Lipowska Dorota, Lipowski Adam
Faculty of Modern Languages and Literature, Adam Mickiewicz University in Poznań, 61-712 Poznań, Poland.
Faculty of Physics, Adam Mickiewicz University in Poznań, 61-712 Poznań, Poland.
Entropy (Basel). 2022 Jan 27;24(2):194. doi: 10.3390/e24020194.
Synonyms and homonyms appear in all natural languages. We analyze their evolution within the framework of the signaling game. Agents in our model use reinforcement learning, where probabilities of selection of a communicated word or of its interpretation depend on weights equal to the number of accumulated successful communications. When the probabilities increase linearly with weights, synonyms appear to be very stable and homonyms decline relatively fast. Such behavior seems to be at odds with linguistic observations. A better agreement is obtained when probabilities increase faster than linearly with weights. Our results may suggest that a certain positive feedback, the so-called Metcalfe's Law, possibly drives some linguistic processes. Evolution of synonyms and homonyms in our model can be approximately described using a certain nonlinear urn model.
同义词和同音异义词存在于所有自然语言中。我们在信号博弈的框架内分析它们的演变。我们模型中的主体使用强化学习，其中选择一个传达的单词或其解释的概率取决于等于累积成功通信次数的权重。当概率与权重呈线性增加时，同义词似乎非常稳定，同音异义词相对快速减少。这种行为似乎与语言学观察结果不一致。当概率比与权重呈线性增加更快时，可以得到更好的一致性。我们的结果可能表明，某种正反馈，即所谓的梅特卡夫定律，可能驱动了一些语言过程。我们模型中同义词和同音异义词的演变可以使用某种非线性瓮模型进行近似描述。
