Population-level linguistic conventionality benefits the robustness, learnability, and cognitive efficiency of emergent languages
Human languages are widely understood to be conventional: for a given meaning, a population of language users expect a certain form to be used. Given the lack of unconventional natural languages with which to compare, there is little direct empirical evidence which explains why linguistic conventionality matters. We carry out a series of emergent language experiments with artificial agents designed to exhibit the effect of conventionality on robustness, learnability, and cognitive efficiency. By experimentally manipulating the interaction dynamics through which agents align their vocabularies, we create conditions that either promote or prevent population-level convergence, resulting in conventional and unconventional emergent languages while holding communicative success constant. We find that conventional languages are indeed more robust, better learnable, and more efficient, when compared with less conventional languages.