JOURNAL ARTICLE

Polynomial Voting Rules.

  • Published In: Mathematics of Operations Research (INFORMS), 2025, v. 50, n. 1. P. 90 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Tang, Wenpin; Yao, David D. 3 of 3

Abstract

The article introduces and analyzes a new class of polynomial voting rules, denoted **Poly(α)**, designed for decentralized consensus systems, with a focus on the proof-of-stake (PoS) blockchain protocol. Unlike traditional voting where voting power equals voter share, Poly(α) decouples these by scaling voting power with a parameter α, allowing voting power to diminish over time, which enhances security by preventing any single voter from dominating the process. The study rigorously characterizes the stochastic behavior of voter shares and voting powers, showing that shares form a martingale converging to a Dirichlet distribution, while voting powers form a supermartingale that decays to zero, with explicit convergence rates. It also identifies a phase transition in the stability of voter shares based on initial stake size and extends the model to include stake trading among bidders, analyzing optimal strategies under varying risk sensitivities and demonstrating conditions under which bidders prefer nonparticipation or buyout strategies. The work further discusses practical implications for controlling block validation times by dynamically adjusting α and outlines potential extensions involving market impact models.

Additional Information

  • Source:Mathematics of Operations Research (INFORMS). 2025/02, Vol. 50, Issue 1, p90
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2025
  • ISSN:0364-765X
  • DOI:10.1287/moor.2023.0080
  • Accession Number:182907651
  • Copyright Statement:Copyright of Mathematics of Operations Research (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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