歧义条件下按需竞争的二人报摊博弈

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Andrea Cinfrignini , Silvia Lorenzini , Davide Petturiti
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引用次数: 0

摘要

我们处理一个单时期的两方报贩博弈,假设两个报贩都是理性和风险中立的,并且在模糊性下运作。每个报贩需要选择自己订购的同一种易腐产品的数量,其全球市场需求用一个离散随机变量来建模,并赋予参考概率测度。此外,根据比例分配规则将全球市场需求分配给报贩。我们用参考概率度量的单个ϵ-contamination对每个报贩所面临的不确定性进行建模,并根据合适的概率度量类进行计算。所得ϵ-contamination模型保留了参考概率下的期望需求,并用于计算个体较低的期望利润作为Choquet期望。因此,在给定对手选择的情况下,每个参与者的优化问题归结为解决使其最低期望利润最大化的订货量问题,这是一个极大值问题。在最终的博弈中,我们证明了纳什均衡总是存在的,尽管它可能不是唯一的。最后,我们用最佳对策函数给出了纳什均衡的表征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A two-player newsvendor game with competition on demand under ambiguity
We deal with a single period two-player newsvendor game where both newsvendors are assumed to be rational and risk-neutral, and to operate under ambiguity. Each newsvendor needs to choose his/her order quantity of the same perishable product, whose global market demand is modeled by a discrete random variable, endowed with a reference probability measure. Furthermore, the global market demand is distributed to newsvendors according to a proportional allocation rule. We model the uncertainty faced by each newsvendor with an individual ϵ-contamination of the reference probability measure, computed with respect to a suitable class of probability measures. The resulting ϵ-contamination model preserves the expected demand under the reference probability and is used to compute the individual lower expected profit as a Choquet expectation. Therefore, the optimization problem of each player reduces to settle the order quantity that maximizes his/her lower expected profit, given the opponent choice, which is a maximin problem. In the resulting game, we prove that a Nash equilibrium always exists, though it may not be unique. Finally, we provide a characterization of Nash equilibria in terms of best response functions.
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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