  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.
