  Since first introduced, computer simulation has been an increasingly
important tool in evolutionary linguistics. Recently, with the development of
deep learning techniques, research in grounded language learning has also
started to focus on facilitating the emergence of compositional languages
without pre-defined elementary linguistic knowledge. In this work, we explore
the emergence of compositional languages for numeric concepts in multi-agent
communication systems. We demonstrate that compositional language for encoding
numeric concepts can emerge through iterated learning in populations of deep
neural network agents. However, language properties greatly depend on the input
representations given to agents. We found that compositional languages only
emerge if they require less iterations to be fully learnt than other
non-degenerate languages for agents on a given input representation.
