Zulkaida Akbar

National Research and Innovation Agency, Indonesia

18 April 2024 Thu 4 pm

                                      IBS Center for Theoretical Physics of Complex Systems (PCS), Administrative Office (B349), Theory Wing, 3rd floor

                                      Expo-ro 55, Yuseong-gu, Daejeon, South Korea, 34126 Tel: +82-42-878-8633                     

Opinions shape our societies, driving decision-making processes and influencing collective behaviors. Understanding the mechanisms behind opinion formation and evolution is crucial for predicting societal trends. Network topologies play a crucial role in shaping opinion dynamics since the structure of the underlying network greatly influences how opinions spread, converge, or diverge within a population. Leveraging the Ising model—a powerful tool adapted from statistical physics— we explore opinion dynamics across diverse network topologies: Watts-Strogatz (W-S), Barabasi-Albert (B-A), and Erdos-Renyi (E-R). Through comprehensive simulations, we explore how network topology impacts key aspects of opinion dynamics. In this presentation, I will discuss how combining network science with statistical physics can help us better understand opinion dynamics, a key challenge in complex systems.

Additionally, I will briefly discuss my research involving the application of machine learning in advanced spectroscopies, including techniques such as Muon-Spin Relaxation and Laser-Induced Breakdown spectroscopies. I will also cover the machine-learning application in Muon tomography and battery research.

  1. unraveling opinion dynamics: insights from network science and ising model