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Keywords

digital innovation ecosystem, multi agent collaboration, fuzzy cooperation, benefit distribution mechanism

Abstract

The digital innovation ecosystem is a typical complex network system with symbiotic coupling and nonlinear interaction characteristics among diverse components due to the influence of digital technology. It not only inherits the general characteristics of the innovation ecosystem, but also presents unique characteristics, such as stronger heterogeneity of innovation entities, more complex resource flow, more dynamic business processes, and more open data sharing. The 14th Five Year Plan for National Economic and Social Development of the People's Republic of China and the Outline of Long Range Goals for 2035 point out the need to accelerate digital development, create new advantages in the digital economy, promote deep integration of the digital economy and the real economy, and accelerate the construction of a new economic development pattern with domestic circulation as the main body and international and domestic dual circulation promotion. The White Paper on the Development of China's Digital Economy (2021) points out that the digital economy is a new economic form driven by digital technology as the core driving force, utilizing modern information networks to effectively integrate key production factors such as digital knowledge and information, in order to promote the deep integration of digital technology and the real economy, and accelerate the reconstruction of economic development and governance models. Therefore, the core of the digital innovation ecosystem is to coordinate the interaction between entities, and achieving collective competition and win-win cooperation is the key to the stable development of the digital innovation ecosystem. This study takes multi-agent collaboration from the perspective of the digital innovation ecosystem as the background, explores the benefit allocation mechanism to promote multi-agent collaborative behavior based on the characteristics of multi-agent, and systematically analyzes the benefit composition and allocation mechanism of multi-agent collaboration based on the theoretical logic of “subject intention collaboration stage-subject collaboration game stage-comprehensive evaluation collaboration stage”, in order to explore the relationship between subjects, and the influencing factors between subjects and distribution The exchange and dissemination of information between the subject and collaborative connecting factors. Secondly, a multi-agent collaborative benefit allocation mechanism was designed from the perspective of the digital innovation ecosystem, and based on the initial model, benefit allocation adjustments were made to the influencing factors. It was concluded that the benefit allocation mechanism under collaborative games not only ensures the stability of subject cooperation, but also makes the benefit allocation more fair and reasonable. The research results indicate that the factor contribution coefficient and synergy degree positively affect the cooperation benefits of the digital innovation ecosystem entities, while the cost coefficient has little impact on it.The benefit distribution mechanism under collaborative games ensures the stability of subject cooperation; Compared to the collaborative observation stage, the distribution of benefits is more fair and reasonable. The research conclusion not only opens up the theoretical “black box” of the impact of collaborative benefit allocation on the collaborative development of entities in the digital innovation ecosystem, but also improves and develops the exploration and application of benefit allocation theory and game theory in the field of digital innovation ecosystem, which provides a new perspective for building a benefit allocation mechanism in the digital innovation ecosystem, while also expanding the application scenarios of benefit allocation mechanisms in the field of subject collaboration in the digital innovation ecosystem. It provides new ideas for subject collaboration to formulate benefit allocation strategies, eliminate resource barriers, and explore value gaps.

DOI

10.16315/j.stm.2023.06.004

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