意见动力学的凸区域：参照成瘾与遗忘的二元共识复杂性方法——集成二聚体模型视角
Convex Regions of Opinion Dynamics, Approaches to the Complexity of Binary Consensus with Reference to Addiction and Obliviousness: Integrated Dimer Model Perspective
AI总结：
本文提出集成二聚体模型，通过计算凸区域的Castellain矩阵、分布函数和二聚体构型概率，并引入依赖性与遗忘因素，研究意见动力学中的二元共识复杂性，为理解社会意见形成提供新方法。
摘要：
意见动力学领域起源于早期将磁物理学方法应用于理解社会意见形成的研究。该领域的一个核心挑战在于建模多样意见如何共存并相互影响。在社会议题领域，本研究利用二聚体构造和二聚体模型建立了一个理论框架。通过数值模拟，我们展示了所提出的模型如何应用于现实世界中的社会意见形成场景。该模型涉及对具有不同位置和距离的凸区域计算Castellain矩阵（K）、分布函数（Z）以及二聚体构型概率（P(D)）。它探索了凸区域的变化如何影响二聚体构型的概率。此外，我们的模型考虑了意见形成过程中的两个关键因素：“依赖性”和“遗忘”。它还深入探讨了意见的“距离”和“位置”概念。数值模拟结果阐明了我们的模型如何有效捕捉现实世界社会意见形成所涉及的过程。本研究为更深入地理解社会意见形成过程以及制定应对现实世界社会问题的策略奠定了基础。本质上，我们的研究引入了一种新颖的方法，用于在意见动力学领域内理解和剖析意见形成的复杂动态。这一理论框架结合基于数值模拟的方法，提供了超越意见动力学范围的新见解。它在社会科学、物理学和计算建模领域开辟了新的视角，最终有助于更深刻地理解社会意见形成。
英文摘要：
The field of opinion dynamics has its roots in early research that applied methods from magnetic physics to gain insights into the formation of social opinions. A central challenge in this field lies in modeling how diverse opinions coexist and exert influence on each other. In the realm of social issues, it's In this study, we leverage the dimer construct and the dimer model to establish a theoretical framework. Through numerical simulations, we demonstrate how this proposed model can be applied to real-world scenarios of social opinion formation. The model involves the computation of the Castellain matrix (K), the distribution function (Z), and the probability of dimer configuration (P(D)) for convex regions with varying positions and distances. It explores how alterations in convex regions impact the probability of dimer configuration. Furthermore, our model takes into account two critical factors: "dependence" and "forgetting" in the process of opinion formation. It also delves into the concepts of "distance" and "location" of opinions. The results of numerical simulations shed light on how our model effectively captures the processes involved in real-world social opinion formation. This study lays the groundwork for a deeper comprehension of the social opinion formation process and the development of strategies to address real-world social issues. In essence, our research introduces a novel methodology for comprehending and dissecting the intricate dynamics of opinion formation within the realm of opinion dynamics. This theoretical framework, coupled with a numerical simulation-based approach, offers fresh insights that extend beyond the confines of opinion dynamics. It opens up new perspectives in the domains of social science, physics, and computational modeling, ultimately contributing to a more profound understanding of social opinion formation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
