Evolution of linguistic diversity in a simple communication system

Area of Science:

• Evolutionary biology
• Computational linguistics
• Artificial intelligence

Background:

• Investigating the origins and evolution of linguistic diversity is crucial for understanding communication systems.
• Referential signaling in nonhuman animals provides a basis for modeling abstract communication.

Purpose of the Study:

• To shed light on the origin and evolution of linguistic diversity using synthetic modeling and artificial life techniques.
• To analyze the evolutionary dynamics of vocabulary sharing.
• To examine the potential of linguistic diversity in distributed artificial intelligence and robotics.

Main Methods:

• Construction of a simple abstract model of a communication system.
• Analysis of evolutionary dynamics of vocabulary sharing based on experimental results.
• Development of an agent-based model to verify abstract model findings and explore applications.

Main Results:

• Mutation rates, population size, and resource restrictions were identified as key factors defining classes of vocabulary sharing.
• A dynamic equilibrium was observed, alternating between states with one dominant shared word and multiple dominant shared words.
• The evolution of linguistic diversity in vocabulary sharing was shown to support cooperative behavior in agent populations.

Conclusions:

• Synthetic modeling and artificial life techniques offer valuable insights into the evolution of linguistic diversity.
• Vocabulary sharing dynamics are complex and influenced by several environmental and genetic factors.
• Linguistic diversity has potential applications in enhancing cooperation within artificial intelligence and robotics systems.