Collaborative Human Decision-Making With Heterogeneous Agents
Collaborative Human Decision-Making With Heterogeneous Agents
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DOI：
10.1109/tcss.2021.3098975
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发表时间：
2021-07-27
影响因子：
5
通讯作者：
Varshney, Pramod K.
中科院分区：
文献类型：
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作者：
Geng, Baocheng;Cheng, Xiancheng;Varshney, Pramod K.
虽然从认知心理学的角度对个人和群体的人类决策建模进行了大量的工作，但从信号处理和信息融合的角度对这一主题的研究相对较新。在这项工作中，我们考虑了一个分布式检测问题，包括一些人类的本地决策者和融合中心（FC）。信号检测理论被用来回答为什么促进异质性可以提高人类协同决策的性能。我们考虑了以下两种情况：1）本地决策者是独立的，异质性的水平是衡量人类专业知识的可变性和2）人类作出相关的本地决策，由于他们的感知和行为的相似性和异质性的相关性。在这两种情况下，我们表明，FC的检测性能可以提高异质性的增加。特别是，在第二种情况下，我们开发了一个投资组合理论为基础的框架，以选择相关的人类代理的参与者，使异质性增强，从而提高决策性能。仿真结果用于说明和性能比较。
While there has been extensive work on modeling of human decision-making both for individuals and groups from a cognitive psychology point of view, research on this topic from a signal processing and information fusion perspective is relatively recent. In this work, we consider a distributed detection problem consisting of a number of human local decision makers and a fusion center (FC). Signal detection theory is exploited to answer why promoting heterogeneity could improve the performance of collaborative human decision-making. We consider the following two scenarios: 1) the local decision makers are independent and the level of heterogeneity is measured in terms of the variability of human expertise and 2) humans make correlated local decisions due to their perceptual and behavioral similarities and heterogeneity is measured by the amount of correlation. In both cases, we show that the detection performance of the FC can be improved with the increase of heterogeneity. In particular, in the second scenario, we develop a portfolio theory-based framework to select participants from correlated human agents so that heterogeneity is enhanced resulting in improved decision-making performance. Simulations are provided for illustration and performance comparison.