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The initial concept for this project was to challenge and enrich a multiple-networks social science approach to understanding the evolution of human societies by engaging more recent work within evolutionary biology and other natural sciences, tracing the emergence of new species and structures. Following a series of meetings to explore the parameters of this cross-disciplinary research, the group concluded that mathematical modeling of linguistic autocatalysis would be the most fruitful approach.
The project will now focus on examining how semantic networks (of language, concepts, and symbols) interact and produce collective memory, engaging more directly with scholars in behavioral economic and mathematics. By focusing on the idea of collective memory, defined as a representation that can be invoked independently of the context where it was created, they will develop several rich historical cases of the emergence of semantic networks for which mathematical representations of their evolution are possible. The aim is to fully examine the process of symbolic emergence, and represent it formally in a way that is portable to other settings.