e objective of this dissertation is to explore and develop models of opinion dynamics for distributed multi-agent systems, focusing on the diverse behaviors of consensus and dissensus under various interaction rules in unsigned graphs. A novel aspect of this dissertation is the modeling of opinion states as unit-length vectors on a sphere, representing unique measures of expressed opinions. The evolution of these state vectors illustrates the change in opinions of each agent, influenced by neighboring opinions. The theoretical contributions establish foundational models for understanding rich opinion behaviors, including consensus and various forms of dissensus. These models are significant not only for describing individual and group behaviors in social networks, but also for explaining different communication methods when agents interact and exchange information. On the application front, the models are applied to multi-robot task allocation. In these contexts, opinion dynamics facilitate upper-level decision-making processes. The research thus bridges theoretical insights and practical implementations, enhancing the understanding and utility of opinion dynamics in complex systems. This work bridges the gap between theoretical models and applications of opinion dynamics in distributed systems. By developing and applying innovative models, the research contributes to a deeper understanding of how opinions evolve and influence decision-making processes in complex, multi-agent environments. This work not only advances the field of opinion dynamics but also provides valuable insights and tools for practical implementations in various technological and social domains.