  Emergent communication research often focuses on optimizing task-specific
utility as a driver for communication. However, human languages appear to
evolve under pressure to efficiently compress meanings into communication
signals by optimizing the Information Bottleneck tradeoff between
informativeness and complexity. In this work, we study how trading off these
three factors -- utility, informativeness, and complexity -- shapes emergent
communication, including compared to human communication. To this end, we
propose Vector-Quantized Variational Information Bottleneck (VQ-VIB), a method
for training neural agents to compress inputs into discrete signals embedded in
a continuous space. We train agents via VQ-VIB and compare their performance to
previously proposed neural architectures in grounded environments and in a
Lewis reference game. Across all neural architectures and settings, taking into
account communicative informativeness benefits communication convergence rates,
and penalizing communicative complexity leads to human-like lexicon sizes while
maintaining high utility. Additionally, we find that VQ-VIB outperforms other
discrete communication methods. This work demonstrates how fundamental
principles that are believed to characterize human language evolution may
inform emergent communication in artificial agents.
