Environmental Variability and the Emergence of Meaning: Simulational Studies Across Imitation, Genetic Algorithms, and Neural Networks

A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show the emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of “communicators” develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. Here we focus on environmental variability, comparing results for environments with (a) constant resources, (b) random resources, and (c) cycles of “boom and bust.” In both simple and complex models across all three mechanisms of strategy change, the emergence of communication is strongly favored by cycles of “boom and bust.” These results are particularly intriguing given the importance of environmental variability in fields as diverse as psychology, ecology and cultural anthropology.