Collaborative Human Decision Making With Heterogeneous Agents

Baocheng Geng, Xiancheng Cheng, Swastik Brahma, David Kellen, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)469-479
Number of pages11
JournalIEEE Transactions on Computational Social Systems
Issue number2
StatePublished - Apr 1 2022


  • Correlated local decisions
  • group decision-making
  • heterogeneity
  • human decision-making
  • information fusion
  • portfolio theory

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Social Sciences (miscellaneous)
  • Modeling and Simulation


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