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An Outcome Preference Information Aggregation Model and Its Algorithm in Hypergame Situations

机译:超级级别偏好信息聚合模型及其在超级赌场情况下的算法

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摘要

In hypergame situations, it is important for a player to get the more correct opponent players' outcome preference information. In this paper, based on the principle of fuzzy pattern recognition, a nonlinear programming model is established for integrating opponent players' different outcome preference evaluation values perceived by different experts without weight information. An iteration algorithm for solving the model is developed. Using the proposed model and its algorithm, not only the weight of each expert but also the integrated outcome preferences can be obtained easily. A numerical example is provided to illustrate the method.
机译:在超级赌场情况下,一名球员都很重要,以获得更正确的对手球员的结果偏好信息。本文基于模糊模式识别的原理,建立了非线性规划模型,用于整合对不同专家感知的对手玩家的不同结果偏好评估值,没有重量信息。开发了一种求解模型的迭代算法。使用所提出的模型及其算法,不仅可以容易获得每个专家的重量,而且还可以容易地获得集成的结果偏好。提供了一个数值示例以说明该方法。

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