情感参与的人类决策模型

IF 1.8 4区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2021-01-02 DOI:10.1080/13873954.2021.1986846
Kaede Iinuma, K. Kogiso
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引用次数: 1

摘要

摘要本研究提出了一个处理情绪诱导行为的计算人类决策模型。所提出的模型可以根据通过混合部分可观察的马尔可夫决策过程的最优策略和通过新的情绪动力学的进化概率分布而获得的概率分布来确定理性或非理性行为。连续负面观察的情绪动力学会导致情绪引发的非理性行为。我们通过两个定理阐明了所提出的模型根据一些模型参数计算有理和无理作用的条件。一个基于日本法庭记录的数值例子证实了所提出的模型模仿了人类的决策过程。此外,我们还讨论了采取预防措施以避免发生谋杀案的可能性。这项研究表明,如果决策者的特征可以建模,那么所提出的模型可以支持人类互动,以避免情绪驱动的谋杀案。
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Emotion-involved human decision-making model
ABSTRACT This study proposes a computational human decision-making model that handles emotion-induced behaviour. The proposed model can determine a rational or irrational action according to a probability distribution obtained by mixing an optimal policy of a partially observable Markov decision process and an evolved probability distribution by novel dynamics of emotions. Emotion dynamics with consecutive negative observations cause emotion-induced irrational behaviours. We clarify the conditions, via two theorems, that the proposed model computes rational and irrational actions in terms of some model parameters. A numerical example based on Japanese court records is used to confirm that the proposed model imitates the human decision-making process. Moreover, we discuss the possibility of preventive measures for avoiding the murder case scenario. This study shows that if the traits of a decision maker can be modelled, the proposed model can support human interactions to avoid an emotion-driven murder case scenario.
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来源期刊
CiteScore
3.80
自引率
5.30%
发文量
7
审稿时长
>12 weeks
期刊介绍: Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.
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