranked validators. The resulting bi-objective optimization problem is solved with a multi-objective evolutionary algorithm and, to support the final choice, we introduce an interactive binary search navigation procedure that guides the nominator through the front and identifies a satisfactory trade-off with only a few questions. Numerical experiments examine the optimization strategies, while an expert assessment involving five experienced nominators confirms the approach's practical relevance and usefulness.

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