Sequential Processing of Observations in Human Decision-Making Systems

In this work, we consider a binary hypothesis testing problem involving human decision-makers. Due to the nature of human behavior, human decision-makers observe 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. We further consider the performance of a group of humans using a global decision-maker that fuses human decisions using the Chair-Varshney rule. When the number of observations that were used by the humans to arrive at their respective decisions are available to the fusion center (FC), the weights in the Chair-Varshney rule are modified to include this information in the decision fusion rule. Numerical and simulation results are presented to corroborate and validate theoretical results.