基于复杂网络演化博弈的生成式人工智能技术扩散研究

doi:10.13998/j.cnki.issn1002-1248.25-0493

Abstract

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

CLC Number:

F046.2

LI Dan, FENG Danran. Diffusion of Generative Artificial Intelligence Technology Based on Complex Network Evolutionary Game[J].Journal of library and information science in agriculture, 2025, (): 1-16.

Figures/Tables 11

Table 1

Model parameter description"

参数	含义	参数	含义
$p 1$	生成式人工智能技术产出的产品或服务的价格	$θ$	技术的外溢比例
$y 1$	生成式人工智能技术带来的产品或服务的生产函数	$b$	生成错误信息的可能性
$k 2 2$	生成式人工智能技术的研发成本	$O$	损失金额
$s$	政府对生成式人工智能技术企业的研发补贴	$L$	企业的市场机会损失成本
$p 2$	延续性技术产出的产品或服务的价格	$R$	用户使用技术给企业带来的潜在收益
$y 2$	延续性技术带来的产品或服务的生产函数	$λ$	网络中研发生成式人工智能技术企业的比例
$c 1$	延续性技术的成本	$M$	网络中生成式人工智能技术用户总数
$N$	网络中生成式人工智能技术企业总数	$z$	用户使用生成式人工智能技术的比例
$α$	用户的依赖程度

Table 1

Fig.1

Fig.2

Table 2

Parameter settings"

$y 1$	$p 1$	$k 2 2$	$y 2$	$p 2$	$c 1$	$s$	$N$	$M$
0.015	3 000	34	0.024	2 400	40	5	200	408
$θ$	$b$	$O$	$L$	$R$	$α$	$k$	$λ$	$z$
0.25	0.3	5	35	0.72	0.65	0.1	0.08	0.05

Table 2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

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