Sequential Processing of Observations in Human Decision-Making Systems

TL;DR

This paper models human decision-making in binary hypothesis testing as a sequential process with variable observation lengths, incorporating belief models and weighted decision fusion by a machine to improve understanding of human-machine collaborative decisions.

Contribution

It introduces a framework for analyzing human decision-makers with sequential observations and belief models, and proposes a weighted fusion rule for combining human decisions based on observation counts.

Findings

Human decision-makers use belief models to accumulate evidence over time.

The fusion of decisions considers the number of observations each human used.

Weighted decision fusion improves overall decision accuracy.

Abstract

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.