Optimal information ordering in sequential detection problems with cognitive biases

In this paper sequential detection problems are treated in the context of cognitive biases. We present a general bias model and we design a generalized sequential probability ratio test (GSPRT) to mitigate the bias impact following a composite hypothesis testing approach. We also derive an optimal ordering of the incoming observations for fast detection defined in terms of the average sample number (ASN) of observations. We verify through numerical analysis that the designed detector fulfills the time and accuracy requirements. Results show that its performance emulates that of a Bayesian detector optimized for fast sequential detection in absence of biases.