  In mathematical psychology, recent models for human decision-making use
Quantum Decision Theory to capture important human-centric features such as
order effects and violation of the sure-thing principle (total probability
law). We construct and analyze a human-sensor system where a quickest detector
aims to detect a change in an underlying state by observing human decisions
that are influenced by the state.
  Apart from providing an analytical framework for such human-sensor systems,
we also analyze the structure of the quickest detection policy. We show that
the quickest detection policy has a single threshold and the optimal cost
incurred is lower bounded by that of the classical quickest detector. This
indicates that intermediate human decisions strictly hinder detection
performance. We also analyze the sensitivity of the quickest detection cost
with respect to the quantum decision parameters of the human decision maker,
revealing that the performance is robust to inaccurate knowledge of the
decision-making process. Numerical results are provided which suggest that
observing the decisions of more rational decision makers will improve the
quickest detection performance. Finally, we illustrate a numerical
implementation of this quickest detector in the context of the Prisoner's
Dilemma problem, in which it has been observed that Quantum Decision Theory can
uniquely model empirically tested violations of the sure-thing principle.
