An Active Preference Learning Approach to Aid the Selection of Validators in Blockchain Environments

33 Pages Posted: 20 Oct 2022

See all articles by Jonas Gehrlein

Jonas Gehrlein

Web3 Foundation

Grzegorz Miebs

Poznan University of Technology - Institute of Computing Science

Matteo Brunelli

affiliation not provided to SSRN

Miłosz Kadziński

Poznan University of Technology

Abstract

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.

Keywords: Blockchain ecosystem, Validator selection, Multiple criteria decision analysis, Active learning

Suggested Citation

Gehrlein, Jonas and Miebs, Grzegorz and Brunelli, Matteo and Kadziński, Miłosz, An Active Preference Learning Approach to Aid the Selection of Validators in Blockchain Environments. Available at SSRN: https://ssrn.com/abstract=4253515

Jonas Gehrlein

Web3 Foundation ( email )

Zug
Switzerland

Grzegorz Miebs (Contact Author)

Poznan University of Technology - Institute of Computing Science ( email )

Pl. Marii Skłodowskiej-Curie 5
Poznań
Poland

Matteo Brunelli

affiliation not provided to SSRN

Miłosz Kadziński

Poznan University of Technology ( email )

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