Human Decision Making with Bounded Rationality
Human Decision Making with Bounded Rationality
复制标题
人类有限理性决策
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
P. Varshney
中科院分区：
文献类型：
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作者：
Baocheng Geng;Qunwei Li;P. Varshney
在需要高精度决策的关键环境中，除了机器观察之外，还利用人类的认知优势和专业知识，有利于提高决策质量和增强态势感知。虽然目前关于人类决策的文献主要基于完美理性的范式，但人类会受到决策噪音的影响，并采用随机选择规则。人类在这种现实环境下的决策需要进一步研究。在本文中，而不是假设一个人选择的最佳行动的概率为1，我们采用了有限理性选择模型，所有的行动都是选择的候选人，但更好的选择，以更高的概率。在贝叶斯假设检验的框架下，我们评估个人的决策性能时，人类具有不同程度的有限理性。此外，我们分析了决策融合规则的两个人类代理的团队和人类参与者的数量变得很大的特征的渐近性能的协同决策。
In critical environments that require a high accuracy of decisions, utilizing human cognitive strengths and expertise in addition to machine observations is advantageous to improve decision quality and enhance situational awareness. While the current literature on human decision making is primarily based on the paradigm of perfect rationality, humans are subject to decision noise and employ stochastic choice rules. Human decision making under such realistic environments needs to be further studied. In this paper, instead of assuming that a human selects the optimal action with probability one, we employ a bounded rationality choice model where all the actions are candidates for selection, but better options are chosen with higher probabilities. In a Bayesian hypothesis testing framework, we evaluate the individual decision making performance when humans have different degrees of bounded rationality. Furthermore, we analyze the decision fusion rule for a team of two human agents and characterize the asymptotic performance of collaborative decision making as the number of human participants becomes large.