Self-Assessing Agents for Explaining Language Change: A Case Study in German

摘要

Language change is increasingly recognized as one of the most crucial sources of evidence for understanding human cognition. Unfortunately, despite sophisticated methods for documenting which changes have taken place, the question of why languages evolve over time remains open for speculation. This paper presents a novel research method that addresses this issue by combining agent-based experiments with deep language processing, and demonstrates the approach through a case study on German definite articles. More specifically, two populations of autonomous agents are equipped with a model of Old High German (500-1100 AD) and Modern High German definite articles respectively, and a set of self-assessment criteria for evaluating their own linguistic performances. The experiments show that inefficiencies detected in the grammar by the Old High German agents correspond to grammatical forms that have actually undergone the most important changes in the German language. The results thus suggest that the question of language change can be reformulated as an optimization problem in which language users try to achieve their communicative goals while allocating their cognitive resources as efficiently as possible.