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.

中文翻译：

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几何顺序学习动力学


在这封信中，我们介绍了一种新颖的动态模型，无需消息交换即可预测对手的确切策略，即几何顺序学习动力学（GSLD）。直觉是双重的；首先，效用函数广泛地由任意指数变体建模；第二，等距采样指数函数包括几何序列。为了验证 GSLD，我们对指数多样性博弈 (EVG) 进行建模，并通过证明它是连续拟凹博弈来证明其收敛性。所提出的方案能够构造精确的个体效用函数，从而实现更快的收敛和较高的效用价值。