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Significant advances have been made in artificial systems by using biologicalsystems as a guide. However, there is often little interaction betweencomputational models for emergent communication and biological models of theemergence of language. Many researchers in language origins and emergentcommunication take compositionality as their primary target for explaining howsimple communication systems can become more like natural language. However,there is reason to think that compositionality is the wrong target on thebiological side, and so too the wrong target on the machine-learning side. Assuch, the purpose of this paper is to explore this claim. This has theoreticalimplications for language origins research more generally, but the focus herewill be the implications for research on emergent communication in computerscience and machine learning---specifically regarding the types of programmesthat might be expected to work and those which will not. I further suggest analternative approach for future research which focuses on reflexivity, ratherthan compositionality, as a target for explaining how simple communicationsystems may become more like natural language. I end by providing somereference to the language origins literature that may be of some use toresearchers in machine learning.

Biology and Compositionality: Empirical Considerations for Emergent-Communication Protocols