Reinforcement with iterative punishment

We consider the efficacy of various forms of reinforcement learning with punishment in evolving linguistic conventions in the context of LewisSkyrms signaling games. We show that the learning strategy of reinforcement with iterative punishment is highly effective at evolving optimal conventions in even complex signaling games. It is also robust and can be easily extended to a self-tuning variety of reinforcement learning. We briefly discuss some of the virtues of reinforcement with iterative punishment and how it may be related to learning in nature.