Geometric Sequential Learning Dynamics

In this letter, we introduce a novel dynamic model for predicting the exact strategies of the opponents without message exchange, namely geometric sequential learning dynamics (GSLD). The intuition is twofold; first, the utility function is widely modeled by arbitrary exponential varieties; second, the equidistant sampled exponential function comprises a geometric sequence. To validate GSLD, we model the exponential variety game (EVG) and prove its convergence by showing that it is a continuous quasi-concave game. The proposed scheme enables the construction of the exact individual utility function, which results in a faster convergence and a high utility value.