Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing
This work addresses the need for detection rules tailored to human cognitive biases in crowd-sensing, though it appears incremental as it applies existing prospect theory to a specific scenario.
The paper tackled the problem of designing binary hypothesis testing rules for behavioral agents in crowd-sensing systems, using prospect theory models for optimists and pessimists, and derived optimal detection rules while showing deviations from Bayesian rules.
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.