Understanding cultural dynamics in human societies is the first step towards solving many complex social issues. In this dissertation I focus on the drivers of diffusion and adoption of cultural traits, such as values, beliefs, and behaviors. I adopt an evolutionary view of cultural dynamics. Particularly, I use concepts from dual inheritance theories of cultural evolution to develop and test an agent-based model capable of simulating the changing distributions of cultural traits in a large population of actors over the course of prolonged periods of time. Particularly, I pay close attention to the mechanisms of indirectly biased transmission of traits and guided variation, which are both hypothesized to be significant drivers of cultural dynamics. Indirectly biased transmission consists of the adoption of specific trait variants on the basis of possession of initially unrelated external markers. Guided variation is then individual adaptation driven by self-exploration.;I use various methods to explore the pathways of cultural evolution. Among them are agent-based modeling, evolutionary computation, complex network analysis and statistical data analysis. Furthermore, I make use of large publicly available datasets to validate my models. The first one of these is the database of bill co-sponsorship in the U.S. House of Representatives from 1973 to 2008. The other is a comprehensive dataset of scientific co-authorship in various disciplines stretching back for over a century. The results show that cultural evolution models based on indirectly biased transmission and guided variation are suitable to explaining the dynamics of various complex social networks. Furthermore, I show that this type of cultural evolution leads to emergence of meaningful cultural signs by gradually associating previously independent external markers with specific cultural trait variants. Finally, I describe how the proposed model leads to plausible social network configurations.
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