Experiments and Models for Decision Fusion by Humans in Inference Networks

Aditya Vempaty, Lav R. Varshney, Gregory J. Koop, Amy H. Criss, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2960-2971
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume66
Issue number11
DOIs
StatePublished - Jun 1 2018

Keywords

  • Bayesian hierarchical modeling
  • Human behavior modeling
  • decision fusion
  • sociotechnical networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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