Optimal information ordering for sequential detection with cognitive biases
The manner and order in which data is presented to a human observer can lead the human to make dramatically different decisions. This raises a question on how to best present data to an observer to achieve the best decision-making performance and minimum adverse effects. In this paper, we present a general framework to model cognitive biases that interfere in the human decision making process. We examine the problem of ordering observations in binary sequential detection. Our treatment considers the limited cognitive effort exerted by a decision-maker and the effect of the observations along with their distributions on the stopping time and accuracy of the sequential test. The complexity of the ordering algorithm is linear in the size of the observation set. Both the average time to make a decision and the probability of decision error are minimized.