Populations have often been perceived as a structuring component for language to emerge and evolve: the larger the population, the more structured the language. While this observation is widespread in the sociolinguistic literature, it has not been consistently reproduced in computer simulations with neural agents. In this paper, we thus aim to clarify this apparent contradiction. We explore emergent language properties by varying agent population size in the speaker-listener Lewis Game. After reproducing the experimental difference, we challenge the simulation assumption that the agent community is homogeneous. We first investigate how speaker-listener asymmetry alters language structure to examine two potential diversity factors: training speed and network capacity. We find out that emergent language properties are only altered by the relative difference of learning speeds between speaker and listener, and not by their absolute values. From then, we leverage this observation to control population heterogeneity without introducing confounding factors. We finally show that introducing such training speed heterogeneities naturally sort out the initial contradiction: larger simulated communities start developing more stable and structured languages.
翻译:通常人们将人口视为语言出现和演变的结构性组成部分:人口越多,语言越有结构化。虽然这一观察在社会语言学文献中很普遍,但在与神经剂的计算机模拟中并没有一贯地复制。 在本文中,我们的目标是澄清这一明显的矛盾。我们在语音-听力者 Lewis Game 中以不同的代理人口规模探索突发语言特性。在复制了实验差异之后,我们质疑模拟假设,即代理群体是同质的。我们首先调查说者-听者不对称如何改变语言结构,以研究两种潜在的多样性因素:培训速度和网络能力。我们发现,新兴语言特性仅因演讲者和听众之间学习速度的相对差异而不是其绝对价值而改变。从那时起,我们利用这种观察来控制人口异常性,而不引入混杂因素。我们最后显示,引入这种培训速度差异自然会消除初始矛盾:更多的模拟社区开始发展更加稳定和结构化的语言。