Introducing First-Principles Calculations: New Approach to Group Dynamics and Bridging Social Phenomena in TeNP-Chain Based Social Dynamics Simulations
<|reference_start|>Introducing First-Principles Calculations: New Approach to Group Dynamics and Bridging Social Phenomena in TeNP-Chain Based Social Dynamics Simulations: This note considers an innovative interdisciplinary methodology that bridges the gap between the fundamental principles of quantum mechanics applied to the study of materials such as tellurium nanoparticles (TeNPs) and graphene and the complex dynamics of social systems. The basis for this approach lies in the metaphorical parallels drawn between the structural features of TeNPs and graphene and the behavioral patterns of social groups in the face of misinformation. TeNPs exhibit unique properties such as the strengthening of covalent bonds within telluric chains and the disruption of secondary structure leading to the separation of these chains. This is analogous to increased cohesion within social groups and disruption of information flow between different subgroups, respectively. . Similarly, the outstanding properties of graphene, such as high electrical conductivity, strength, and flexibility, provide additional aspects for understanding the resilience and adaptability of social structures in response to external stimuli such as fake news. This research note proposes a novel metaphorical framework for analyzing the spread of fake news within social groups, analogous to the structural features of telluric nanoparticles (TeNPs). We investigate how the strengthening of covalent bonds within TeNPs reflects the strengthening of social cohesion in groups that share common beliefs and values. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.<|reference_end|>
@article{kawahata2024introducing,
    title={Introducing First-Principles Calculations: New Approach to Group
  Dynamics and Bridging Social Phenomena in TeNP-Chain Based Social Dynamics
  Simulations},
    author={Yasuko Kawahata},
    journal={arXiv preprint arXiv:2403.05593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05593},
    primaryClass={physics.soc-ph cs.AI physics.ed-ph}
}