Novelties are a key driver of societal progress, yet we lack a comprehensive understanding of the factors that generate them. Recent evidence suggests that innovation emerges from the balance between exploiting past discoveries and exploring new possibilities, the so-called ``adjacent possible". This thesis aims at developing new analysis tools and models to study how people navigate the seemingly infinite space of possibilities. Firstly, I extend the notion of the adjacent possible to account for novelties as combinations of existing elements. In particular, I model innovation as a random walk on an expanding complex network of content, in which novelties correspond not only to the first visit of nodes, but also of links. The model correctly reproduces how novelties emerge in empirical data, highlighting the importance of the exploration process in shaping the growth of the network. Secondly, since people continuously interact and exchange information with each other, I investigate the role of social interactions in enhancing discoveries. I hence propose a model where multiple agents extend their adjacent possible through the links of a complex social network, exploiting in this way opportunities coming from their contacts. By adding a social dimension to the adjacent possible, I prove that the discovery potential of an individual is influenced by its position on the social network. Finally, I combine the two concepts of the adjacent possible in the content and social dimension to develop a data-driven model of music exploration on online platforms. In such a model, multiple agents grow their individual space of possibilities by exploring a network of similarity between artists, while exploiting suggestions from their friends on the social network. The comparison with the empirical data indicates that the adjacent possible, in both the content and the social space, plays a crucial role in determining the individual propensity to innovate
