ACP-based Dynamic Incentive Mechanism for Information Sharing in DAO

Abstract

As a new paradigm in the Web3 era, Decentralized Autonomous Organizations (DAOs) not only embodie the spirit of decentralization and collective governance, but also are the forefront of promoting community-led innovation. However, DAOs face challenges in sustainable growth and scalability in community governance, which are closely related to the income distribution model and member participation. Therefore, the Artificial systems, Computational experiments, Parallel execution (ACP) approach is applied to optimize the contribution evaluation and incentive feedback of member behavior through parallel governance and decision-making methods, so as to improve the intelligence of the DAO incentive mechanism. On this basis, the long short-term memory network (LSTM) and combinatorial game theory are used to conduct experimental verification on information sharing within the community. The experimental results show that our proposed method can not only achieve a high degree of information sharing in the community faster than other methods, but also has the ability of autonomous dynamic adjustment. It is of great significance and value to the community governance research and scenario implementation of DAOs.