关键词: 复杂网络, 演化博弈, 生成式人工智能, 技术扩散

Abstract:

[Purpose/Significance] Against the backdrop of intensifying global technological competition and the drive for scientific and technological progress under national innovation strategies, generative artificial intelligence (AI) technology, as an emerging disruptive technology, has had a profound impact on the economy and society through its widespread application. However, the diffusion of this technology in the market still faces numerous challenges. This paper aims to delve into the micro-level decision-making factors influencing enterprises' research and development (R&D) of generative AI technology, as well as the specific impact of user group interactions on the effectiveness of technology diffusion, by constructing a complex network evolutionary game model. The research seeks to uncover the inherent laws governing technology diffusion, providing a scientific basis for policymakers and corporate practitioners to promote the healthy development and effective diffusion of generative AI technology, thereby fostering comprehensive socio-economic progress. [Method/Process] This paper adopts the complex network evolutionary game model as the primary research method, integrating complex network theory, technological innovation diffusion theory, and social influence theory to construct a game model for corporate decision-making regarding generative AI technology. By incorporating the structural characteristics of complex networks and the dynamic mechanisms of evolutionary games, the study simulates the R&D decision-making processes of enterprises under varying conditions of user adoption rates, government subsidy levels, differences in technology benefits and costs, and technology spillover effects. Simultaneously, numerical simulation analysis is employed to explore the specific impacts of changes in these factors on the diffusion effectiveness of generative AI technology decisions, thereby thoroughly revealing the micro-mechanisms underlying technology diffusion. [Results/Conclusions] The research results indicate that an increase in user adoption rates significantly and positively drives the diffusion of generative AI technology, with moderate user dependency behaviors further accelerating this process. Government subsidies play a particularly prominent role in promoting technology diffusion when user adoption rates and the initial proportion of enterprises choosing R&D strategies in the network are low. However, as these proportions rise, the marginal effect of subsidies gradually diminishes. The difference in benefits between enterprises that develop generative AI technology and those that do not has a marked impact on technology diffusion, whereas the impact of cost differences is relatively minor. Furthermore, the spillover effects of generative AI technology may induce free-rider behaviors among other enterprises, hindering technology diffusion. Additionally, when the maturity level of generative AI technology is low, it reduces user trust in the technology, thereby inhibiting its widespread dissemination. Based on these conclusions, this paper proposes policy recommendations such as encouraging user participation, flexibly adjusting subsidy policies, enhancing technology maturity, and establishing intellectual property laws and regulations to facilitate the effective diffusion of generative AI technology.

Key words: complex network, evolutionary game, generative AI, technology diffusion