In nature, many social species demonstrate collective behavior ranging from coordinated motion in flocks of birds and schools of fish to collective decision making in humans. Such distinct behavioral patterns at the group level are the consequence of local interactions among the individuals. We can learn from these biological systems, which have successfully evolved to operate in noisy and fault-prone environments, and understand how these complex interactions can be applied to engineered systems where robustness remains a major challenge. This dissertation addresses a two-scale approach to study these interactions- one in larger scale, where we are interested in the information exchange in a group and how it enables the group to reach a common decision, and the other in a smaller scale, where we are focused in the presence and directionality in the information exchange in a pair of individuals. To understand the interactions at large scale, we use a graph theoretic approach to study consensus or synchronization protocols over two types of biologically-inspired interaction networks. The first network captures both collaborative and antagonistic interactions and the second considers the impact of dynamic leaders in presence of purely collaborative interactions. To study the interactions at small scale, we use an information theoretic approach to understand the directionality of information transfer in a pair of individual using a real-world data-set of animal group motion. Finally, we choose the issue of same-sex marriage in the United States to demonstrate that collective opinion formation is not only a result of negotiations among the individuals, but also reflects inherent spatial and political similarities and temporal delays.