Simple, but Not Too Simple: Learnability vs. Functionality in Language Evolution

We show that artificial language evolution involves the interplay of two opposing forces: pressure toward simple representations imposed by the dynamics of collective learning, and pressure towards complex representations imposed by requirements of agents’ tasks. The push-pull of these two forces results in the emergence of a language that is balanced: “simple but not too simple.” We introduce the classification game to study the emergence of these balanced languages and their properties. Our agents use artificial neural networks to learn how to solve tasks, and a simple counting algorithm to simultaneously learn a language as a form-meaning mapping. We show that task-language coupling drives the simplicity-complexity balance, and that both compositional and holistic languages can emerge.