  In this work, we consider a binary hypothesis testing problem involving a
group of human decision-makers. Due to the nature of human behavior, each human
decision-maker observes the phenomenon of interest sequentially up to a random
length of time. The humans use a belief model to accumulate the log-likelihood
ratios until they cease observing the phenomenon. The belief model is used to
characterize the perception of the human decision-maker towards observations at
different instants of time, i.e., some decision-makers may assign greater
importance to observations that were observed earlier, rather than later and
vice-versa. The global decision-maker is a machine that fuses human decisions
using the Chair-Varshney rule with different weights for the human decisions,
where the weights are determined by the number of observations that were used
by the humans to arrive at their respective decisions.
