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Game theory based compatible incentive mechanism design for non-cryptocurrency blockchain systems

Citation

Li, Xiang and Lui, Q and Wu, S and Cao, Z and Bai, Q, Game theory based compatible incentive mechanism design for non-cryptocurrency blockchain systems, Journal of Industrial Information Integration, (31) Article 100426. ISSN 2452-414X (2023) [Refereed Article]


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DOI: doi:10.1016/j.jii.2022.100426

Abstract

With the success of Bitcoin, nowadays, both academia and industry have been focusing on the expansion of blockchain in non-cryptocurrency applications to facilitate the development of Industry 4.0. The incentive mechanism in blockchain, which encourages miners to participate in validation processes, is important to the maintenance of the system. However, the techniques, which are widely applied in existing blockchain systems, are not suitable for non-cryptocurrency applications with business models and have become a major issue that undermines the adoption of blockchain. In this paper, we propose a compatible incentive mechanism for non-cryptocurrency applications to encourage validatorsí actions to align with consensus algorithm. The compatible incentive mechanism works with a consensus algorithm to make the blockchain technology more adaptable in industry applications. The proposed design matches with the voting-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm which is adopted by most non-cryptocurrency applications. Game theory-based analysis is conducted to identify the problems caused by participantsí misbehaviours. Meanwhile, a mechanism design based on game theory analysis is adopted to develop our incentive mechanism which satisfies the compatibility requirement. Both mathematical proof and simulation experiments are used to evaluate our mechanism design. The results demonstrate that our compatible incentive mechanism encourages the desired honest behaviour among validators so that the blockchain can be maintained to align with the consensus algorithm.

Item Details

Item Type:Refereed Article
Keywords:Agent-based modelling, game theory, distributed systems
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Autonomous agents and multiagent systems
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Computer systems
UTAS Author:Li, Xiang (Ms Xiang Li)
UTAS Author:Wu, S (Mr Shiqing Wu)
UTAS Author:Cao, Z (Dr Zehong Cao)
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:154603
Year Published:2023
Deposited By:Information and Communication Technology
Deposited On:2022-12-14
Last Modified:2023-01-06
Downloads:0

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