An incentive mechanism based on game theory in Optimistic Rollup

Optimistic Rollup is a blockchain scalability solution. However, it assumes that transactions are correct unless challenged by a challenger, which leads to the concept of a challenge period. If there is no challenge during this time, the user needs to wait until the end of the challenge period, which is very inconvenient for the user. This is because challengers and task publishers are self-interested and aim to maximize their own benefits, leading to internal incentive issues within Optimistic Rollup. Although zero-knowledge proofs can effectively address this issue, ZK rollups face challenges such as high computational complexity and the need for transaction-specific proofs. Optimistic rollups have low computational requirements and simple design, making the design of an effective incentive mechanism a good choice. This paper proposes a reward-based incentive model to encourage rational workers to actively verify transactions based on game theory. The rewards in this incentive model are related to a time factor, where earlier verification yields greater rewards, motivating verifiers to verify transactions more quickly. We simulate under various proportions of false declarations and the results show that our method could incentivize the workers effectively to exert their best efforts.

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Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.04.112
Citations
2
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
4.96
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article

An incentive mechanism based on game theory in Optimistic Rollup

Qiuling Yue, Cheng Cheng, Zhong Chen, Weijian Jiang
2 citations
Procedia Computer Science
Reinforcement Learning in Robotics
4.96
article

An incentive mechanism based on game theory in Optimistic Rollup

Qiuling Yue, Cheng Cheng, Zhong Chen, Weijian Jiang
article en
2 citations

Abstract

Optimistic Rollup is a blockchain scalability solution. However, it assumes that transactions are correct unless challenged by a challenger, which leads to the concept of a challenge period. If there is no challenge during this time, the user needs to wait until the end of the challenge period, which is very inconvenient for the user. This is because challengers and task publishers are self-interested and aim to maximize their own benefits, leading to internal incentive issues within Optimistic Rollup. Although zero-knowledge proofs can effectively address this issue, ZK rollups face challenges such as high computational complexity and the need for transaction-specific proofs. Optimistic rollups have low computational requirements and simple design, making the design of an effective incentive mechanism a good choice. This paper proposes a reward-based incentive model to encourage rational workers to actively verify transactions based on game theory. The rewards in this incentive model are related to a time factor, where earlier verification yields greater rewards, motivating verifiers to verify transactions more quickly. We simulate under various proportions of false declarations and the results show that our method could incentivize the workers effectively to exert their best efforts.

Procedia Computer ScienceVol. 259
Hainan University (CN)
Openalex Percentile: Top 5%
Reinforcement Learning in Robotics
4.96
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An incentive mechanism based on game theory in Optimistic Rollup — Qiuling Yue, Cheng Cheng, et al. · Procedia Computer Science (2025) | TGRS Research Map | TGRS