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An active preference learning approach to aid the selection of validators in blockchain environments


[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Published in


Journal year: 2023 | Journal volume: vol. 118

Article type

scientific article

Publication language


  • Blockchain ecosystem
  • Validator selection
  • Multiple criteria decision analysis
  • Active learning
  • Preference learning
  • Multi-attribute value function

EN We consider a real-world problem faced in some blockchain ecosystems that select their active validators - the actors that maintain the blockchain - from a larger set of candidates through an election-based mechanism. Specifically, we focus on Polkadot, a protocol that aggregates preference lists from another set of actors, nominators, that contain a limited number of trusted validators and thereby influence the election’s outcome. This process is financially incentivized but often overwhelms human decision makers due to the problem’s complexity and the multitude of available alternatives. This paper presents a decision support system (DSS) to help the nominators choose the validators in an environment with frequently changing data. The system structures the relevant multiple attribute problem and incorporates a dedicated active learning algorithm. Its goal is to find a sufficiently small set of pairwise elicitation questions to infer nominators’ preferences. We test the proposed solution in an experiment with 115 real nominators from the Polkadot ecosystem. The empirical results confirm that our approach outperforms the unaided process in terms of required interaction time, imposed cognitive effort, and offered efficacy. The developed DSS can be easily extended to other blockchain ecosystems.

Date of online publication






Article Number: 102869

Ministry points / journal


Impact Factor

6,9 [List 2022]

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