Co-evolving Language and Social Structure Using a Genetic Algorithm

It is interesting how we can take a train of thought and transfer this into another person's mind by pushing the air around us. Human language, this complexmedium that distinctly separates humans from animals, has baffled scientists forcenturies. But as it lacks of historical data, researchers wish to benefit fromcomputer science and the field of artificial life to understand the origin oflanguage. This thesis illuminates the potential for using agent-based models toinvestigate the relationship between biology, culture and behavior on anindividual level.This is done in two parts. First, different theories and computational modelsexperimenting with language evolution are presented. This includes a thoroughimplementation of and elaborations on one recent paper, where languageacquisition is illustrated favorable over multiple evolutionary time scales inan agent-based model. In the second part, a more bio-inspired methodology isproposed to make the former model more robust and better suited for extensions.This is demonstrated by letting the agents evolving some social biases, whilethey are conducting a naming game in a social structure. A naming game is anabstraction, often used in the research field, to model the spreading anddiversity of language. Through pair-wise dialogs, the goal of the game is toreach self-organized agreement on naming an arbitrary object in theirenvironment. Given the assumption that communication is beneficial for socialstructure and that social structure is beneficial for reproduction, theexperimental work demonstrates that agents are able to build social structuresthat resembles real life social topologies, although the naming game mighthappen too rapid in respect to the evolving social structure.Hopefully, with support from other disciplines, the presented model is suitedfor further investigation of social, or other functional, traits that caninfluence language evolution