Experiments and Models for Decision Fusion by Humans in Inference Networks
Experiments and Models for Decision Fusion by Humans in Inference Networks
复制标题
DOI：
10.1109/tsp.2017.2784358
复制
发表时间：
2018-06
影响因子：
5.4
通讯作者：
Aditya Vempaty;L. Varshney;Gregory J. Koop;A. Criss;P. Varshney
中科院分区：
文献类型：
--
作者：
Aditya Vempaty;L. Varshney;Gregory J. Koop;A. Criss;P. Varshney
随着物联网（IoT）的出现以及智能设备和无线传感器网络（WSNs）的快速部署，人类与机器数据进行了广泛的交互。这些人类决策者使用通过社会技术网络提供信息的传感器。传感器可以是其他人类用户，也可以是物联网设备。决策者本身也是网络的一部分，需要了解他们的行为方式。在行为实验的基础上，分析了人类的决策融合行为。从这些实验中收集的数据表明，人们以随机的方式执行决策融合，这取决于各种因素，而不像机器以确定性的方式执行这项任务。贝叶斯层次模型的发展来描述所观察到的随机人类行为。这种分层模型捕捉了在个人、群体和人口水平上观察到的人的差异。这样一个模型的影响，设计大规模的推理系统的开发最佳决策融合树与人类和机器代理。
With the advent of the Internet of Things (IoT) and a rapid deployment of smart devices and wireless sensor networks (WSNs), humans interact extensively with machine data. These human decision makers use sensors that provide information through a sociotechnical network. The sensors can be other human users or they can be IoT devices. The decision makers themselves are also part of the network, and there is a need to understand how they will behave. In this paper, the decision fusion behavior of humans is analyzed on the basis of behavioral experiments. The data collected from these experiments demonstrate that people perform decision fusion in a stochastic manner dependent on various factors, unlike machines that perform this task in a deterministic manner. A Bayesian hierarchical model is developed to characterize the observed stochastic human behavior. This hierarchical model captures the differences observed in people at individual, crowd, and population levels. The implications of such a model on designing large-scale inference systems are presented by developing optimal decision fusion trees with both human and machine agents.