Sequential Processing of Observations in Human Decision-Making Systems
This addresses decision-making in human-machine systems, but it is incremental as it builds on existing fusion methods with human-specific models.
The paper tackles the problem of binary hypothesis testing with human decision-makers who observe sequentially up to random times, using a belief model to accumulate log-likelihood ratios, and a machine fuses decisions via a weighted Chair-Varshney rule based on observation counts.
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.