Bhargav Thota | Searcharxiv

arXiv2025

Order-disorder-order transitions and winning margins' scaling in kinetic exchange opinion model

The kinetic exchange opinion model shows a well-studied order disorder transition as the noise parameter, representing discord between interacting agents, is increased. A further increase in the noise drives the model, in low dimensions, to an extreme segregation ordering through a transition of similar nature. The scaling behavior of the winning margins have distinct features in the ordered and disordered phases that are similar to the observations noted recently in election data in various countries, explaining the qualitative differences in such scaling between tightly contested and land-slide election victories.

physics.soc-ph↗

arXiv2024

Kinetic exchange opinion dynamics for the battleground-states in the 2024 US presidential elections

The strongly polarizing political discourse in the U. S. implies that a small minority of the population, determining the outcome of the presidential elections in a few so called battleground-states, also determines the outcome of the overall election. Given the almost equal distributions of the electoral college members in the so-called blue and red states, the members elected from these battleground states would determine the election results. We build a kinetic exchange opinion model that takes into account the dynamical nature of the opinions of the individuals in the battleground states and the already determined core voters of the non-battleground states. In a fully connected graph, we consider the interaction among the population in the battleground states while the agents in the non-battleground states are assumed to have fixed opinions. We provide the analytical results and numerical simulations using realistic parameters from the opinion poll of the previous election's data. Counter-intuitively, a more noisy environment predicts a higher chance of the Democrats' win.

physics.soc-ph↗