Cognitive biases in Bayesian updating and optimal information sequencing

Abstract

In this paper, we consider the problem of optimally ordering information to a human subject to maximize detection performance in a binary hypothesis testing problem. We begin by proposing a modification of the traditional Bayesian solution to hypothesis testing problems to incorporate the effect of human cognitive biases. Next, we consider the problem of selecting a subset of information to maximize detection performance in truncated hypothesis testing problems. We then use the solution to that problem to determine the real time ordering of information to enhance human binary hypothesis testing. We verify through simulations that the proposed ordering methods with and without cognitive biases minimize the probability of miss and the probability of false alarm.