Stable communication through dynamic language

Abstract

I use agent-based computational models of inferential language transmission to investigate the relationship between language change and the indeterminacy of meaning. I describe a model of communication and learning based on the inference of meaning through disambiguation across multiple contexts, which is then embedded within an iterated learning model. The dynamic flexibility and uncertainty inherent in the model leads directly to variation between agents, in both their conceptual and lexical structures. Over generations of repeated meaning inference, this variation leads to significant language change. Despite such change, however, the language maintains its utility as a communicative tool within each individual generation.