The "Value at Risk" Principle in Hierarchical Game

Q3 Engineering
M. Gorelov
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引用次数: 0

Abstract

A hierarchical game of two persons with random factors is considered. It is assumed that the top-level player has the right of the first move. It is believed that the lower level player at the time of decision making knows exactly the realization of the random factor and the choice of partner. And the top-level player at the time of decision making knows only a probabilistic measure on the set of values ​​of an uncertain factor. The principle of optimality is new: it is believed that a top-level player is ready to neglect some of the "unpleasant" events, the total probability of which is given, but otherwise he is careful. Under these assumptions, the maximum guaranteed result of the top-level player is calculated. The structure of strategies providing such a result is clarified. Two cases were investigated: a game with and without feedback. To solve the problem, an original definition of the maximum guaranteed result is proposed. It is equivalent to the classical definition, but is simpler. Using this technique, solving of the problem reduces to identical transformations of the formulas for predicate calculus. As a result of the solution, the optimal strategy and the set of "unpleasant" cases which are excluded from consideration search task is reduced to calculating multiple maximins on finite-dimensional spaces. In this case, the operation of calculating the expected value with respect to given probabilistic measure is considered to be "elementary". Models of this type can have different interpretations. One can use them for methodological justification of the principle of maximum guaranteed result. One can use them when solving risk management tasks. One can consider them as models for managing the "customer base" of the service company. The proposed method allows to study such models at a qualitative level, and in some cases to obtain quantitative results.
层次博弈中的“风险价值”原则
考虑了一个具有随机因素的两个人的层次博弈。假设顶级玩家拥有第一步的权利。据信,在决策时,较低级别的玩家确切地知道随机因素的实现和伙伴的选择。决策时的顶级参与者只知道一组值的概率测度​​不确定因素。最优性原理是新的:人们认为顶级球员准备忽略一些“不愉快”的事件,这些事件的总概率是给定的,但在其他方面他很小心。在这些假设下,计算出顶级玩家的最大保证结果。阐明了提供这种结果的战略结构。调查了两个案例:一个有反馈和没有反馈的游戏。为了解决这个问题,提出了最大保证结果的原始定义。它相当于经典定义,但更简单。使用这种技术,问题的解决可以简化为谓词演算公式的相同转换。作为该解的结果,最优策略和从考虑搜索任务中排除的一组“不愉快”情况被简化为在有限维空间上计算多个最大值。在这种情况下,计算给定概率测度的期望值的操作被认为是“基本的”。这种类型的模型可能有不同的解释。人们可以用它们来对最大保证结果原则进行方法上的论证。在解决风险管理任务时可以使用它们。人们可以将其视为管理服务公司“客户群”的模型。所提出的方法允许在定性水平上研究此类模型,并在某些情况下获得定量结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Systems Science and Applications
Advances in Systems Science and Applications Engineering-Engineering (all)
CiteScore
1.20
自引率
0.00%
发文量
0
期刊介绍: Advances in Systems Science and Applications (ASSA) is an international peer-reviewed open-source online academic journal. Its scope covers all major aspects of systems (and processes) analysis, modeling, simulation, and control, ranging from theoretical and methodological developments to a large variety of application areas. Survey articles and innovative results are also welcome. ASSA is aimed at the audience of scientists, engineers and researchers working in the framework of these problems. ASSA should be a platform on which researchers will be able to communicate and discuss both their specialized issues and interdisciplinary problems of systems analysis and its applications in science and industry, including data science, artificial intelligence, material science, manufacturing, transportation, power and energy, ecology, corporate management, public governance, finance, and many others.
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