This chapter illustrates some examples of how computational modeling has been used to tackle the complex adaptive systems that underpin the emergence of language structure: individual learning, cultural transmission, and biological evolution. It surveys an approach that has become particularly important in evolutionary linguistics, which arose out of a concern with the challenge of finding a scientific methodology for language evolution: computational modeling. The chapter discusses the biological evolution of innate signaling. Smith explores a large space of possible algorithms for updating an agent's matrix, and finds that there are many different ones that are capable of successfully acquiring an optimal signaling system. In order to have a complete picture of the biological foundations for language, one needs to consider the process of cultural evolution, and specifically how different learning mechanisms influence the emergence of structure in a population of individuals through iterated learning.