  Significant advances have been made in artificial systems by using biological
systems as a guide. However, there is often little interaction between
computational models for emergent communication and biological models of the
emergence of language. Many researchers in language origins and emergent
communication take compositionality as their primary target for explaining how
simple communication systems can become more like natural language. However,
there is reason to think that compositionality is the wrong target on the
biological side, and so too the wrong target on the machine-learning side. As
such, the purpose of this paper is to explore this claim. This has theoretical
implications for language origins research more generally, but the focus here
will be the implications for research on emergent communication in computer
science and machine learning---specifically regarding the types of programmes
that might be expected to work and those which will not. I further suggest an
alternative approach for future research which focuses on reflexivity, rather
than compositionality, as a target for explaining how simple communication
systems may become more like natural language. I end by providing some
reference to the language origins literature that may be of some use to
researchers in machine learning.
