Electoral Process

Electoral processes are being rigorously analyzed to improve fairness, efficiency, and security, with a focus on understanding voter behavior and optimizing voting systems. Current research employs various computational methods, including agent-based simulations, machine learning algorithms (like BERT models), and novel distance metrics for comparing election results, to model and analyze different voting systems and their susceptibility to manipulation. These studies aim to enhance the accuracy and transparency of elections, potentially leading to more representative outcomes and increased voter confidence through improved technologies and a deeper understanding of electoral dynamics. The findings inform the design of more effective and equitable voting systems and tools for election management.