  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.
