Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing

TL;DR

This paper develops prospect-theory based decision rules for human agents in hypothesis testing, addressing behavioral deviations from traditional Bayesian approaches, with models for optimists and pessimists.

Contribution

It introduces prospect-theory inspired detection rules for behavioral agents, expanding hypothesis testing beyond rational Bayesian models.

Findings

Human decision rules deviate from Bayesian under prospect theory.

Optimal detection rules vary for optimists and pessimists.

Behavioral models influence hypothesis testing outcomes.

Abstract

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.