Towards the Design of Prospect-Theory based Human Decision Rules for
  Hypothesis Testing
  Detection rules have traditionally been designed for rational agents that
minimize the Bayes risk (average decision cost). With the advent of
crowd-sensing systems, there is a need to redesign binary hypothesis testing
rules for behavioral agents, whose cognitive behavior is not captured by
traditional utility functions such as Bayes risk. In this paper, we adopt
prospect theory based models for decision makers. We consider special agent
models namely optimists and pessimists in this paper, and derive optimal
detection rules under different scenarios. Using an illustrative example, we
also show how the decision rule of a human agent deviates from the Bayesian
decision rule under various behavioral models, considered in this paper.
