赵森烽-克勤概率的赌本分配研究与期望值定理

针对概率论发展史上合理分配赌本问题，把赵森烽-克勤概率用于合理分配赌本需要的最少赌博次数研究，结果发现，该问题中基于经典概率得出的数学期望不会在实际中出现，实际中出现的是基于赵森烽-克勤概率的"数学期望"的两个极端值。利用赵森烽-克勤概率能客观地反映出给定规则下最少赌博次数与最多赌博次数时的赌博结果，同时刻画出赌博输赢的经典期望值和实际值，从而为有针对性地制定或修改赌博策略和合理地分配赌本提供依据，在此基础上给出期望值不确定定理。文中以机器人服务收费为例说明该定理的现实意义。

Distribution of gambling capital and expectation value theorem for Zhao Senfeng-Keqin probability

With respect to the reasonable distribution of gambling capital in the developmental history of probability theory, Zhao Senfeng-Keqin probability has been used to investigate the minimum number of gambling times necessary for the rational allocation of the minimum amount of gambling capital. Results have shown that the mathematical expectation for this problem, based on classical probability, failed to occur in practice. What appeared instead are two extreme values of "mathematical expectation" based on the Zhao Senfeng-Keqin probability, which can objectively reflect the gambling results within the smallest and largest number of gambling times for a given rule. In addition, it describes both the classic expectation value and the actual value, thereby providing a basis for formulating or amending specific gambling tactics and the reasonable allocation of gambling capital. The result is an uncertainty theorem for the expectation value. In this paper, we illustrate the practical significance of this theorem by giving an example of service charging on a robot.