Automata networks for memory loss effects in the formation of linguistic conventions
This work provides theoretical insights into language evolution for linguists and computational modelers, but it is incremental as it builds on existing models with a simple memory approach.
The paper tackled the problem of how populations reach linguistic conventions by modeling memory loss effects using automata networks, finding sharp transitions at critical values dependent on individual vicinities through computer simulations.
This work attempts to give new theoretical insights to the absence of intermediate stages in the evolution of language. In particular, it is developed an automata networks approach to a crucial question: how a population of language users can reach agreement on a linguistic convention? To describe the appearance of sharp transitions in the self-organization of language, it is adopted an extremely simple model of (working) memory. At each time step, language users simply loss part of their word-memories. Through computer simulations of low-dimensional lattices, it appear sharp transitions at critical values that depend on the size of the vicinities of the individuals.