Reinforcement Learning Model for Human Behavior of Signaling Games

Abstract

This paper examines the applicability of the reinforcement learning schema for modelling player's decision-making process within a signaling game context where one player has information the other player does not have. This situation of asymmetric information is very common in the realworld. Though many applications of signaling games have been developed to solve economic problems, the previously proposed models could not reproduce the human way of signaling. We show some interesting empirical results concerning the refinement of equilibria by the proposed reinforcement learning model.