Abstract:

[Purpose/Significance] This study aims to systematically examine the application of simulation modeling in bibliometrics and to clarify its methodological position within the broader framework of digital humanities tools and agricultural knowledge services. In particular, the paper highlights the innovative potential of integrating simulation modeling with generative artificial intelligence, which enables more flexible representation of heterogeneous behaviors and context-dependent decision-making processes. By bridging bibliometrics, digital humanities tools, and agricultural knowledge services, this research contributes to the theoretical advancement of bibliometric methodology and provides a structured foundation for future applications in agricultural information practice. [Method/Process] This study adopts a systematic literature-based analytical approach to review and synthesize major simulation modeling methods applied in bibliometrics. The analysis covers several representative categories of simulation models, including dynamic modeling of classical bibliometric laws, evolution models of co-authorship and citation networks, multi-agent-based simulation, information and knowledge diffusion models, and evolutionary game-theoretic models. These methods are examined with respect to their modeling objects, underlying assumptions, key parameters, and analytical capabilities. Rather than organizing the review solely by research topics, this study emphasizes simulation modeling logic as the central analytical thread. Each category of simulation method is analyzed in terms of how micro-level rules and interactions generate macro-level bibliometric patterns. Particular attention is paid to the role of digital humanities tools in operationalizing these models, especially through visualization, system integration, and interactive simulation environments that facilitate exploration and interpretation. In addition, this study introduces recent advances in generative artificial intelligence, particularly large language model-based agents, as an extension of traditional multi-agent simulation. By incorporating generative AI into simulation frameworks, it becomes possible to model heterogeneous agents with richer cognitive representations, adaptive behaviors, and contextual reasoning abilities. The methodological discussion draws on theoretical foundations from bibliometrics, complex systems, and computational social science, while also considering practical constraints related to data availability, model calibration, and validation. [Results/Conclusions] The analysis demonstrates that simulation modeling significantly enhances the explanatory power of bibliometric research by revealing dynamic mechanisms behind literature growth, collaboration structures, and knowledge diffusion processes. Compared with traditional static indicators, simulation-based approaches provide deeper insights into how bibliometric patterns emerge and evolve over time. The integration of generative artificial intelligence further expands this capability by enabling more realistic modeling of behavioral heterogeneity and context-sensitive decision-making among research actors. From an application perspective, the study shows that simulation models and associated digital humanities tools can be effectively embedded into agricultural knowledge service workflows. These applications include research evaluation, scientific information services, and policy communication, where simulation-based scenario analysis can support strategic planning and decision-making. At the same time, the study identifies several challenges, including data quality constraints, computational costs, and issues related to model interpretability and transparency. The findings suggest that future research should focus on improving data integration, enhancing model validation strategies, and further exploring the integration of generative AI to support more adaptive and explainable simulation systems. By doing so, simulation-based bibliometrics can play a more substantial role in advancing agricultural information services and research management in complex, data-intensive environments.

Key words: digital humanities, knowledge services, agents, simulation methods, generative artificial intelligence, bibliometrics