Individuals, despite having varied life experiences and learning processes, can communicate effectively through languages. This study aims to explore the efficiency of language as a communication medium. We put forth two specific hypotheses: First, discrete messages are more effective than continuous ones when agents have diverse personal experiences. Second, communications using multiple discrete tokens are more advantageous than those using a single token. To valdate these hypotheses, we designed multi-agent machine learning experiments to assess communication efficiency using various information transmission methods between speakers and listeners. Our empirical findings indicate that, in scenarios where agents are exposed to different data, communicating through sentences composed of discrete tokens offers the best inter-agent communication efficiency. The limitations of our finding include lack of systematic advantages over other more sophisticated encoder-decoder model such as variational autoencoder and lack of evluation on non-image dataset, which we will leave for future studies.

翻译：个体尽管拥有不同的生活经历和学习过程，仍能通过语言进行有效沟通。本研究旨在探索语言作为沟通媒介的效率。我们提出两个具体假设：第一，当智能体具有多样化的个人经验时，离散消息比连续消息更有效；第二，使用多个离散令牌的通信比使用单个令牌更具优势。为验证这些假设，我们设计了多智能体机器学习实验，通过说话者与听者之间的多种信息传递方式评估通信效率。实证结果表明，在智能体接触不同数据的场景下，使用由离散令牌组成的句子进行通信能实现最佳智能体间通信效率。本研究发现的局限性包括：相较于变分自编码器等更复杂的编码器-解码器模型缺乏系统性优势，以及未在非图像数据集上进行评估，这些将留待未来研究。